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Qasim M, Csaplovics E. AGB estimation using Sentinel-2 and Sentinel-1 datasets. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:299. [PMID: 38396046 DOI: 10.1007/s10661-024-12478-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
Climate change is one of the greatest threats recently, of which developing countries are facing most of the brunt. In the fight against climate change, forests can play an important role, since they hold a substantial amount of terrestrial carbon and can therefore affect the global carbon cycle. Deforestation, however, is a significant challenge. There are financial incentives that can help in halting deforestation by compensating developing countries for their efforts. They require however assessments which makes it essential for developing countries to regularly monitor their stocking. Based on the aforementioned, forest carbon stock assessment was conducted in Margalla Hills National Park i.e., Sub-tropical Chir Pine Forest (SCPF) and Sub-tropical Broadleaved Evergreen Forest (SBEF), in Pakistan combining field inventory with a remote-sensing-based approach using machine learning algorithms. Circular plots of a 20 m radius were used for recording the data and Sentinel-2 (S2) and Sentinel-1 (S1) satellite data were used for estimating the Aboveground Biomass (AGB). The performances of Random Forests (RF) and Support Vector Machine (SVM) were explored. The AGB was higher for the SCPF. The RF performed better for SCPF, but SVM was better for SBEF. The free available satellite data in the form of S2 and S1 data offers an advantage for AGB estimations. The combination of S2 and S1 for future AGB studies in Pakistan is also recommended.
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Affiliation(s)
- Mohammad Qasim
- Chair of Remote Sensing, Faculty of Environmental Sciences, Technische Universität Dresden, Helmholtz Straße 10, 01069, Dresden, Germany.
| | - Elmar Csaplovics
- Chair of Remote Sensing, Faculty of Environmental Sciences, Technische Universität Dresden, Helmholtz Straße 10, 01069, Dresden, Germany
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2
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Araza A, de Bruin S, Hein L, Herold M. Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting. Sci Rep 2023; 13:12704. [PMID: 37543683 PMCID: PMC10404296 DOI: 10.1038/s41598-023-38935-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 07/17/2023] [Indexed: 08/07/2023] Open
Abstract
Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010-2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6-47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9-39 Mg C [Formula: see text] and [Formula: see text]=0.16-0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting.
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Affiliation(s)
- Arnan Araza
- Laboratory of Geo-information and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands.
- Environmental Systems Analysis, Wageningen University and Research, Wageningen, The Netherlands.
| | - Sytze de Bruin
- Laboratory of Geo-information and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
| | - Lars Hein
- Environmental Systems Analysis, Wageningen University and Research, Wageningen, The Netherlands
| | - Martin Herold
- Laboratory of Geo-information and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
- Remote Sensing and Geoinformatics Section, Helmholtz GFZ German Research Centre for Geosciences, Telegrafenberg Potsdam, Germany
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3
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Ramachandran N, Saatchi S, Tebaldini S, d'Alessandro MM, Dikshit O. Mapping tropical forest aboveground biomass using airborne SAR tomography. Sci Rep 2023; 13:6233. [PMID: 37069184 PMCID: PMC10110524 DOI: 10.1038/s41598-023-33311-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/10/2023] [Indexed: 04/19/2023] Open
Abstract
Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency's BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200-500 Mg ha-1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.
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Affiliation(s)
- Naveen Ramachandran
- Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India.
| | - Sassan Saatchi
- Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA, 91125, USA
| | - Stefano Tebaldini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, 20133, Italy
| | | | - Onkar Dikshit
- Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India
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Dewa DD, Buchori I. Impacts of rapid urbanization on spatial dynamics of land use-based carbon emission and surface temperature changes in the Semarang Metropolitan Region, Indonesia. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:259. [PMID: 36595039 DOI: 10.1007/s10661-022-10839-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 12/10/2022] [Indexed: 06/17/2023]
Abstract
This study explores the relationship between carbon emission patterns and the land surface temperature (LST) changes due to the rapid urbanization in the Semarang Metropolitan Region (SMR), an Indonesian area that has experienced rapid urban growth compared to other urban areas. This research used the stock-difference and gain-loss methods to calculate carbon stocks and emissions. Then, band 6 on Landsat 5 TM (2008) and band 10 on Landsat 8 OLI (2013 and 2018) were used to calculate the LST changes. These results showed that the peri-urban area had a more significant change. The correlation between carbon emissions and an increased SMR temperature correlates to 0.646. This shows that the carbon emissions pattern promotes temperature dynamics in the SMR. Furthermore, this study proved the release of carbon emissions in line with LST dynamics spatially. In this case, this study proved that rapid urbanization in the SMR promotes both carbon emission and LST. Those changes are also affected by vegetation canopy availability and other activities. As a result, the government must prioritize spatial planning in the SMR to mitigate environmental change risk. In addition, the government must develop novel strategies to deal with a wide range of fast and unpredictable potential changes in the urban area and its surroundings.
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Affiliation(s)
- Dimas Danar Dewa
- Doctoral Program in Architecture and Urbanism, Universitas Diponegoro, Semarang, Indonesia.
| | - Imam Buchori
- Department of Urban and Regional Planning, Faculty of Engineering, Universitas Diponegoro, Semarang, Indonesia
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Thapa K, Thapa GJ, Manandhar U, Dhakal M, Jnawali SR, Maraseni TN. Carbonated tiger-high above-ground biomass carbon stock in protected areas and corridors and its observed negative relationship with tiger population density and occupancy in the Terai Arc Landscape, Nepal. PLoS One 2023; 18:e0280824. [PMID: 36696434 PMCID: PMC9876270 DOI: 10.1371/journal.pone.0280824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 12/30/2022] [Indexed: 01/26/2023] Open
Abstract
Healthy natural forests maintain and/or enhances carbon stock while also providing potential habitat and an array of services to wildlife including large carnivores such as the tiger. This study is the first of its kind in assessing relationships between above-ground biomass carbon stock, tiger density and occupancy probability and its status in protected areas, corridors, and forest connectivity blocks. The dataset used to assess the relationship were: (1) Converged posterior tiger density estimates from camera trap data derived from Bayesian- Spatially Explicit Capture-Recapture model from Chitwan National Park; (2) Site wise probability of tiger occupancy estimated across the Terai Arc Landscape and (3) Habitat wise above-ground biomass carbon stock estimated across the Terai Arc Landscape. Carbon stock maps were derived based on eight habitat classes and conservation units linking satellite (Landsat 7 ETM+) images and field collected sampling data. A significant negative relationship (r = -0.20, p<0.01) was observed between above-ground biomass carbon stock and tiger density in Chitwan National Park and with tiger occupancy (r = -0.24, p = 0.023) in the landscape. Within protected areas, we found highest mean above-ground biomass carbon stock in high density mixed forest (~223 tC/ha) and low in degraded scrubland (~73.2 tC/ha). Similarly, we found: (1) highest tiger density ~ 0.06 individuals per 0.33 km2 in the riverine forest and lowest estimates (~0.00) in degraded scrubland; and (2) predictive tiger density of 0.0135 individuals per 0.33 km2 is equivalent to mean total of 43.7 tC/ha in Chitwan National Park. Comparatively, we found similar above-ground biomass carbon stock among corridors, large forest connectivity blocks (~117 tC/ha), and within in tiger bearing protected areas (~119 tC/ha). Carbon conservation through forest restoration particularly in riverine habitats (forest and grassland) and low transitional state forests (degraded scrubland) provides immense opportunities to generate win-win solutions, sequester more carbon and maintain habitat integrity for tigers and other large predators.
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Affiliation(s)
| | - Gokarna Jung Thapa
- WWF Nepal, Baluwatar, Kathmandu, Nepal.,Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Maheshwar Dhakal
- Department of National Parks and Wildlife Conservation, Babarmahal, Kathmandu, Nepal
| | | | - Tek Narayan Maraseni
- University of Southern Queensland, Institute for Life Sciences and the Environment, Toowoomba, Queensland, Australia
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A Novel Composite Index to Measure Environmental Benefits in Urban Land Use Optimization Problems. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11040220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In urban land use optimization problems, different conflicting objectives are applied. One of the most significant goals in urban land use optimization problems is to maximize environmental benefits. To quantify environmental benefits in land use optimization, many researchers have employed a variety of methodologies. According to previous studies, there is no standard approach for calculating environmental benefits in urban land use allocation problems. Against this background, this study aims to (a) identify indicators of environmental benefits and (b) propose a novel composite index to measure environmental benefits in urban land use optimization problems. This study identified four indicators as a measure of environmental benefits based on a literature assessment and expert opinion. These are spatial compactness, land surface temperature, carbon storage, and ecosystem service value. In this work, we proposed a novel composite environmental benefits index (EBI) to quantify environmental benefits in urban land use allocation problems using an ordered weighted averaging (OWA) method. The study results showed that land surface temperature (LST) is the most influential indicator of environmental benefit while carbon storage is the least important factor. Finally, the proposed method was applied in Rajshahi city in Bangladesh. This study identified that, in an average-risk decision, most of the land (64.55%) of the study area falls within the low-environmental-benefit zone due to a lack of vegetated land cover. The result suggests the potential of using EBI in the land use allocation problem to ensure environmental benefits.
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Dullah H, Malek MA, Omar H, Mangi SA, Hanafiah MM. Assessing changes of carbon stock in dipterocarp forest due to hydro-electric dam construction in Malaysia. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:44264-44276. [PMID: 33847888 DOI: 10.1007/s11356-021-13833-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/05/2021] [Indexed: 06/12/2023]
Abstract
Deforestation and forest degradation are among the leading global concerns, as they could reduce the carbon sink and sequestration potential of the forest. The impoundment of Kenyir River, Hulu Terengganu, Malaysia, in 1985 due to the development of hydropower station has created a large area of water bodies following clearance of forested land. This study assessed the loss of forest carbon due to these activities within the period of 37 years, between 1972 and 2019. The study area consisted of Kenyir Lake catchment area, which consisted mainly of forests and the great Kenyir Lake. Remote sensing datasets have been used in this analysis. Satellite images from Landsat 1-5 MSS and Landsat 8 OLI/TRIS that were acquired between the years 1972 and 2019 were used to classify land uses in the entire landscape of Kenyir Lake catchment. Support vector machine (SVM) was adapted to generate the land-use classification map in the study area. The results show that the total study area includes 278,179 ha and forest covers dominated the area for before and after the impoundment of Kenyir Lake. The assessed loss of carbon between the years 1972 and 2019 was around 8.6 million Mg C with an annual rate of 0.36%. The main single cause attributing to the forest loss was due to clearing of forest for hydro-electric dam construction. However, the remaining forests surrounding the study area are still able to sequester carbon at a considerable rate and thus balance the carbon dynamics within the landscapes. The results highlight that carbon sequestration scenario in Kenyir Lake catchment area shows the potential of the carbon sink in the study area are acceptable with only 17% reduction of sequestration ability. The landscape of the study area is considered as highly vegetated area despite changes due to dam construction.
