Assessing and Monitoring Forest Degradation in a Deciduous Tropical Forest in Mexico via Remote Sensing Indicators

Open Foris Collect Earth: a remote sensing sampling survey of Azerbaijan to support climate change reporting in the land use, land use change, and forestry

Land use, land use change, and forestry (LULUCF) are critical in climate change mitigation. Producing or collecting activity data for LULUCF is essential in developing national greenhouse gas inventories, national communications, biennial update reports, and nationally determined contributions to meet international commitments under climate change. Collect Earth is a free, publicly accessible software for monitoring dynamics between all land use classes: forestlands, croplands, grasslands, wetlands, settlements, and other lands. Collect Earth supports countries in monitoring the trends in land use and land cover over time by applying a sample-based approach and generating reliable, high-quality, consistent, accurate, transparent, robust, comparable, and complete activity data through augmented visual interpretation for climate change reporting. This article reports forest extent estimates in Azerbaijan, analyzing 7782 0.5-ha sampling units through an augmented visual interpretation of very high spatial and temporal resolution images on the Google Earth platform. The results revealed that in 2016, tree cover existed in 31.9% of total land, equal to 2,751,167 ha and 1,301,188 ha or 15.1% of the total land, with a 5.4% sampling error covered by forests. The estimate is 15 to 25% higher than the previous estimates, equal to 169,418 to 260,888 ha of forest that was never reported in previous studies.

Using Open Foris Collect Earth in Kyrgyzstan to support greenhouse gas inventory in the land use, land use change, and forestry sector

The Kyrgyz Republic (Kyrgyzstan) is one of the countries most vulnerable to the adverse effects of climate change in Central Asia. The land use, land use change, and forestry (LULUCF) sector is critical in climate change mitigation in Kyrgyzstan and is integral to national greenhouse gas (GHG) inventories. However, consistent, complete, and updated activity data is required for the LULUCF sector to develop a transparent GHG inventory. Collect Earth (CE), developed by the Food and Agriculture Organization of the United Nations (FAO), is a free, user-friendly, and open-source tool for collecting activity data for the LULUCF sector. CE assists countries in developing GHG inventories by providing consistent and complete land representation. This article reports an estimate of land use and land-use change dynamics in Kyrgyzstan, based on analyzing 13,414 1-hectare (ha) sampling units through an augmented visual interpretation approach using satellite imagery at the very high spatial and temporal resolution available through the Google Earth platform. The results show that in 2019, forests covered 1.36 million ha or 6.83% of the total land with a 6.23% uncertainty. This estimate was 5 to 16% higher than previous estimates, detecting an additional 63,024 to 188,164 ha of forestland that had not been reported previously. The new estimates suggest an average increase of 10.4% in the current forestlands of Kyrgyzstan.

The role of protected areas co-management in enhancing resistance and resilience of deciduous forest ecosystem to extreme climatic events in Bangladesh

Due to ongoing and projected climate change as well as increasing anthropogenic disturbances, the tropical deciduous forest has been experiencing a decline in its biomass and productivity. To mitigate this adverse effect, many tropical countries have adopted forest co-management engaging local communities. However, the effects of co-management on the resistance and resilience of forest ecosystems to extreme climatic events have rarely been tested. The present study investigates the effects of co-management on resistance and resilience to extreme climatic events in two major tropical deciduous forest protected areas of Bangladesh, namely Madhupur National Park (MNP) and Bhawal National Park (BNP), through remotely sensed satellite data. We used the Google Earth Engine platform to access the Landsat images from 1990 to 2020 for a comprehensive assessment of the forest cover condition under two major management regimes (i.e., traditional and co-management). We find that co-management slows down the rate of forest destruction, where the rate of forest destruction was 108 ha year^-1 in MNP and 121 ha year^-1 in BNP during the year 1990-2008 under traditional forest management system. Under the co-management regime, forest cover increased by 19 ha year^-1 and 41 ha year^-1 from 2009 to 2020 respectively in MNP and BNP. Our study finds a highly significant correlation between rainfall (p < 0.001) and forest health, although co-management had poor impacts on forest resistance and resilience in case of extreme climatic events, such as drought and heavy rainfall. We find, no significant impacts of co-management on resistance and resilience to drought in MNP, and on resistance and resilience to heavy rainfall in MNP and BNP. In BNP, the impacts of co-management on resistance (p < 0.05) and resilience (p < 0.01) of forest to drought were highly significant. Forest co-management although have the potentials to reduce the deforestation rate by mitigating anthropogenic disturbances, its capacity to tackle the adverse impact of climate change was limited in our study. An adaptive co-management model, therefore, is crucial for mainstreaming the adverse effect of climate change on the tropical deciduous forest to harness the maximum potential of community participation in forest resources management.

