The contribution of trees outside of forests to landscape carbon and climate change mitigation in West Africa

While closed canopy forests have been an important focal point for land cover change monitoring and climate change mitigation, less consideration has been given to methods for large scale measurements of trees outside of forests. Trees outside of forests are an important but often overlooked natural resource throughout sub-Saharan Africa, providing benefits for livelihoods as well as climate change mitigation and adaptation. In this study, the development of an individual tree cover map using very high-resolution remote sensing and a comparison with a new automated machine learning mapping product revealed an important contribution of trees outside of forests to landscape tree cover and carbon stocks in a region where trees outside of forests are important components of livelihood systems. Here, we test and demonstrate the use of allometric scaling from remote sensing crown area to provide estimates of landscape-scale carbon stocks. Prominent biomass and carbon maps from global-scale remote sensing greatly underestimate the “invisible” carbon in these sparse tree-based systems. The measurement of tree cover and carbon in these landscapes has important application in climate change mitigation and adaptation policies.The Land Cover and Land Use Change (LCLUC) Program at the National Aeronautics and Space Administration, USA. The APC was funded by NASA and Michigan State University.https://www.mdpi.com/journal/forestsam202

The Contribution of Trees Outside of Forests to Landscape Carbon and Climate Change Mitigation in West Africa

While closed canopy forests have been an important focal point for land cover change monitoring and climate change mitigation, less consideration has been given to methods for large scale measurements of trees outside of forests. Trees outside of forests are an important but often overlooked natural resource throughout sub-Saharan Africa, providing benefits for livelihoods as well as climate change mitigation and adaptation. In this study, the development of an individual tree cover map using very high-resolution remote sensing and a comparison with a new automated machine learning mapping product revealed an important contribution of trees outside of forests to landscape tree cover and carbon stocks in a region where trees outside of forests are important components of livelihood systems. Here, we test and demonstrate the use of allometric scaling from remote sensing crown area to provide estimates of landscape-scale carbon stocks. Prominent biomass and carbon maps from global-scale remote sensing greatly underestimate the “invisible” carbon in these sparse tree-based systems. The measurement of tree cover and carbon in these landscapes has important application in climate change mitigation and adaptation policies

Allometry for Biomass Estimation in Jatropha Trees Planted as Boundary Hedge in Farmers ’ Fields

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Regional assessment of lake water clarity using satellite remote sensing

Lake water clarity as measured by Secchi disk transparency (SDT) is a cost-effective measure of water quality. However, in regions where there are thousands of lakes, sampling even a small proportion of those lakes for SDT year after year is cost prohibitive. Remote sensing has the potential to be a powerful tool for assessing lake clarity over large spatial scales. The overall objective of our study was to examine whether Landsat-7 ETM+ could be used to measure water clarity across a large range of lakes. Our specific objectives were to: 1) develop a regression model to estimate SDT from Landsat data calibrated using 93 lakes in Michigan, U.S.A., and to 2) examine how the distribution of SDT across the 93 calibration lakes influenced the model. Our calibration dataset included a large number of lakes with a wide range of SDT values that captured the summer statewide distribution of SDT values in Michigan. Our regression model had a much lower r2 value than previously published studies conducted on smaller datasets. To examine the importance of the distribution of calibration data, we simulated a calibration dataset with a different SDT distribution by sub-sampling the original dataset to match the distribution of previous studies. The sub-sampled dataset had a much higher percentage of lakes with shallow water clarity, and the resulting regression model had a much higher r2 value than our original model. Our study shows that the use of Landsat to measure water clarity is sensitive to the distribution of water clarity used in the calibration set

Assessment of tropical forest degradation by selective logging and fire using Landsat imagery

Many studies have assessed the process of forest degradation in the Brazilian Amazon using remote sensing approaches to estimate the extent and impact by selective logging and forest fires on tropical rain forest. However, only a few have estimated the combined impacts of those anthropogenic activities. We conducted a detailed analysis of selective logging and forest fire impacts on natural forests in the southern Brazilian Amazon state of Mato Grosso, one of the key logging centers in the country. To achieve this goal a 13-year series of annual Landsat images (1992-2004) was used to test different remote sensing techniques for measuring the extent of selective logging and forest fires, and to estimate their impact and interaction with other land use types occurring in the study region. Forest canopy regeneration following these disturbances was also assessed. Field measurements and visual observations were conducted to validate remote sensing techniques. Our results indicated that the Modified Soil Adjusted Vegetation Index aerosol free (MSAVI(af)) is a reliable estimator of fractional coverage under both clear sky and under smoky conditions in this study region. During the period of analysis, selective logging was responsible for disturbing the largest proportion (31%) of natural forest in the study area, immediately followed by deforestation (29%). Altogether, forest disturbances by selective logging and forest fires affected approximately 40% of the study site area. Once disturbed by selective logging activities, forests became more susceptible to fire in the study site. However, our results showed that fires may also occur in undisturbed forests. This indicates that there are further factors that may increase forest fire susceptibility in the study area. Those factors need to be better understood. Although selective logging affected the largest amount of natural forest in the study period, 35% and 28% of the observed losses of forest canopy cover were due to forest fire and selective logging combined and to forest fire only, respectively. Moreover, forest areas degraded by selective logging and forest fire is an addition to outright deforestation estimates and has yet to be accounted for by land use and land cover change assessments in tropical regions. Assuming that this observed trend of land use and land cover conversion continues, we predict that there will be no undisturbed forests remaining by 2011 in this study site. Finally, we estimated that 70% of the total forest area disturbed by logging and fire had sufficiently recovered to become undetectable using satellite data in 2004. (C) 2010 Elsevier B.V. All rights reserved

Mapping deforestation and secondary growth in Rondonia, Brazil, using imaging radar and thematic mapper data

Excellent data on deforestation have been obtained in the tropics with the use of high-resolution optical sensors. Yet, several problems remain. Cloud cover creates data gaps that limit the possibility of complete and frequent assessments, and secondary growth is not well characterized. Active microwave sensors could complement these sensors because they operate independently of cloud cover and smoke and can detect differences in woody biomass and forest structure associated with various stages of forest clearing and regrowth. An example of comparison and synergy between the two techniques is discussed here. Polarimetric, C- (5.6 cm) and L-band (24 cm) frequency, radar data gathered in October 1994 by NASA's Spaceborne Imaging Radar C, on a test site southeast of the city of Porto Velho, in the state of Rondonia, Brazil, are analyzed in conjunction with one 1993 Landsat Thematic Mapper (TM) scene, a 9-year time series of Satellite pour l'observation de la Terre (SPOT) XS data, two Japan Earth Resources Satellite (JERS-1) radar images from 1994 and 1995, and a field visit conducted in 1995. The C-band radar data are found to be of limited utility for mapping deforestation. At L-band, multiple polarizations are required to obtain a reliable classification. The single polarization, L-band, single date, JERS-1 data underestimate the extent of deforestation, especially during the wet season. With multiple polarizations, six classes of land cover, including one level of regrowth, are mapped with 90% accuracy, but intermediate regrowth 5-8 years of age is not well separated from the forest. The Landsat TM data identify deforested areas better but provide less information on residual woody biomass levels. Combining the two classifications, seven classes of land cover including two levels of regrowth are mapped with 93% accuracy. The results show that the deforestation rate for 1994 was 1.7%. Large variations in residual woody biomass are detected among new clearings. Half of the total deforested land is in some stage of regrowth but most of it is less than 5 years old. Secondary growth is therefore a significant form or land use that is recleared quickly