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Affiliation(s)
- Hayana Dullah
- Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia.
| | - Marlinda Abdul Malek
- Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia
| | - Hamdan Omar
- Forest Research Institute Malaysia (FRIM), Kepong, Selangor, Malaysia
| | - Sajjad Ali Mangi
- Department of Civil Engineering, Mehran University of Engineering and Technology, SZAB Campus Khairpur Mirs, Sindh, Pakistan
| | - Marlia Mohd Hanafiah
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia
- Centre for Tropical Climate Change System, Institute of Climate Change, Universiti Kebangsaaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia
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8
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New Biomass Estimates for Chaparral-Dominated Southern California Landscapes. REMOTE SENSING 2021. [DOI: 10.3390/rs13081581] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Chaparral shrublands are the dominant wildland vegetation type in Southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrublands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict aboveground live biomass in Southern California using remote sensing data (Landsat Normalized Difference Vegetation Index (NDVI)) and a suite of geophysical variables. By substituting the NDVI and precipitation predictors for any given year, we were able to apply the model to each year from 2000 to 2019. Using a total of 980 field plots, our model had a k-fold cross-validation R2 of 0.51 and an RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE = 9.7) for Sierran mixed-conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers, we calculated the mean year 2010 shrubland biomasses for the four national forests that ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomasses from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measurements in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire.
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9
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Fernandez-Bou AS, Dierick D, Allen MF, Harmon TC. Precipitation-drainage cycles lead to hot moments in soil carbon dioxide dynamics in a Neotropical wet forest. GLOBAL CHANGE BIOLOGY 2020; 26:5303-5319. [PMID: 32458420 DOI: 10.1111/gcb.15194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 05/07/2020] [Indexed: 06/11/2023]
Abstract
Soil CO2 concentrations and emissions from tropical forests are modulated seasonally by precipitation. However, subseasonal responses to meteorological events (e.g., storms, drought) are less well known. Here, we present the effects of meteorological variability on short-term (hours to months) dynamics of soil CO2 concentrations and emissions in a Neotropical wet forest. We continuously monitored soil temperature, moisture, and CO2 for a three-year period (2015-2017), encompassing normal conditions, floods, a dry El Niño period, and a hurricane. We used a coupled model (Hydrus-1D) for soil water propagation, heat transfer, and diffusive gas transport to explain observed soil moisture, soil temperature, and soil CO2 concentration responses to meteorology, and we estimated soil CO2 efflux with a gradient-flux model. Then, we predicted changes in soil CO2 concentrations and emissions under different warming climate change scenarios. Observed short-term (hourly to daily) soil CO2 concentration responded more to precipitation than to other meteorological variables (including lower pressure during the hurricane). Observed soil CO2 failed to exhibit diel patterns (associated with diel temperature fluctuations in drier climates), except during the drier El Niño period. Climate change scenarios showed enhanced soil CO2 due to warmer conditions, while precipitation played a critical role in moderating the balance between concentrations and emissions. The scenario with increased precipitation (based on a regional model projection) led to increases of +11% in soil CO2 concentrations and +4% in soil CO2 emissions. The scenario with decreased precipitation (based on global circulation model projections) resulted in increases of +4% in soil CO2 concentrations and +18% in soil CO2 emissions, and presented more prominent hot moments in soil CO2 outgassing. These findings suggest that soil CO2 will increase under warmer climate in tropical wet forests, and precipitation patterns will define the intensity of CO2 outgassing hot moments.
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Affiliation(s)
- Angel Santiago Fernandez-Bou
- Environmental Systems Graduate Program , Department of Civil & Environmental Engineering, University of California Merced, Merced, CA, USA
| | - Diego Dierick
- La Selva Biological Station, Organization for Tropical Studies, Costa Rica
- Department of Biological Sciences, Florida International University, Miami, FL, USA
| | - Michael F Allen
- Department of Microbiology & Plant Pathology and Center for Conservation Biology, University of California Riverside, Riverside, CA, USA
| | - Thomas C Harmon
- Environmental Systems Graduate Program , Department of Civil & Environmental Engineering, University of California Merced, Merced, CA, USA
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10
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Estimation of Future Changes in Aboveground Forest Carbon Stock in Romania. A Prediction Based on Forest-Cover Pattern Scenario. FORESTS 2020. [DOI: 10.3390/f11090914] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The aboveground forest biomass plays a key role in the global carbon cycle and is considered a large and constant carbon reservoir. Hence, exploring the future potential changes in forest-cover pattern can help to estimate the trend of forest biomass and therefore, carbon stock in a certain area. As a result, the present paper attempts to model the potential changes in aboveground forest carbon stock based on the forest-cover pattern scenario simulated for 2050. Specifically, the resulting aboveground forest biomass, estimated for 2015 using the allometric equation based on diameter at breast height and the estimated forest density, was used as baseline data in the present approach. These spatial data were integrated into the forest-cover pattern scenario, predicted by using a spatially explicit model, i.e., the Conversion of Land Use and its Effects at Small regional extent (CLUE-S), in order to estimate the potential variation of aboveground forest carbon stock. Our results suggest an overall increase by approximately 4% in the aboveground forest carbon stock until 2050 in Romania. However, important differences in the forest-cover pattern change were predicted on the regional scale, thus highlighting that the rates of carbon accumulation will change significantly in large areas. This study may increase the knowledge of aboveground forest biomass and the future trend of carbon stock in the European countries. Furthermore, due to their predictive character, the results may provide a background for further studies, in order to investigate the potential ecological, socio-economic and forest management responses to the changes in the aboveground forest carbon stock. However, in view of the uncertainties associated with the data accuracy and methodology used, it is presumed that the results include several spatial errors related to the estimation of aboveground forest biomass and simulation of future forest-cover pattern change and therefore, represent an uncertainty for the practical management of applications and decisions.
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Akhtar AM, Qazi WA, Ahmad SR, Gilani H, Mahmood SA, Rasool A. Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:584. [PMID: 32808098 DOI: 10.1007/s10661-020-08546-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 08/11/2020] [Indexed: 06/11/2023]
Abstract
In this study, we investigate stand-alone and combined Pleiades high-resolution passive optical and ALOS PALSAR active Synthetic Aperture Radar (SAR) satellite imagery for aboveground biomass (AGB) estimation in subtropical mountainous Chir Pine (Pinus roxburghii) forest in Murree Forest Division, Punjab, Pakistan. Spectral vegetation indices (NDVI, SAVI, etc.) and sigma nought HV-polarization backscatter dB values are derived from processing optical and SAR datasets, respectively, and modeled against field-measured AGB values through various regression models (linear, nonlinear, multi-linear). For combination of multiple spectral indices, NDVI, TNDVI, and MSAVI2 performed the best with model R2/RMSE values of 0.86/47.3 tons/ha. AGB modeling with SAR sigma nought dB values gives low model R2 value of 0.39. The multi-linear combination of SAR sigma nought dB values with spectral indices exhibits more variability as compared with the combined spectral indices model. The Leave-One-Out-Cross-Validation (LOOCV) results follow closely the behavior of the model statistics. SAR data reaches AGB saturation at around 120-140 tons/ha, with the region of high sensitivity around 50-130 tons/ha; the SAR-derived AGB results show clear underestimation at higher AGB values. The models involving only spectral indices underestimate AGB at low values (< 60 tons/ha). This study presents biomass estimation maps of the Chir Pine forest in the study area and also the suitability of optical and SAR satellite imagery for estimating various biomass ranges. The results of this work can be utilized towards environmental monitoring and policy-level applications, including forest ecosystem management, environmental impact assessment, and performance-based REDD+ payment distribution.
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Affiliation(s)
- Aqeela M Akhtar
- College of Earth & Environmental Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, Punjab, 54590, Pakistan
- Development Working Plan Circle, Punjab Forest Department, 108 Ravi Road, Lahore, Punjab, Pakistan
| | - Waqas A Qazi
- Geospatial Research & Education Lab (GREL), Department of Space Science, Institute of Space Technology, Islamabad, 44000, Pakistan
| | - Sajid Rashid Ahmad
- College of Earth & Environmental Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, Punjab, 54590, Pakistan
| | - Hammad Gilani
- Geospatial Research & Education Lab (GREL), Department of Space Science, Institute of Space Technology, Islamabad, 44000, Pakistan.
| | - Syed Amer Mahmood
- Department of Space Science, University of the Punjab, Quaid-e-Azam Campus, Lahore, Punjab, 54590, Pakistan
| | - Ansir Rasool
- Green Pakistan Program, Punjab Forest Department, Lahore, Punjab, Pakistan
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12
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Spawn SA, Sullivan CC, Lark TJ, Gibbs HK. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci Data 2020; 7:112. [PMID: 32249772 PMCID: PMC7136222 DOI: 10.1038/s41597-020-0444-4] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 02/14/2020] [Indexed: 11/09/2022] Open
Abstract
Remotely sensed biomass carbon density maps are widely used for myriad scientific and policy applications, but all remain limited in scope. They often only represent a single vegetation type and rarely account for carbon stocks in belowground biomass. To date, no global product integrates these disparate estimates into an all-encompassing map at a scale appropriate for many modelling or decision-making applications. We developed an approach for harmonizing vegetation-specific maps of both above and belowground biomass into a single, comprehensive representation of each. We overlaid input maps and allocated their estimates in proportion to the relative spatial extent of each vegetation type using ancillary maps of percent tree cover and landcover, and a rule-based decision schema. The resulting maps consistently and seamlessly report biomass carbon density estimates across a wide range of vegetation types in 2010 with quantified uncertainty. They do so for the globe at an unprecedented 300-meter spatial resolution and can be used to more holistically account for diverse vegetation carbon stocks in global analyses and greenhouse gas inventories.
