2
|
Analysis of Canopy Gaps of Coastal Broadleaf Forest Plantations in Northeast Taiwan Using UAV Lidar and the Weibull Distribution. REMOTE SENSING 2022. [DOI: 10.3390/rs14030667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Canopy gaps are pivotal for monitoring forest ecosystem dynamics. Conventional field methods are time-consuming and labor intensive, making them impractical for regional mapping and systematic monitoring. Gaps may be delineated using airborne lidar or aerial photographs acquired from a manned aircraft. However, high cost in data acquisition and low flexibility in flight logistics significantly reduce the accessibility of the approaches. To address these issues, this study utilized miniature light detection and ranging (lidar) onboard an unmanned aircraft vehicle (UAVlidar) to map forest canopy gaps of young and mature broadleaf forest plantations along the coast of northeastern Taiwan. This study also used UAV photographs (UAVphoto) for the same task for comparison purposes. The canopy height models were derived from UAVlidar and UAVphoto with the availability of a digital terrain model from UAVlidar. Canopy gap distributions of the forests were modeled with the power-law zeta and Weibull distributions. The performance of UAVlidar was found to be superior to UAVphoto in delineating the gap distribution through ground observation, mainly due to lidar’s ability to detect small canopy gaps. There were apparent differences of the power-law zeta distributions for the young and mature forest stands with the exponents λ of 1.36 (1.45) and 1.71 (1.61) for UAVlidar and UAVphoto, respectively, suggesting that larger canopy gaps were present within the younger stands. The canopy layer of mature forest stands was homogeneous, and the size distributions of both sensors and methods were insensitive to the spatial extent of the monitored area. Contrarily, the young forests were heterogeneous, but only UAVlidar with the Weibull distribution responded to the change of spatial extent. This study demonstrates that using the Weibull distribution to analyze canopy gap from high-spatial resolution UAVlidar may provide detailed information of regional forest canopy of coastal broadleaf forests.
Collapse
|
3
|
Pontes-Lopes A, Silva CVJ, Barlow J, Rincón LM, Campanharo WA, Nunes CA, de Almeida CT, Silva Júnior CHL, Cassol HLG, Dalagnol R, Stark SC, Graça PMLA, Aragão LEOC. Drought-driven wildfire impacts on structure and dynamics in a wet Central Amazonian forest. Proc Biol Sci 2021; 288:20210094. [PMID: 34004131 PMCID: PMC8131120 DOI: 10.1098/rspb.2021.0094] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/23/2021] [Indexed: 11/12/2022] Open
Abstract
While the climate and human-induced forest degradation is increasing in the Amazon, fire impacts on forest dynamics remain understudied in the wetter regions of the basin, which are susceptible to large wildfires only during extreme droughts. To address this gap, we installed burned and unburned plots immediately after a wildfire in the northern Purus-Madeira (Central Amazon) during the 2015 El-Niño. We measured all individuals with diameter of 10 cm or more at breast height and conducted recensuses to track the demographic drivers of biomass change over 3 years. We also assessed how stem-level growth and mortality were influenced by fire intensity (proxied by char height) and tree morphological traits (size and wood density). Overall, the burned forest lost 27.3% of stem density and 12.8% of biomass, concentrated in small and medium trees. Mortality drove these losses in the first 2 years and recruitment decreased in the third year. The fire increased growth in lower wood density and larger sized trees, while char height had transitory strong effects increasing tree mortality. Our findings suggest that fire impacts are weaker in the wetter Amazon. Here, trees of greater sizes and higher wood densities may confer a margin of fire resistance; however, this may not extend to higher intensity fires arising from climate change.