How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest

Time series analysis has gained popularity in forest disturbance monitoring thanks to the availability of satellite and airborne remote sensing images and the development of different time series methods for change detection. Previous research has focused on time series data noise reduction, the magnitude of breakpoints, and accuracy assessment; however, few have looked in detail at how the trend and seasonal model components contribute to disturbance detection in different forest types. Here, we use Landsat time series images spanning 1994–2018 to map forest disturbance in a western Pacific area of Mexico, where both temperate and tropical dry forests have been subject to severe deforestation and forest degradation processes. Since these two forest types have distinct seasonal characteristics, we investigate how trend and seasonal model components, such as the goodness-of-fit (R2), magnitude of change, amplitude, and model length in a stable historical period, affect forest disturbance detection. We applied the Breaks For Additive Season and Trend Monitor (BFAST) algorithm and after accuracy assessment by stratified random sample points, and we obtained 68% and 86% of user accuracy and 75.6% and 86% of producer’s accuracy in disturbance detection, in tropical dry forests and temperate forests, respectively. We extracted the noncorrelated trend and seasonal model components R2, magnitude, amplitude, length of the stable historical period, and percentage of pixels with NA and tested their effects on disturbance detection employing forest-type specific logistic regression. Our results showed that, for all forests combined, the amplitude and stable historical period length contributed to disturbance detection. While for tropical dry forest alone, amplitude was the main predictor, and for the temperate forest alone, the stable historical period length contributed most to the prediction, although it was not statistically significant. These findings provide insights for improving the results of forest disturbance detection in different forest types.

Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

Projected land-use changes in the Shared Socioeconomic Pathways: Insights and implications

The conceptualization of the Shared Socioeconomic Pathways (SSPs) framework represented a major leap in scenario development in the context of global environmental change and sustainability, providing significant advances from the previous scenario frameworks-especially the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios. It is highly likely that the SSP concept, along with its scenario narratives and their respective results, including land-use change projections, will play a substantial role in the forthcoming Sixth Assessment Report by the IPCC. Here, we offer some insights that could make the SSPs' projected future changes in global land use more comprehensive and also help improve the interpretability of such projections. For example, instead of focusing on the quantity of each land-use class at various time points which results only in a net change when change is detected between time points, we recommend that the projected gross gains and gross losses in each land-use class across all scenarios should also be considered. Overall, the insights presented could also help pave the way for stronger collaboration between the SSP-climate science community and the land system science community; such collaboration is much needed in addressing the challenges of global environmental change towards a climate-resilient sustainable development pathway.

Mixed Effectiveness of REDD+ Subnational Initiatives after 10 Years of Interventions on the Yucatan Peninsula, Mexico

Since 2010, the Reducing Emissions from Deforestation and Degradation (REDD+) mechanism has been implemented in Mexico’s Yucatan Peninsula, a biodiversity hotspot with persistent deforestation problems. We apply the before-after-control-intervention approach and quasi-experimental methods to evaluate the effectiveness of REDD+ interventions in reducing deforestation at municipal (meso) and community (micro) scales. Difference-in-differences regression and propensity score matching did not show an overall reduction in forest cover loss from REDD+ projects at both scales. However, Synthetic Control Method (SCM) analyses demonstrated mixed REDD+ effectiveness among intervened municipalities and communities. Funding agencies and number of REDD+ projects intervening in a municipality or community did not appear to affect REDD+ outcomes. However, cattle production and commercial agriculture land uses tended to impede REDD+ effectiveness. Cases of communities with important forestry enterprises exemplified reduced forest cover loss but not when cattle production was present. Communities and municipalities with negative REDD+ outcomes were notable along the southern region bordering Guatemala and Belize, a remote forest frontier fraught with illegal activities and socio-environmental conflicts. We hypothesize that strengthening community governance and organizational capacity results in REDD+ effectiveness. The observed successes and problems in intervened communities deserve closer examination for REDD+ future planning and development of strategies on the Yucatan Peninsula.

Tropical Cyclone Landfall Frequency and Large-Scale Environmental Impacts along Karstic Coastal Regions (Yucatan Peninsula, Mexico)

Tropical cyclones (TCs) are natural systems that develop over ocean basins and are key components of the atmospheric activity during the warm season. However, there are still knowledge gaps about the combined positive and negative TC impacts on the structure and function of coastal socio-ecosystems. Using remote sensing tools, we analyzed the frequency, trajectory, and intensity of 1894 TCs from 1851–2019 to identify vulnerable “hotspots” across the Yucatan Peninsula (YP), Mexico. A total of 151 events hit the YP, with 96% of landings on the eastern coast. We focused on three major hurricanes (Emily and Wilma, 2005; Dean, 2007) and one tropical storm (Stan, 2005) to determine the impacts on cumulative precipitation, vegetation change, and coastal phytoplankton (Chl-a) distribution across the YP. Despite a short inland incursion, Wilma’s environmental damage was coupled to strong winds (157–241 km/h), slow motion (4–9 km/h), and heavy precipitation (up to 770 mm). Because of an extensive footprint, Wilma caused more vegetation damage (29%) than Dean (20%), Emily (7%), and Stan (2%). All TCs caused a Chl-a increase associated to submarine discharge and upwelling off the peninsula coastlines. Disaster risk along the coast underscores negative economic impacts and positive ecological benefits at the regional scale.

Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data

Current estimates of CO2 emissions from forest degradation are generally based on insufficient information and are characterized by high uncertainty, while a global definition of ‘forest degradation’ is currently being discussed in the scientific arena. This study proposes an automated approach to monitor degradation using a Landsat time series. The methodology was developed using the Google Earth Engine (GEE) and applied in a pine forest area of the Dominican Republic. Land cover change mapping was conducted using the random forest (RF) algorithm and resulted in a cumulative overall accuracy of 92.8%. Forest degradation was mapped with a 70.7% user accuracy and a 91.3% producer accuracy. Estimates of the degraded area had a margin of error of 10.8%. A number of 344 Landsat collections, corresponding to the period from 1990 to 2018, were used in the analysis. Additionally, 51 sample plots from a forest inventory were used. The carbon stocks and emissions from forest degradation were estimated using the RF algorithm with an R2 of 0.78. GEE proved to be an appropriate tool to monitor the degradation of tropical forests, and the methodology developed herein is a robust, reliable, and replicable tool that could be used to estimate forest degradation and improve monitoring, reporting, and verification (MRV) systems under the reducing emissions from deforestation and forest degradation (REDD+) mechanism.

An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-Resolution, Low-Cost Satellite Images

Advanced monitoring and mapping of forest areas using the latest technological advances in satellite imagery is an alternative solution for sustainable forest management compared to conventional ground measurements. Remote sensing products have been a key source of information and cost-effective options for monitoring changes in harvested areas. Despite recent advances in satellite technology with a broad variety of spectral and temporal resolutions, monitoring the areal extent of harvested forest areas in managed forests is still a challenge, primarily due to the highly dynamic spatiotemporal patterns of logging activities. Our goal was to introduce a plot-based method for monitoring harvested forest areas from very high-resolution (VHR), low-cost satellite images. Our method encompassed two data categories, which included vegetation indices (VIs) and texture analysis (TA). Each group of data was used to model the amount of harvested volume both independently and in combination. Our results indicated that the composition of all spectral bands can improve the accuracy of all models of average volume by 23.52 RMSE reduction and total volume by 33.57 RMSE reduction. This method demonstrated that monitoring and extrapolation of the calculated relation and results from smaller forested areas could be applied as an automatic remote-based supervised monitoring method over larger forest areas.

Forest Degradation Assessment Based on Trend Analysis of MODIS-Leaf Area Index: A Case Study in Mexico

: Assessing forest degradation has been a challenging task due to the generally slow-changing nature of the process, which demands long periods of observation and high frequency of records. This research contributes to efforts aimed at detecting forest degradation by analyzing the trend component of the time series of Leaf Area Index (LAI) collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) over Central Mexico from 2002 to 2017. The analysis of the trend component is proposed to overcome the challenge of identifying very subtle and gradual changes that can be undetected if only the raw time series is examined. Additionally, the use of LAI as an alternative to other widely used indexes (e.g., Normalize Difference Vegetation Index and Enhanced Vegetation Index) facilitates consideration of the structural changes evident from degradation though not necessarily observable with spectral indices. Overall, results indicate that 52% of the study area has experienced positive trends of vegetation change (i.e., increasing LAI), 37% has remained unchanged, and 11% exhibits some level of forest degradation. Particularly, the algorithm estimated that 0.6% (385 km2) is highly degraded, 5.3% (3406 km2) moderately degraded, and 5.1% (3245 km2) slightly degraded. Most of the moderate and highly degraded areas are distributed over the east side of the study area and evergreen broadleaf appears to be the most affected forest type. Model validation resulted an accuracy of 63%. Some actions to improve this accuracy are suggested, but also a different approach to validate this type of study is suggested as an area of opportunity for future research.

Current and Potential Spatial Distribution of Six Endangered Pine Species of Mexico: Towards a Conservation Strategy

Mexico is home to the highest species diversity of pines: 46 species out of 113 reported around the world. Within the great diversity of pines in Mexico, Pinus culminicola Andresen et Beaman, P. jaliscana Perez de la Rosa, P. maximartinenzii Rzed., P. nelsonii Shaw, P. pinceana Gordon, and P. rzedowskii Madrigal et M. Caball. are six catalogued as threatened or endangered due to their restricted distribution and low population density. Therefore, they are of special interest for forest conservation purposes. In this paper, we aim to provide up-to-date information on the spatial distribution of these six pine species according to different historical registers coming from different herbaria distributed around the country by using spatial modeling. Therefore, we recovered historical observations of the natural distribution of each species and modelled suitable areas of distribution according to environmental requirements. Finally, we evaluated the distributions by contrasting changes of vegetation in the period 1991–2016. The results highlight areas of distribution for each pine species in the northeast, west, and central parts of Mexico. The results of this study are intended to be the basis of in situ and ex situ conservation strategies for the endangered Mexican pines.