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Affiliation(s)
- Seth A Spawn
- Department of Geography, University of Wisconsin-Madison, Madison, WI, USA.
- Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI, USA.
| | - Clare C Sullivan
- Department of Geography, University of Wisconsin-Madison, Madison, WI, USA
- Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI, USA
| | - Tyler J Lark
- Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI, USA
| | - Holly K Gibbs
- Department of Geography, University of Wisconsin-Madison, Madison, WI, USA
- Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI, USA
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13
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Carbon Stock Estimations in a Mediterranean Riparian Forest: A Case Study Combining Field Data and UAV Imagery. FORESTS 2020. [DOI: 10.3390/f11040376] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This study aims to estimate the total biomass aboveground and soil carbon stocks in a Mediterranean riparian forest and identify the contribution of the different species and ecosystem compartments to the overall riparian carbon reservoir. We used a combined field and object-based image analysis (OBIA) approach, based on unmanned aerial vehicle (UAV) multispectral imagery, to assess C stock of three dominant riparian species. A linear discriminator was designed, based on a set of spectral variables previously selected in an optimal way, permitting the classification of the species corresponding to every object in the study area. This made it possible to estimate the area occupied by each species and its contribution to the tree aboveground biomass (AGB). Three uncertainty levels were considered, related to the trade-off between the number of unclassified and misclassified objects, leading to an error control associated with the estimated tree AGB. We found that riparian woodlands dominated by Acacia dealbata Link showed the highest average carbon stock per unit area (251 ± 90 tC ha−1) followed by Alnus glutinosa (L.) Gaertner (162 ± 12 tC ha−1) and by Salix salviifolia Brot. (73 ± 17 tC ha−1), which are mainly related to the stem density, vegetation development and successional stage of the different stands. The woody tree compartment showed the highest inputs (79%), followed by the understory vegetation (12%) and lastly by the soil mineral layer (9%). Spectral vegetation indices developed to suppress saturation effects were consistently selected as important variables for species classification. The total tree AGB in the study area varies from 734 to 1053 tC according to the distinct levels of uncertainty. This study provided the foundations for the assessment of the riparian carbon sequestration and the economic value of the carbon stocks provided by similar Mediterranean riparian forests, a highly relevant ecosystem service for the regulation of climate change effects.
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14
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Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data. FORESTS 2020. [DOI: 10.3390/f11020163] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.
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Abstract
Forest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.
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16
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Maxwell SL, Evans T, Watson JEM, Morel A, Grantham H, Duncan A, Harris N, Potapov P, Runting RK, Venter O, Wang S, Malhi Y. Degradation and forgone removals increase the carbon impact of intact forest loss by 626. SCIENCE ADVANCES 2019; 5:eaax2546. [PMID: 31692892 PMCID: PMC6821461 DOI: 10.1126/sciadv.aax2546] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 09/16/2019] [Indexed: 05/05/2023]
Abstract
Intact tropical forests, free from substantial anthropogenic influence, store and sequester large amounts of atmospheric carbon but are currently neglected in international climate policy. We show that between 2000 and 2013, direct clearance of intact tropical forest areas accounted for 3.2% of gross carbon emissions from all deforestation across the pantropics. However, full carbon accounting requires the consideration of forgone carbon sequestration, selective logging, edge effects, and defaunation. When these factors were considered, the net carbon impact resulting from intact tropical forest loss between 2000 and 2013 increased by a factor of 6 (626%), from 0.34 (0.37 to 0.21) to 2.12 (2.85 to 1.00) petagrams of carbon (equivalent to approximately 2 years of global land use change emissions). The climate mitigation value of conserving the 549 million ha of tropical forest that remains intact is therefore significant but will soon dwindle if their rate of loss continues to accelerate.
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Affiliation(s)
- Sean L. Maxwell
- Centre for Biodiversity and Conservation Science, School of Earth and Environmental Sciences, University of Queensland, St. Lucia, QLD 4072, Australia
- Wildlife Conservation Society, Global Conservation Program, Bronx, NY 10460, USA
- Corresponding author.
| | - Tom Evans
- Wildlife Conservation Society, Global Conservation Program, Bronx, NY 10460, USA
| | - James E. M. Watson
- Centre for Biodiversity and Conservation Science, School of Earth and Environmental Sciences, University of Queensland, St. Lucia, QLD 4072, Australia
- Wildlife Conservation Society, Global Conservation Program, Bronx, NY 10460, USA
| | - Alexandra Morel
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
- Zoological Society of London, Regent Park, London NW1 4RY, UK
| | - Hedley Grantham
- Wildlife Conservation Society, Global Conservation Program, Bronx, NY 10460, USA
| | - Adam Duncan
- Wildlife Conservation Society, Global Conservation Program, Bronx, NY 10460, USA
| | - Nancy Harris
- World Resources Institute, 10 G Street NE Suite 800, Washington, DC 20002, USA
| | - Peter Potapov
- Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
| | - Rebecca K. Runting
- School of Geography, University of Melbourne, Parkville, VIC 3010, Australia
| | - Oscar Venter
- Ecosystem Science and Management, University of Northern British Columbia, Prince George, Canada
| | - Stephanie Wang
- Wildlife Conservation Society, Global Conservation Program, Bronx, NY 10460, USA
| | - Yadvinder Malhi
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
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Tejada G, Görgens EB, Espírito-Santo FDB, Cantinho RZ, Ometto JP. Evaluating spatial coverage of data on the aboveground biomass in undisturbed forests in the Brazilian Amazon. CARBON BALANCE AND MANAGEMENT 2019; 14:11. [PMID: 31482475 PMCID: PMC7226941 DOI: 10.1186/s13021-019-0126-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Accepted: 08/20/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Brazilian Amazon forests contain a large stock of carbon that could be released into the atmosphere as a result of land use and cover change. To quantify the carbon stocks, Brazil has forest inventory plots from different sources, but they are unstandardized and not always available to the scientific community. Considering the Brazilian Amazon extension, the use of remote sensing, combined with forest inventory plots, is one of the best options to estimate forest aboveground biomass (AGB). Nevertheless, the combination of limited forest inventory data and different remote sensing products has resulted in significant differences in the spatial distribution of AGB estimates. This study evaluates the spatial coverage of AGB data (forest inventory plots, AGB maps and remote sensing products) in undisturbed forests in the Brazilian Amazon. Additionally, we analyze the interconnection between these data and AGB stakeholders producing the information. Specifically, we provide the first benchmark of the existing field plots in terms of their size, frequency, and spatial distribution. RESULTS We synthesized the coverage of forest inventory plots, AGB maps and airborne light detection and ranging (LiDAR) transects of the Brazilian Amazon. Although several extensive forest inventories have been implemented, these AGB data cover a small fraction of this region (e.g., central Amazon remains largely uncovered). Although the use of new technology such as airborne LiDAR cover a significant extension of AGB surveys, these data and forest plots represent only 1% of the entire forest area of the Brazilian Amazon. CONCLUSIONS Considering that several institutions involved in forest inventories of the Brazilian Amazon have different goals, protocols, and time frames for forest surveys, forest inventory data of the Brazilian Amazon remain unstandardized. Research funding agencies have a very important role in establishing a clear sharing policy to make data free and open as well as in harmonizing the collection procedure. Nevertheless, the use of old and new forest inventory plots combined with airborne LiDAR data and satellite images will likely reduce the uncertainty of the AGB distribution of the Brazilian Amazon.
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Affiliation(s)
- Graciela Tejada
- Earth System Science Center (CCST), National Institute for Space Research (INPE), Av dos Astronautas 1758, São José dos Campos, SP 12227-010 Brazil
| | - Eric Bastos Görgens
- Department of Forestry Engineering, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Campus JK, Rod. MGT 367, km 583 5000, Alto do Jacuba, Diamantina, MG 39100-000 Brazil
| | - Fernando Del Bon Espírito-Santo
- Centre for Landscape and Climate Research (CLCR) and Leicester Institute for Space and Earth Observation (LISEO), School of Geography, Geology and Environment, University of Leicester, University Road, Leicester, LE1 7RH UK
| | - Roberta Zecchini Cantinho
- United Nations Development Programme (UNDP), SEN 802, 17, Conj. C-St. Mans̃oes DB, Brasília, DF 70800-400 Brazil
| | - Jean Pierre Ometto
- Earth System Science Center (CCST), National Institute for Space Research (INPE), Av dos Astronautas 1758, São José dos Campos, SP 12227-010 Brazil
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18
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Fusco EJ, Rau BM, Falkowski M, Filippelli S, Bradley BA. Accounting for aboveground carbon storage in shrubland and woodland ecosystems in the Great Basin. Ecosphere 2019. [DOI: 10.1002/ecs2.2821] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Emily J. Fusco
- Graduate Program in Organismic and Evolutionary Biology University of Massachusetts‐Amherst Amherst Massachusetts 01003 USA
| | - Benjamin M. Rau
- USGS New England Water Science Center Northborough Massachusetts 01532 USA
| | - Michael Falkowski
- Department of Ecosystem Science and Sustainability Colorado State University Fort Collins Colorado 80523 USA
| | - Steven Filippelli
- Department of Ecosystem Science and Sustainability Colorado State University Fort Collins Colorado 80523 USA
| | - Bethany A. Bradley
- Graduate Program in Organismic and Evolutionary Biology University of Massachusetts‐Amherst Amherst Massachusetts 01003 USA
- Department of Environmental Conservation University of Massachusetts‐Amherst Amherst Massachusetts 01003 USA
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19
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An End to End Process Development for UAV-SfM Based Forest Monitoring: Individual Tree Detection, Species Classification and Carbon Dynamics Simulation. FORESTS 2019. [DOI: 10.3390/f10080680] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
To promote Bio-Energy with Carbon dioxide Capture and Storage (BECCS), which aims to replace fossil fuels with bio energy and store carbon underground, and Reducing Emissions from Deforestation and forest Degradation (REDD+), which aims to reduce the carbon emissions produced by forest degradation, it is important to build forest management plans based on the scientific prediction of forest dynamics. For Measurement, Reporting and Verification (MRV) at an individual tree level, it is expected that techniques will be developed to support forest management via the effective monitoring of changes to individual trees. In this study, an end-to-end process was developed: (1) detecting individual trees from Unmanned Aerial Vehicle (UAV) derived digital images; (2) estimating the stand structure from crown images; (3) visualizing future carbon dynamics using a forest ecosystem process model. This process could detect 93.4% of individual trees, successfully classified two species using Convolutional Neural Network (CNN) with 83.6% accuracy and evaluated future ecosystem carbon dynamics and the source-sink balance using individual based model FORMIND. Further ideas for improving the sub-process of the end to end process were discussed. This process is expected to contribute to activities concerned with carbon management such as designing smart utilization for biomass resources and projecting scenarios for the sustainable use of ecosystem services.