Collapse
Affiliation(s)
- Aline Pontes-Lopes
- Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
| | - Camila V. J. Silva
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
- Amazon Environmental Research Institute (IPAM), Brasília 71503-505, Brazil
| | - Jos Barlow
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
| | - Lorena M. Rincón
- National Institute for Research in Amazonia (INPA), Manaus 69067-375, Brazil
| | - Wesley A. Campanharo
- Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
| | - Cássio A. Nunes
- Department of Ecology and Conservation, Federal University of Lavras (UFLA), Lavras 37200-000, Brazil
| | - Catherine T. de Almeida
- Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
- Department of Forest Sciences, Luiz de Queiroz College of Agriculture, University of São Paulo (USP/ESALQ), Piracicaba 13418-900, Brazil
| | - Celso H. L. Silva Júnior
- Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
- Department of Agricultural Engineering, State University of Maranhão (UEMA), São Luís 65055-310, Brazil
| | - Henrique L. G. Cassol
- Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
| | - Ricardo Dalagnol
- Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
| | - Scott C. Stark
- Department of Forestry, Michigan State University, East Lansing, MI 48824, USA
| | | | - Luiz E. O. C. Aragão
- Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
- College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK
| |
Collapse
|
4
|
Nunes MH, Jucker T, Riutta T, Svátek M, Kvasnica J, Rejžek M, Matula R, Majalap N, Ewers RM, Swinfield T, Valbuena R, Vaughn NR, Asner GP, Coomes DA. Recovery of logged forest fragments in a human-modified tropical landscape during the 2015-16 El Niño. Nat Commun 2021; 12:1526. [PMID: 33750781 PMCID: PMC7943823 DOI: 10.1038/s41467-020-20811-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 12/02/2020] [Indexed: 01/29/2023] Open
Abstract
The past 40 years in Southeast Asia have seen about 50% of lowland rainforests converted to oil palm and other plantations, and much of the remaining forest heavily logged. Little is known about how fragmentation influences recovery and whether climate change will hamper restoration. Here, we use repeat airborne LiDAR surveys spanning the hot and dry 2015-16 El Niño Southern Oscillation event to measure canopy height growth across 3,300 ha of regenerating tropical forests spanning a logging intensity gradient in Malaysian Borneo. We show that the drought led to increased leaf shedding and branch fall. Short forest, regenerating after heavy logging, continued to grow despite higher evaporative demand, except when it was located close to oil palm plantations. Edge effects from the plantations extended over 300 metres into the forests. Forest growth on hilltops and slopes was particularly impacted by the combination of fragmentation and drought, but even riparian forests located within 40 m of oil palm plantations lost canopy height during the drought. Our results suggest that small patches of logged forest within plantation landscapes will be slow to recover, particularly as ENSO events are becoming more frequent. It is unclear whether tropical forest fragments within plantation landscapes are resilient to drought. Here the authors analyse LiDAR and ground-based data from the 2015-16 El Niño event across a logging intensity gradient in Borneo. Although regenerating forests continued to grow, canopy height near oil palm plantations decreased, and a strong edge effect extended up to at least 300 m away.
Collapse
Affiliation(s)
- Matheus Henrique Nunes
- Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, CB2 3QZ, UK. .,Department of Geosciences and Geography, University of Helsinki, Helsinki, 00014, Finland.
| | - Tommaso Jucker
- Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, CB2 3QZ, UK.,School of Biological Sciences, University of Bristol, Bristol, BS8 1TH, UK
| | - Terhi Riutta
- Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire, SL5 7PY, UK.,School of Geography and the Environment, Environmental Change Institute, University of Oxford, Oxford, OX1 3QY, UK
| | - Martin Svátek
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood Technology, Mendel University in Brno, 613 00, Brno, Czech Republic
| | - Jakub Kvasnica
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood Technology, Mendel University in Brno, 613 00, Brno, Czech Republic
| | - Martin Rejžek
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood Technology, Mendel University in Brno, 613 00, Brno, Czech Republic
| | - Radim Matula
- Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Prague, 165 00, Czech Republic
| | | | - Robert M Ewers
- Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire, SL5 7PY, UK
| | - Tom Swinfield
- Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, CB2 3QZ, UK
| | - Rubén Valbuena
- Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, CB2 3QZ, UK.,School of Natural Sciences, Bangor University, Gwynedd, LL57 2UW, UK
| | - Nicholas R Vaughn
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe AZ and Hilo, Tempe, HI, USA
| | - Gregory P Asner
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe AZ and Hilo, Tempe, HI, USA
| | - David A Coomes
- Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, CB2 3QZ, UK.
| |
Collapse
|
5
|
Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. REMOTE SENSING 2020. [DOI: 10.3390/rs12172685] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1.
Collapse
|