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20
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Estimation and Mapping of Forest Structure Parameters from Open Access Satellite Images: Development of a Generic Method with a Study Case on Coniferous Plantation. REMOTE SENSING 2019. [DOI: 10.3390/rs11111275] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Temperate forests are under climatic and economic pressures. Public bodies, NGOs and the wood industry are looking for accurate, current and affordable data driven solutions to intensify wood production while maintaining or improving long term sustainability of the production, biodiversity, and carbon sequestration. Free tools and open access data have already been exploited to produce accurate quantitative forest parameters maps suitable for policy and operational purposes. These efforts have relied on different data sources, tools, and methods that are tailored for specific forest types and climatic conditions. We hypothesized we could build on these efforts in order to produce a generic method suitable to perform as well or better in a larger range of settings. In this study we focus on building a generic approach to create forest parameters maps and confirm its performance on a test site: a maritime pine (Pinus pinaster) forest located in south west of France. We investigated and assessed options related with the integration of multiple data sources (SAR L- and C-band, optical indexes and spatial texture indexes from Sentinel-1, Sentinel-2 and ALOS-PALSAR-2), feature extraction, feature selection and machine learning techniques. On our test case, we found that the combination of multiple open access data sources has synergistic benefits on the forest parameters estimates. The sensibility analysis shows that all the data participate to the improvements, that reach up to 13.7% when compared to single source estimates. Accuracy of the estimates is as follows: aboveground biomass (AGB) 28% relative RMSE, basal area (BA) 27%, diameter at breast height (DBH) 20%, age 17%, tree density 24%, and height 13%. Forward feature selection and SVR provided the best estimates. Future work will focus on validating this generic approach in different settings. It may prove beneficial to package the method, the tools, and the integration of open access data in order to make spatially accurate and regularly updated forest structure parameters maps effortlessly available to national bodies and forest organizations.
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Duncanson L, Armston J, Disney M, Avitabile V, Barbier N, Calders K, Carter S, Chave J, Herold M, Crowther TW, Falkowski M, Kellner JR, Labrière N, Lucas R, MacBean N, McRoberts RE, Meyer V, Næsset E, Nickeson JE, Paul KI, Phillips OL, Réjou-Méchain M, Román M, Roxburgh S, Saatchi S, Schepaschenko D, Scipal K, Siqueira PR, Whitehurst A, Williams M. The Importance of Consistent Global Forest Aboveground Biomass Product Validation. SURVEYS IN GEOPHYSICS 2019; 40:979-999. [PMID: 31395994 PMCID: PMC6647371 DOI: 10.1007/s10712-019-09538-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 04/27/2019] [Indexed: 05/17/2023]
Abstract
Several upcoming satellite missions have core science requirements to produce data for accurate forest aboveground biomass mapping. Largely because of these mission datasets, the number of available biomass products is expected to greatly increase over the coming decade. Despite the recognized importance of biomass mapping for a wide range of science, policy and management applications, there remains no community accepted standard for satellite-based biomass map validation. The Committee on Earth Observing Satellites (CEOS) is developing a protocol to fill this need in advance of the next generation of biomass-relevant satellites, and this paper presents a review of biomass validation practices from a CEOS perspective. We outline the wide range of anticipated user requirements for product accuracy assessment and provide recommendations for the validation of biomass products. These recommendations include the collection of new, high-quality in situ data and the use of airborne lidar biomass maps as tools toward transparent multi-resolution validation. Adoption of community-vetted validation standards and practices will facilitate the uptake of the next generation of biomass products.
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Affiliation(s)
- L. Duncanson
- Department of Geographical Sciences, University of Maryland, College Park, 2181 Lefrak Hall, College Park, MD 20742 USA
| | - J. Armston
- Department of Geographical Sciences, University of Maryland, College Park, 2181 Lefrak Hall, College Park, MD 20742 USA
| | - M. Disney
- Department of Geography, University College London, Gower Street, London, WC1E 6BT UK
| | - V. Avitabile
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, 21027 Ispra, Italy
| | - N. Barbier
- AMAP, IRD, CIRAD,
CNRS, INRA, Montpellier University, TA A51/PS2, 34398 Montpellier cedex 5, France
| | - K. Calders
- CAVElab – Computational and Applied Vegetation Ecology, Ghent University, Room A2.089, Coupure Links 653, 9000 Ghent, Belgium
| | - S. Carter
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
| | - J. Chave
- Laboratoire Evolution et Diversit. Biologique, UMR 5174, CNRS, Universit. Toulouse Paul Sabatier, 118 route de Narbonne, 31062 Toulouse cedex 9, France
| | - M. Herold
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
| | - T. W. Crowther
- Institute of Integrative Biology, ETH Zürich, Univeritätstrasse 16, 8006 Zurich, Switzerland
| | - M. Falkowski
- Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523 USA
| | - J. R. Kellner
- Institute at Brown for Environment and Society, Brown University, Providence, RI 02912 USA
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI 02912 USA
| | - N. Labrière
- Laboratoire Evolution et Diversit. Biologique, UMR 5174, CNRS, Universit. Toulouse Paul Sabatier, 118 route de Narbonne, 31062 Toulouse cedex 9, France
| | - R. Lucas
- Earth Observation and Ecosystem Dynamics Research Group, Department of Geography and Earth Sciences (DGES), Aberystwyth University, Aberystwyth, Wales SY23 3DB UK
| | - N. MacBean
- Department of Geography, Indiana University, 701 E. Kirkwood Ave., Bloomington, IN 47405 USA
| | - R. E. McRoberts
- USDA Forest Service, Northern Research Station, Saint Paul, 1992 Folwell Ave, St Paul, MN 55108 USA
| | - V. Meyer
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA
| | - E. Næsset
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, 1432 Ås, Norway
| | - J. E. Nickeson
- NASA Goddard Space Flight Center/Science Systems and Applications Inc., 10210 Greenbelt Rd #600, Lanham, MD 20706 USA
| | - K. I. Paul
- CSIRO Land and Water, GPO Box 1700, Canberra, ACT 2601 Australia
| | - O. L. Phillips
- School of Geography, University of Leeds, Leeds, LS2 9JT UK
| | - M. Réjou-Méchain
- AMAP, IRD, CIRAD,
CNRS, INRA, Montpellier University, TA A51/PS2, 34398 Montpellier cedex 5, France
| | - M. Román
- Earth from Space Institute, Universities Space Research Association, Columbia, MD USA
| | - S. Roxburgh
- CSIRO Land and Water, GPO Box 1700, Canberra, ACT 2601 Australia
| | - S. Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA
| | - D. Schepaschenko
- International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria
| | - K. Scipal
- European Space Agency, ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands
| | - P. R. Siqueira
- Department of Electrical and Computer Engineering, 201 Marcus Hall, University of Massachusetts, 100 Natural Resources Road, Amherst, MA 01003 USA
| | - A. Whitehurst
- Arctic Slope Federal Technical Services, 7000 Muirkirk Meadows Dr #100, Laurel, MD 20707 USA
| | - M. Williams
- School of GeoScience, University of Edinburgh, Drummond St, Edinburgh, EH8 9XP UK
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Santos de Lima L, Merry F, Soares-Filho B, Oliveira Rodrigues H, dos Santos Damaceno C, Bauch MA. Illegal logging as a disincentive to the establishment of a sustainable forest sector in the Amazon. PLoS One 2018; 13:e0207855. [PMID: 30517153 PMCID: PMC6281205 DOI: 10.1371/journal.pone.0207855] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 11/07/2018] [Indexed: 11/23/2022] Open
Abstract
Brazil recently began granting timber concessions in public forests to promote sustainable forest use. The effectiveness of this strategy hinges on the design and implementation of the concessions themselves as well as their competitive position within the logging sector as a whole. There is, however, a lack of information on the competitive interaction between legal and illegal logging and its effects on concessions profits. We address this knowledge gap by using a spatially explicit simulation model of the Amazon timber industry to examine the potential impact of illegal logging on timber concessions allocation and profits in a 30-year harvest cycle. In a scenario in which illegal logging takes place outside concessions, including private and public “undesignated” lands, concession harvested area would decrease by 59% due to competition with illegal logging. Moreover, 29 out of 39 National Forests (≈74%) would experience a decrease in harvested area. This “leakage” effect could reduce concession net rents by up to USD 1.3 Billion after 30 years. Federal and State “undesignated” lands, if not adequately protected, could have 40% of their total volume illegally harvested in 30 years. Our results reinforce the need to invest in tackling illegal logging, if the government wants the forest concessions program to be successful.
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Affiliation(s)
- Letícia Santos de Lima
- Departamento de Engenharia Hidráulica e Recursos Hídricos, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Integrative Research Institute on Transformations of Human-Environment Systems, Humboldt-Universität zu Berlin, Berlin, Germany
- * E-mail:
| | - Frank Merry
- Conservation Strategy Fund, Washington, D.C., United States of America
| | - Britaldo Soares-Filho
- Centro de Sensoriamento Remoto, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Hermann Oliveira Rodrigues
- Centro de Sensoriamento Remoto, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Marcos A. Bauch
- Serviço Florestal Brasileiro, Ministério de Meio Ambiente, Governo do Brasil, Brasília, Distrito Federal, Brazil
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Bell DM, Gregory MJ, Kane V, Kane J, Kennedy RE, Roberts HM, Yang Z. Multiscale divergence between Landsat- and lidar-based biomass mapping is related to regional variation in canopy cover and composition. CARBON BALANCE AND MANAGEMENT 2018; 13:15. [PMID: 30218413 PMCID: PMC6138055 DOI: 10.1186/s13021-018-0104-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 09/07/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Satellite-based aboveground forest biomass maps commonly form the basis of forest biomass and carbon stock mapping and monitoring, but biomass maps likely vary in performance by region and as a function of spatial scale of aggregation. Assessing such variability is not possible with spatially-sparse vegetation plot networks. In the current study, our objective was to determine whether high-resolution lidar-based and moderate-resolution Landsat-base aboveground live forest biomass maps converged on similar predictions at stand- to landscape-levels (10 s to 100 s ha) and whether such differences depended on biophysical setting. Specifically, we examined deviations between lidar- and Landsat-based biomass mapping methods across scales and ecoregions using a measure of error (normalized root mean square deviation), a measure of the unsystematic deviations, or noise (Pearson correlation coefficient), and two measures related to systematic deviations, or biases (intercept and slope of a regression between the two sets of predictions). RESULTS Compared to forest inventory data (0.81-ha aggregate-level), lidar and Landsat-based mean biomass predictions exhibited similar performance, though lidar predictions exhibited less normalized root mean square deviation than Landsat when compared with the reference plot data. Across aggregate-levels, the intercepts and slopes of regression equations describing the relationships between lidar- and Landsat-based biomass predictions stabilized (i.e., little additional change with increasing area of aggregates) at aggregate-levels between 10 and 100 ha, suggesting a consistent relationship between the two maps at landscape-scales. Differences between lidar- and Landsat-based biomass maps varied as a function of forest canopy heterogeneity and composition, with systematic deviations (regression intercepts) increasing with mean canopy cover and hardwood proportion within forests and correlations decreasing with hardwood proportion. CONCLUSIONS Deviations between lidar- and Landsat-based maps indicated that satellite-based approaches may represent general gradients in forest biomass. Ecoregion impacted deviations between lidar and Landsat biomass maps, highlighting the importance of biophysical setting in determining biomass map performance across aggregate scales. Therefore, regardless of the source of remote sensing (e.g., Landsat vs. lidar), factors affecting the measurement and prediction of forest biomass, such as species composition, need to be taken into account whether one is estimating biomass at the plot, stand, or landscape scale.
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Affiliation(s)
- David M. Bell
- Pacific Northwest Research Station, USDA Forest Service, 3200 SW Jefferson Way, Corvallis, OR 97331 USA
| | - Matthew J. Gregory
- Forest Ecosystems and Society Department, Oregon State University, Corvallis, OR USA
| | - Van Kane
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA USA
| | - Jonathan Kane
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA USA
| | - Robert E. Kennedy
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR USA
| | - Heather M. Roberts
- Forest Ecosystems and Society Department, Oregon State University, Corvallis, OR USA
| | - Zhiqiang Yang
- Forest Ecosystems and Society Department, Oregon State University, Corvallis, OR USA
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Hanan EJ, Tague C, Choate J, Liu M, Kolden C, Adam J. Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin-up. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2018; 28:1197-1214. [PMID: 29573305 DOI: 10.1002/eap.1718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 02/07/2018] [Accepted: 02/21/2018] [Indexed: 06/08/2023]
Abstract
Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.
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Affiliation(s)
- Erin J Hanan
- Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, 99164, USA
| | - Christina Tague
- Department of Environmental Science, Policy and Management, University of California Santa Barbara, Santa Barbara, California, 93106, USA
| | - Janet Choate
- Department of Environmental Science, Policy and Management, University of California Santa Barbara, Santa Barbara, California, 93106, USA
| | - Mingliang Liu
- Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, 99164, USA
| | - Crystal Kolden
- Department of Geography, University of Idaho, Moscow, Idaho, 83844, USA
| | - Jennifer Adam
- Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, 99164, USA
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The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest. FORESTS 2018. [DOI: 10.3390/f9060303] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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26
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Schwieder M, Leitão PJ, Pinto JRR, Teixeira AMC, Pedroni F, Sanchez M, Bustamante MM, Hostert P. Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna. CARBON BALANCE AND MANAGEMENT 2018; 13:7. [PMID: 29766371 PMCID: PMC5953907 DOI: 10.1186/s13021-018-0097-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 05/05/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND The quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global carbon cycle and to monitor and report change processes, especially in the context of international policy mechanisms such as REDD+ or the implementation of Nationally Determined Contributions (NDCs) and the UN Sustainable Development Goals (SDGs). Especially in heterogeneous ecosystems, such as Savannas, accurate carbon quantifications are still lacking, where highly variable vegetation densities occur and a strong seasonality hinders consistent data acquisition. In order to account for these challenges we analyzed the potential of land surface phenological metrics derived from gap-filled 8-day Landsat time series for carbon mapping. We selected three areas located in different subregions in the central Brazil region, which is a prominent example of a Savanna with significant carbon stocks that has been undergoing extensive land cover conversions. Here phenological metrics from the season 2014/2015 were combined with aboveground carbon field samples of cerrado sensu stricto vegetation using Random Forest regression models to map the regional carbon distribution and to analyze the relation between phenological metrics and aboveground carbon. RESULTS The gap filling approach enabled to accurately approximate the original Landsat ETM+ and OLI EVI values and the subsequent derivation of annual phenological metrics. Random Forest model performances varied between the three study areas with RMSE values of 1.64 t/ha (mean relative RMSE 30%), 2.35 t/ha (46%) and 2.18 t/ha (45%). Comparable relationships between remote sensing based land surface phenological metrics and aboveground carbon were observed in all study areas. Aboveground carbon distributions could be mapped and revealed comprehensible spatial patterns. CONCLUSION Phenological metrics were derived from 8-day Landsat time series with a spatial resolution that is sufficient to capture gradual changes in carbon stocks of heterogeneous Savanna ecosystems. These metrics revealed the relationship between aboveground carbon and the phenology of the observed vegetation. Our results suggest that metrics relating to the seasonal minimum and maximum values were the most influential variables and bear potential to improve spatially explicit mapping approaches in heterogeneous ecosystems, where both spatial and temporal resolutions are critical.
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Affiliation(s)
- M Schwieder
- Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany.
| | - P J Leitão
- Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
- Department Landscape Ecology and Environmental System Analysis, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, 38106, Braunschweig, Germany
| | - J R R Pinto
- Departamento de Engenharia Florestal, Universidade de Brasília, Brasília, DF, 70919-970, Brazil
| | - A M C Teixeira
- Graduate Program in Botany, University of Brasília, Brasília, DF, 70919-970, Brazil
| | - F Pedroni
- Instituto de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso, Pontal do Araguaia, MT, 78698-000, Brazil
| | - M Sanchez
- Instituto de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso, Pontal do Araguaia, MT, 78698-000, Brazil
| | - M M Bustamante
- Departamento de Ecologia, Universidade de Brasília, Brasília, DF, 70919-970, Brazil
| | - P Hostert
- Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
- Integrative Research Institute on Transformations of Human-Environment Systems-IRI THESys, Humboldt-Universitätzu Berlin, Unter den Linden 6, 10099, Berlin, Germany
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Lohberger S, Stängel M, Atwood EC, Siegert F. Spatial evaluation of Indonesia's 2015 fire-affected area and estimated carbon emissions using Sentinel-1. GLOBAL CHANGE BIOLOGY 2018; 24:644-654. [PMID: 28746734 DOI: 10.1111/gcb.13841] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 07/16/2017] [Indexed: 05/16/2023]
Abstract
Fires raged once again across Indonesia in the latter half of 2015, creating a state of emergency due to poisonous smoke and haze across Southeast Asia as well as incurring great financial costs to the government. A strong El Niño-Southern Oscillation (ENSO) led to drought in many parts of Indonesia, resulting in elevated fire occurrence comparable with the previous catastrophic event in 1997/1998. Synthetic Aperture Radar (SAR) data promise to provide improved detection of land use and land cover changes in the tropics as compared to methodologies dependent upon cloud- and haze-free images. This study presents the first spatially explicit estimates of burned area across Sumatra, Kalimantan, and West Papua based on high-resolution Sentinel-1A SAR imagery. Here, we show that 4,604,569 hectares (ha) were burned during the 2015 fire season (overall accuracy 84%), and compare this with other existing operational burned area products (MCD64, GFED4.0, GFED4.1s). Intersection of burned area with fine-scale land cover and peat layer maps indicates that 0.89 gigatons carbon dioxide equivalents (Gt CO2 e) were released through the fire event. This result is compared to other estimates based on nonspatially explicit thermal anomaly measurements or atmospheric monitoring. Using freely available SAR C-band data from the Sentinel mission, we argue that the presented methodology is able to quickly and precisely detect burned areas, supporting improvement in fire control management as well as enhancing accuracy of emissions estimation.
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Affiliation(s)
| | | | - Elizabeth C Atwood
- RSS Remote Sensing Solutions GmbH, Baierbrunn, Germany
- Department of Biology II, GeoBio Center, Ludwig-Maximilians-Universität Munich, Planegg-Martinsried, Germany
| | - Florian Siegert
- RSS Remote Sensing Solutions GmbH, Baierbrunn, Germany
- Department of Biology II, GeoBio Center, Ludwig-Maximilians-Universität Munich, Planegg-Martinsried, Germany
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28
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Estes L, Chen P, Debats S, Evans T, Ferreira S, Kuemmerle T, Ragazzo G, Sheffield J, Wolf A, Wood E, Caylor K. A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses. GLOBAL CHANGE BIOLOGY 2018; 24:322-337. [PMID: 28921806 DOI: 10.1111/gcb.13904] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 07/24/2017] [Indexed: 06/07/2023]
Abstract
Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ∼45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higher-resolution imagery were most accurate, followed by multi-map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland-adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but ∼40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users.
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Affiliation(s)
- Lyndon Estes
- Graduate School of Geography, Clark University, Worcester, MA, USA
- Woodrow Wilson School, Princeton University, Princeton, NJ, USA
- Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
| | - Peng Chen
- Department of Geography, Indiana University, Bloomington, IN, USA
| | - Stephanie Debats
- Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
| | - Tom Evans
- Department of Geography, Indiana University, Bloomington, IN, USA
| | | | - Tobias Kuemmerle
- Geography Department, Humboldt University, Berlin, Germany
- Integrative Research Institute for Transformations in Human-Environment Systems, Humboldt University, Berlin, Germany
| | - Gabrielle Ragazzo
- Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
| | - Justin Sheffield
- Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
- Geography and Environment, University of Southampton, Southampton, UK
| | | | - Eric Wood
- Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
| | - Kelly Caylor
- Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
- Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, USA
- Department of Geography, University of California Santa Barbara, Santa Barbara, CA, USA
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29
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Xu L, Saatchi SS, Shapiro A, Meyer V, Ferraz A, Yang Y, Bastin JF, Banks N, Boeckx P, Verbeeck H, Lewis SL, Muanza ET, Bongwele E, Kayembe F, Mbenza D, Kalau L, Mukendi F, Ilunga F, Ebuta D. Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo. Sci Rep 2017; 7:15030. [PMID: 29118358 PMCID: PMC5678085 DOI: 10.1038/s41598-017-15050-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 10/16/2017] [Indexed: 11/10/2022] Open
Abstract
National forest inventories in tropical regions are sparse and have large uncertainty in capturing the physiographical variations of forest carbon across landscapes. Here, we produce for the first time the spatial patterns of carbon stored in forests of Democratic Republic of Congo (DRC) by using airborne LiDAR inventory of more than 432,000 ha of forests based on a designed probability sampling methodology. The LiDAR mean top canopy height measurements were trained to develop an unbiased carbon estimator by using 92 1-ha ground plots distributed across key forest types in DRC. LiDAR samples provided estimates of mean and uncertainty of aboveground carbon density at provincial scales and were combined with optical and radar satellite imagery in a machine learning algorithm to map forest height and carbon density over the entire country. By using the forest definition of DRC, we found a total of 23.3 ± 1.6 GtC carbon with a mean carbon density of 140 ± 9 MgC ha-1 in the aboveground and belowground live trees. The probability based LiDAR samples capture variations of structure and carbon across edaphic and climate conditions, and provide an alternative approach to national ground inventory for efficient and precise assessment of forest carbon resources for emission reduction (ER) programs.
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Affiliation(s)
- Liang Xu
- Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA.
| | - Sassan S Saatchi
- Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA.,Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Aurélie Shapiro
- World Wide Fund for Nature(WWF) Germany Biodiversity Unit, Berlin, Germany
| | - Victoria Meyer
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Antonio Ferraz
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Yan Yang
- Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA
| | - Jean-Francois Bastin
- Landscape Ecology and Plant Production Systems Unit, Université libre de Bruxelles, Bruxelles, Belgium.,BIOSE department, Gembloux Agro Bio Tech, Gembloux, Belgium
| | - Norman Banks
- Southern Mapping Company, Airborne LiDAR Survey Unit, Johannesburg, South Africa
| | - Pascal Boeckx
- Isotope Bioscience Laboratory - ISOFYS, Ghent University, Ghent, Belgium
| | - Hans Verbeeck
- CAVElab - Computational and Applied Vegetation Ecology, Ghent University, Ghent, Belgium
| | - Simon L Lewis
- School of Geography, University of Leeds, Leeds, UK.,Department of Geography, University College London, London, UK
| | | | - Eddy Bongwele
- Observatoire Satellital des Forets d'Afrique Central (OSFAC), Kinshasa, Democratic Republic of the Congo
| | - Francois Kayembe
- Direction des Inventaires et Aménagement Forestiers (DIAF), Kinshasa, Democratic Republic of the Congo
| | - Daudet Mbenza
- Direction des Inventaires et Aménagement Forestiers (DIAF), Kinshasa, Democratic Republic of the Congo
| | - Laurent Kalau
- Direction des Inventaires et Aménagement Forestiers (DIAF), Kinshasa, Democratic Republic of the Congo
| | - Franck Mukendi
- Direction des Inventaires et Aménagement Forestiers (DIAF), Kinshasa, Democratic Republic of the Congo
| | - Francis Ilunga
- Direction des Inventaires et Aménagement Forestiers (DIAF), Kinshasa, Democratic Republic of the Congo
| | - Daniel Ebuta
- Direction des Inventaires et Aménagement Forestiers (DIAF), Kinshasa, Democratic Republic of the Congo
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30
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Kearney SP, Coops NC, Chan KMA, Fonte SJ, Siles P, Smukler SM. Predicting carbon benefits from climate-smart agriculture: High-resolution carbon mapping and uncertainty assessment in El Salvador. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2017; 202:287-298. [PMID: 28738202 DOI: 10.1016/j.jenvman.2017.07.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 07/12/2017] [Accepted: 07/15/2017] [Indexed: 06/07/2023]
Abstract
Agroforestry management in smallholder agriculture can provide climate change mitigation and adaptation benefits and has been promoted as 'climate-smart agriculture' (CSA), yet has generally been left out of international and voluntary carbon (C) mitigation agreements. A key reason for this omission is the cost and uncertainty of monitoring C at the farm scale in heterogeneous smallholder landscapes. A largely overlooked alternative is to monitor C at more aggregated scales and develop C contracts with groups of land owners, community organizations or C aggregators working across entire landscapes (e.g., watersheds, communities, municipalities, etc.). In this study we use a 100-km2 agricultural area in El Salvador to demonstrate how high-spatial resolution optical satellite imagery can be used to map aboveground woody biomass (AGWB) C at the landscape scale with very low uncertainty (95% probability of a deviation of less than 1%). Uncertainty of AGWB-C estimates remained low (<5%) for areas as small as 250 ha, despite high uncertainties at the farm and plot scale (34-99%). We estimate that CSA adoption could more than double AGWB-C stocks on agricultural lands in the study area, and that utilizing AGWB-C maps to target denuded areas could increase C gains per unit area by 46%. The potential value of C credits under a plausible adoption scenario would range from $38,270 to $354,000 yr-1 for the study area, or about $13 to $124 ha-1 yr-1, depending on C prices. Considering farm sizes in smallholder landscapes rarely exceed 1-2 ha, relying solely on direct C payments to farmers may not lead to widespread CSA adoption, especially if farm-scale monitoring is required. Instead, landscape-scale approaches to C contracting, supported by satellite-based monitoring methods such as ours, could be a key strategy to reduce costs and uncertainty of C monitoring in heterogeneous smallholder landscapes, thereby incentivizing more widespread CSA adoption.
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Affiliation(s)
- Sean Patrick Kearney
- Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada.
| | - Nicholas C Coops
- Department of Forest Resource Management, 2424 Main Mall, University of British Columbia, Vancouver, V6T 1Z4, Canada.
| | - Kai M A Chan
- Institute for Resources, Environment and Sustainability, University of British Columbia, 2202, Main Mall, Vancouver, V6T 1Z4, Canada.
| | - Steven J Fonte
- Department of Soil and Crop Sciences, Colorado State University, 1170 Campus Delivery, Fort Collins, CO 80523, USA.
| | - Pablo Siles
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Apartado Aéreo 6713, Cali, 763537, Colombia.
| | - Sean M Smukler
- Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada; Tropical Agriculture Program, The Earth Institute at Columbia University, 61 Route 9W, Lamont Hall, Room 2H, Palisades, NY 10964-8000, USA.
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31
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Duncanson L, Huang W, Johnson K, Swatantran A, McRoberts RE, Dubayah R. Implications of allometric model selection for county-level biomass mapping. CARBON BALANCE AND MANAGEMENT 2017; 12:18. [PMID: 29046991 PMCID: PMC5647317 DOI: 10.1186/s13021-017-0086-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 10/07/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Carbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend on the application of allometric models, which often have unknown and unreported uncertainties outside of the size class or environment in which they were developed. RESULTS Here, we test three popular allometric approaches to field biomass estimation, and explore the implications of allometric model selection for county-level biomass mapping in Sonoma County, California. We test three allometric models: Jenkins et al. (For Sci 49(1): 12-35, 2003), Chojnacky et al. (Forestry 87(1): 129-151, 2014) and the US Forest Service's Component Ratio Method (CRM). We found that Jenkins and Chojnacky models perform comparably, but that at both a field plot level and a total county level there was a ~ 20% difference between these estimates and the CRM estimates. Further, we show that discrepancies are greater in high biomass areas with high canopy covers and relatively moderate heights (25-45 m). The CRM models, although on average ~ 20% lower than Jenkins and Chojnacky, produce higher estimates in the tallest forests samples (> 60 m), while Jenkins generally produces higher estimates of biomass in forests < 50 m tall. Discrepancies do not continually increase with increasing forest height, suggesting that inclusion of height in allometric models is not primarily driving discrepancies. Models developed using all three allometric models underestimate high biomass and overestimate low biomass, as expected with random forest biomass modeling. However, these deviations were generally larger using the Jenkins and Chojnacky allometries, suggesting that the CRM approach may be more appropriate for biomass mapping with lidar. CONCLUSIONS These results confirm that allometric model selection considerably impacts biomass maps and estimates, and that allometric model errors remain poorly understood. Our findings that allometric model discrepancies are not explained by lidar heights suggests that allometric model form does not drive these discrepancies. A better understanding of the sources of allometric model errors, particularly in high biomass systems, is essential for improved forest biomass mapping.
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Affiliation(s)
- Laura Duncanson
- Biosciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, USA
- Department of Geographical Sciences, University of Maryland, College Park, USA
| | - Wenli Huang
- Department of Geographical Sciences, University of Maryland, College Park, USA
| | - Kristofer Johnson
- USDA Forest Service, Northern Research Station, Newton Square, PA USA
| | - Anu Swatantran
- Department of Geographical Sciences, University of Maryland, College Park, USA
| | | | - Ralph Dubayah
- Department of Geographical Sciences, University of Maryland, College Park, USA
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32
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Spectral Similarity and PRI Variations for a Boreal Forest Stand Using Multi-angular Airborne Imagery. REMOTE SENSING 2017. [DOI: 10.3390/rs9101005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Baccini A, Walker W, Carvalho L, Farina M, Sulla-Menashe D, Houghton RA. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 2017; 358:230-234. [DOI: 10.1126/science.aam5962] [Citation(s) in RCA: 421] [Impact Index Per Article: 60.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 09/08/2017] [Indexed: 01/30/2023]
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34
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Assessing and Monitoring Forest Degradation in a Deciduous Tropical Forest in Mexico via Remote Sensing Indicators. FORESTS 2017. [DOI: 10.3390/f8090302] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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35
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Understanding Measurement Reporting and Verification Systems for REDD+ as an Investment for Generating Carbon Benefits. FORESTS 2017. [DOI: 10.3390/f8080271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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36
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Koju U, Zhang J, Gilani H. Exploring multi-scale forest above ground biomass estimation with optical remote sensing imageries. ACTA ACUST UNITED AC 2017. [DOI: 10.1088/1755-1315/57/1/012011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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37
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Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics. REMOTE SENSING 2016. [DOI: 10.3390/rs8121029] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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38
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Marvin DC, Asner GP. Spatially explicit analysis of field inventories for national forest carbon monitoring. CARBON BALANCE AND MANAGEMENT 2016; 11:9. [PMID: 27335582 PMCID: PMC4894926 DOI: 10.1186/s13021-016-0050-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Accepted: 05/16/2016] [Indexed: 05/22/2023]
Abstract
BACKGROUND Tropical forests provide a crucial carbon sink for a sizable portion of annual global CO2 emissions. Policies that incentivize tropical forest conservation by monetizing forest carbon ultimately depend on accurate estimates of national carbon stocks, which are often based on field inventory sampling. As an exercise to understand the limitations of field inventory sampling, we tested whether two common field-plot sampling approaches could accurately estimate carbon stocks across approximately 76 million ha of Perúvian forests. A 1-ha resolution LiDAR-based map of carbon stocks was used as a model of the country's carbon geography. RESULTS Both field inventory sampling approaches worked well in estimating total national carbon stocks, almost always falling within 10 % of the model national total. However, the sampling approaches were unable to produce accurate spatially-explicit estimates of the carbon geography of Perú, with estimates falling within 10 % of the model carbon geography across no more than 44 % of the country. We did not find any associations between carbon stock errors from the field plot estimates and six different environmental variables. CONCLUSIONS Field inventory plot sampling does not provide accurate carbon geography for a tropical country with wide ranging environmental gradients such as Perú. The lack of association between estimated carbon errors and environmental variables suggests field inventory sampling results from other nations would not differ from those reported here. Tropical forest nations should understand the risks associated with primarily field-based sampling approaches, and consider alternatives leading to more effective forest conservation and climate change mitigation.
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Affiliation(s)
- David C. Marvin
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama St., Stanford, CA 94305 USA
| | - Gregory P. Asner
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama St., Stanford, CA 94305 USA
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Quiroz Arita C, Yilmaz Ö, Barlak S, Catton KB, Quinn JC, Bradley TH. A geographical assessment of vegetation carbon stocks and greenhouse gas emissions on potential microalgae-based biofuel facilities in the United States. BIORESOURCE TECHNOLOGY 2016; 221:270-275. [PMID: 27643735 DOI: 10.1016/j.biortech.2016.09.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 09/01/2016] [Accepted: 09/02/2016] [Indexed: 06/06/2023]
Abstract
The microalgae biofuels life cycle assessments (LCA) present in the literature have excluded the effects of direct land use change (DLUC) from facility construction under the assumption that DLUC effects are negligible. This study seeks to model the greenhouse gas (GHG) emissions of microalgae biofuels including DLUC by quantifying the CO2 equivalence of carbon released to the atmosphere through the construction of microalgae facilities. The locations and types of biomass and Soil Organic Carbon that are disturbed through microalgae cultivation facility construction are quantified using geographical models of microalgae productivity potential including consideration of land availability. The results of this study demonstrate that previous LCA of microalgae to biofuel processes have overestimated GHG benefits of microalgae-based biofuels production by failing to include the effect of DLUC. Previous estimations of microalgae biofuel production potential have correspondingly overestimated the volume of biofuels that can be produced in compliance with U.S. environmental goals.
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Affiliation(s)
- Carlos Quiroz Arita
- Mechanical Engineering, 1374 Campus Delivery, Colorado State University, Fort Collins, CO 80524, USA.
| | - Özge Yilmaz
- Civil and Environmental Engineering, 1372 Campus Delivery, Colorado State University, Fort Collins, CO 80523, USA
| | - Semin Barlak
- Civil and Environmental Engineering, 1372 Campus Delivery, Colorado State University, Fort Collins, CO 80523, USA
| | - Kimberly B Catton
- Civil and Environmental Engineering, 1372 Campus Delivery, Colorado State University, Fort Collins, CO 80523, USA
| | - Jason C Quinn
- Mechanical Engineering, 1374 Campus Delivery, Colorado State University, Fort Collins, CO 80524, USA
| | - Thomas H Bradley
- Mechanical Engineering, 1374 Campus Delivery, Colorado State University, Fort Collins, CO 80524, USA
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Piponiot C, Cabon A, Descroix L, Dourdain A, Mazzei L, Ouliac B, Rutishauser E, Sist P, Hérault B. A methodological framework to assess the carbon balance of tropical managed forests. CARBON BALANCE AND MANAGEMENT 2016; 11:15. [PMID: 27525036 PMCID: PMC4967106 DOI: 10.1186/s13021-016-0056-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Accepted: 07/05/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND Managed forests are a major component of tropical landscapes. Production forests as designated by national forest services cover up to 400 million ha, i.e. half of the forested area in the humid tropics. Forest management thus plays a major role in the global carbon budget, but with a lack of unified method to estimate carbon fluxes from tropical managed forests. In this study we propose a new time- and spatially-explicit methodology to estimate the above-ground carbon budget of selective logging at regional scale. RESULTS The yearly balance of a logging unit, i.e. the elementary management unit of a forest estate, is modelled by aggregating three sub-models encompassing (i) emissions from extracted wood, (ii) emissions from logging damage and deforested areas and (iii) carbon storage from post-logging recovery. Models are parametrised and uncertainties are propagated through a MCMC algorithm. As a case study, we used 38 years of National Forest Inventories in French Guiana, northeastern Amazonia, to estimate the above-ground carbon balance (i.e. the net carbon exchange with the atmosphere) of selectively logged forests. Over this period, the net carbon balance of selective logging in the French Guianan Permanent Forest Estate is estimated to be comprised between 0.12 and 1.33 Tg C, with a median value of 0.64 Tg C. Uncertainties over the model could be diminished by improving the accuracy of both logging damage and large woody necromass decay submodels. CONCLUSIONS We propose an innovating carbon accounting framework relying upon basic logging statistics. This flexible tool allows carbon budget of tropical managed forests to be estimated in a wide range of tropical regions.
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Affiliation(s)
- Camille Piponiot
- Université de la Guyane, UMR EcoFoG (AgroParisTech, CNRS, Inra, Université des Antilles, Cirad), Campus agronomique, 97310 Kourou, French Guiana
- CNRS, UMR EcoFoG (AgroParisTech, Inra, Université de la Guyane, Université des Antilles, Cirad), Campus Agronomique, 97310 Kourou, French Guiana
| | - Antoine Cabon
- Centre Tecnològic Forestal de Catalunya, Crta. de Sant Llorenç de Morunys, Km.2, 25280 Solsona, Spain
| | | | - Aurélie Dourdain
- Cirad, UMR EcoFoG (AgroParisTech, CNRS, Inra, Université de la Guyane, Université des Antilles), Campus agronomique, 97310 Kourou, French Guiana
| | - Lucas Mazzei
- EMBRAPA Amazônia Oriental, Trav. Dr. Enéas Pinheiro, Belém, Brazil
| | - Benjamin Ouliac
- Guyane Energie Climat, 16 rue Victor Schoelcher, 97300 Cayenne, French Guiana
| | | | - Plinio Sist
- Cirad UR Forêts et Sociétés, 34398 Montpellier, France
| | - Bruno Hérault
- Cirad, UMR EcoFoG (AgroParisTech, CNRS, Inra, Université de la Guyane, Université des Antilles), Campus agronomique, 97310 Kourou, French Guiana
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Asner GP, Sousan S, Knapp DE, Selmants PC, Martin RE, Hughes RF, Giardina CP. Rapid forest carbon assessments of oceanic islands: a case study of the Hawaiian archipelago. CARBON BALANCE AND MANAGEMENT 2016; 11:1. [PMID: 26793270 PMCID: PMC4705141 DOI: 10.1186/s13021-015-0043-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 12/22/2015] [Indexed: 06/05/2023]
Abstract
BACKGROUND Spatially explicit forest carbon (C) monitoring aids conservation and climate change mitigation efforts, yet few approaches have been developed specifically for the highly heterogeneous landscapes of oceanic island chains that continue to undergo rapid and extensive forest C change. We developed an approach for rapid mapping of aboveground C density (ACD; units = Mg or metric tons C ha-1) on islands at a spatial resolution of 30 m (0.09 ha) using a combination of cost-effective airborne LiDAR data and full-coverage satellite data. We used the approach to map forest ACD across the main Hawaiian Islands, comparing C stocks within and among islands, in protected and unprotected areas, and among forests dominated by native and invasive species. RESULTS Total forest aboveground C stock of the Hawaiian Islands was 36 Tg, and ACD distributions were extremely heterogeneous both within and across islands. Remotely sensed ACD was validated against U.S. Forest Service FIA plot inventory data (R2 = 0.67; RMSE = 30.4 Mg C ha-1). Geospatial analyses indicated the critical importance of forest type and canopy cover as predictors of mapped ACD patterns. Protection status was a strong determinant of forest C stock and density, but we found complex environmentally mediated responses of forest ACD to alien plant invasion. CONCLUSIONS A combination of one-time airborne LiDAR data acquisition and satellite monitoring provides effective forest C mapping in the highly heterogeneous landscapes of the Hawaiian Islands. Our statistical approach yielded key insights into the drivers of ACD variation, and also makes possible future assessments of C storage change, derived on a repeat basis from free satellite data, without the need for additional LiDAR data. Changes in C stocks and densities of oceanic islands can thus be continually assessed in the face of rapid environmental changes such as biological invasions, drought, fire and land use. Such forest monitoring information can be used to promote sustainable forest use and conservation on islands in the future.
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Affiliation(s)
- Gregory P. Asner
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama St, Stanford, CA 94305 USA
| | - Sinan Sousan
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama St, Stanford, CA 94305 USA
| | - David E. Knapp
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama St, Stanford, CA 94305 USA
| | - Paul C. Selmants
- Department of Natural Resources and Environmental Management, University of Hawaii at Manoa, 1910 East–West Rd., Honolulu, HI 96822 USA
| | - Roberta E. Martin
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama St, Stanford, CA 94305 USA
| | - R. Flint Hughes
- USDA Forest Service, Pacific Southwest Research Station, Institute of Pacific Islands Forestry, 60 Nowelo Street, Hilo, HI 96720 USA
| | - Christian P. Giardina
- USDA Forest Service, Pacific Southwest Research Station, Institute of Pacific Islands Forestry, 60 Nowelo Street, Hilo, HI 96720 USA
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Dilling L, Kelsey KC, Fernandez DP, Huang YD, Milford JB, Neff JC. Managing Carbon on Federal Public Lands: Opportunities and Challenges in Southwestern Colorado. ENVIRONMENTAL MANAGEMENT 2016; 58:283-296. [PMID: 27272016 DOI: 10.1007/s00267-016-0714-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Accepted: 05/21/2016] [Indexed: 06/06/2023]
Abstract
Federal lands in the United States have been identified as important areas where forests could be managed to enhance carbon storage and help mitigate climate change. However, there has been little work examining the context for decision making for carbon in a multiple-use public land environment, and how science can support decision making. This case study of the San Juan National Forest and the Bureau of Land Management Tres Rios Field Office in southwestern Colorado examines whether land managers in these offices have adequate tools, information, and management flexibility to practice effective carbon stewardship. To understand how carbon was distributed on the management landscape we added a newly developed carbon map for the SJNF-TRFO area based on Landsat TM texture information (Kelsey and Neff in Remote Sens 6:6407-6422. doi: 10.3390/rs6076407 , 2014). We estimate that only about 22 % of the aboveground carbon in the SJNF-TRFO is in areas designated for active management, whereas about 38 % is in areas with limited management opportunities, and 29 % is in areas where natural processes should dominate. To project the effects of forest management actions on carbon storage, staff of the SJNF are expected to use the Forest Vegetation Simulator (FVS) and extensions. While identifying FVS as the best tool generally available for this purpose, the users and developers we interviewed highlighted the limitations of applying an empirically based model over long time horizons. Future research to improve information on carbon storage should focus on locations and types of vegetation where carbon management is feasible and aligns with other management priorities.
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Affiliation(s)
- Lisa Dilling
- Environmental Studies Program, Center for Science and Technology Policy Research, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, 80309, USA.
- Western Water Assessment, University of Colorado Boulder, Boulder, CO, 80309, USA.
| | - Katharine C Kelsey
- Environmental Studies Program, University of Colorado Boulder, Boulder, CO, 80309, USA
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, AK, 99501, USA
| | - Daniel P Fernandez
- Environmental Studies Program, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Yin D Huang
- Environmental Studies Program, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Jana B Milford
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Jason C Neff
- Environmental Studies Program, University of Colorado Boulder, Boulder, CO, 80309, USA
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Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery. FORESTS 2016. [DOI: 10.3390/f7070138] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Marvin DC, Koh LP, Lynam AJ, Wich S, Davies AB, Krishnamurthy R, Stokes E, Starkey R, Asner GP. Integrating technologies for scalable ecology and conservation. Glob Ecol Conserv 2016. [DOI: 10.1016/j.gecco.2016.07.002] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation. REMOTE SENSING 2016. [DOI: 10.3390/rs8060469] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Woody Biomass Estimation in a Southwestern U.S. Juniper Savanna Using LiDAR-Derived Clumped Tree Segmentation and Existing Allometries. REMOTE SENSING 2016. [DOI: 10.3390/rs8060453] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Synthesizing Global and Local Datasets to Estimate Jurisdictional Forest Carbon Fluxes in Berau, Indonesia. PLoS One 2016; 11:e0146357. [PMID: 26752298 PMCID: PMC4709193 DOI: 10.1371/journal.pone.0146357] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Accepted: 12/16/2015] [Indexed: 11/19/2022] Open
Abstract
Background Forest conservation efforts are increasingly being implemented at the scale of sub-national jurisdictions in order to mitigate global climate change and provide other ecosystem services. We see an urgent need for robust estimates of historic forest carbon emissions at this scale, as the basis for credible measures of climate and other benefits achieved. Despite the arrival of a new generation of global datasets on forest area change and biomass, confusion remains about how to produce credible jurisdictional estimates of forest emissions. We demonstrate a method for estimating the relevant historic forest carbon fluxes within the Regency of Berau in eastern Borneo, Indonesia. Our method integrates best available global and local datasets, and includes a comprehensive analysis of uncertainty at the regency scale. Principal Findings and Significance We find that Berau generated 8.91 ± 1.99 million tonnes of net CO2 emissions per year during 2000–2010. Berau is an early frontier landscape where gross emissions are 12 times higher than gross sequestration. Yet most (85%) of Berau’s original forests are still standing. The majority of net emissions were due to conversion of native forests to unspecified agriculture (43% of total), oil palm (28%), and fiber plantations (9%). Most of the remainder was due to legal commercial selective logging (17%). Our overall uncertainty estimate offers an independent basis for assessing three other estimates for Berau. Two other estimates were above the upper end of our uncertainty range. We emphasize the importance of including an uncertainty range for all parameters of the emissions equation to generate a comprehensive uncertainty estimate–which has not been done before. We believe comprehensive estimates of carbon flux uncertainty are increasingly important as national and international institutions are challenged with comparing alternative estimates and identifying a credible range of historic emissions values.
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Bustamante MMC, Roitman I, Aide TM, Alencar A, Anderson LO, Aragão L, Asner GP, Barlow J, Berenguer E, Chambers J, Costa MH, Fanin T, Ferreira LG, Ferreira J, Keller M, Magnusson WE, Morales-Barquero L, Morton D, Ometto JPHB, Palace M, Peres CA, Silvério D, Trumbore S, Vieira ICG. Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity. GLOBAL CHANGE BIOLOGY 2016; 22:92-109. [PMID: 26390852 DOI: 10.1111/gcb.13087] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2015] [Revised: 07/23/2015] [Accepted: 08/19/2015] [Indexed: 05/05/2023]
Abstract
Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.
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Affiliation(s)
| | - Iris Roitman
- Department of Ecology, University of Brasília, Brasília, DF, CEP 70910900, Brazil
| | - T Mitchell Aide
- Department of Biology, University of Puerto Rico, San Juan, PR, 00931-3360, Puerto Rico
| | - Ane Alencar
- Amazon Environmental Research Institute - IPAM, SHIN CA5 Bl J2 Sala 309, Brasilia, DF, Brazil
| | - Liana O Anderson
- National Center for Monitoring and Early Warning of Natural Disasters - CEMADEN, Parque Tecnológico de São José dos Campos, Estrada Doutor Altino Bondensan, 500, São José dos Campos, SP, 12247-016, Brazil
- Environmental Change Institute, ECI, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
- Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, 12247-016, Brazil
| | - Luiz Aragão
- Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, 12247-016, Brazil
| | - Gregory P Asner
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA, 94305, USA
| | - Jos Barlow
- Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
- Museu Paraense Emilio Goeldi, C.P. 399, Belém, Pará, CEP 66040170, Brasil
| | - Erika Berenguer
- Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
| | - Jeffrey Chambers
- Geography Department, University of California, Berkeley, CA, 94720, USA
| | - Marcos H Costa
- Department of Agricultural Engineering, Federal University of Viçosa, Viçosa, MG, 36570-900, Brazil
| | - Thierry Fanin
- Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Laerte G Ferreira
- Instituto de Estudos Sócio-Ambientais - IESA, Federal University of Goiás, Goiânia, Brazil
| | - Joice Ferreira
- Embrapa Amazonia Oriental, C. Postal 48 66017-970, Belem, PA, Brazil
| | - Michael Keller
- USDA Forest Service, International Institute of Tropical Forestry, San Juan, Puerto Rico
- EMBRAPA Monitoramento por Satélite, Campinas, São Paulo, Brasil
| | - William E Magnusson
- Instituto Nacional de Pesquisas da Amazônia (INPA), Caixa Postal 2223, Manaus, AM, 69067-971, Brazil
| | - Lucia Morales-Barquero
- School of Environment, Natural Resources and Geography, College of Natural Sciences, Bangor University, Bangor, Gwynedd, LL57 2UW, UK
| | - Douglas Morton
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Code 618, Greenbelt, MD, 20771, USA
| | - Jean P H B Ometto
- Earth System Science Centre (CCST), National Institute for Space Research (INPE), Av dos Astronautas, 1758, São José dos Campos, SP, 12227-010, Brazil
| | - Michael Palace
- Earth System Research Center, Institute for the Study of Earth, Oceans, and Space, UNH, Norwich, UK
| | - Carlos A Peres
- School of Environmental Sciences, University of East Anglia, Norwich, NR47TJ, UK
| | - Divino Silvério
- Department of Ecology, University of Brasília, Brasília, DF, CEP 70910900, Brazil
| | | | - Ima C G Vieira
- Museu Paraense Emilio Goeldi, C.P. 399, Belém, Pará, CEP 66040170, Brasil
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LEE JH. Change Analysis of Aboveground Forest Carbon Stocks According to the Land Cover Change Using Multi-Temporal Landsat TM Images and Machine Learning Algorithms. ACTA ACUST UNITED AC 2015. [DOI: 10.11108/kagis.2015.18.4.081] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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50
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Non-Parametric Retrieval of Aboveground Biomass in Siberian Boreal Forests with ALOS PALSAR Interferometric Coherence and Backscatter Intensity. J Imaging 2015. [DOI: 10.3390/jimaging2010001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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