Measurement of deforestation in the Brazilian Amazon using satellite remote sensing

A clear understanding of the role of the biota in the global carbon cycle is limited by an absence of accurate measurements of deforestation rates in the tropics. This study measures the rate and extent of deforestation in the Brazilian Amazon, a tropical forest biome approximately 5 $\times$ 10\sp6 km\sp2 in size and the largest extant tropical forest biome in the world. The study focuses on remote sensing measurements of deforestation rates and the area of secondary vegetation, but also utilizes tabular data to document deforestation when satellite data are not available. The analysis concludes: (1) Regression analysis of SPOT, TM, and AVHRR measurements suggests that the AVHRR will greatly overestimate deforestation and be highly variable; the use of a brightness temperature threshold is highly sensitive and unreliable. The upward bias of AVHRR is a function of the density of deforestation. (2) An accurate measurement of deforestation requires Landsat TM data, and can be accomplished using low cost visual interpretation of photographic products at 1:250,000 scale, with accuracies within 10% of that obtained using digital image processing techniques employing supervised statistical classifiers. (3) Secondary growth in the Brazilian Amazon represents a large fraction of the total deforested area, and the abandonment of agricultural land is an important land cover transition. Abandonment rates were 70-83% of clearing rates from primary forests. At any one point in time, approximately 30% of the deforested area is in some stage of abandonment, and quite likely nearly all deforested land becomes abandoned after approximately 5 years. (4) Previous estimates of the total area deforested in the Amazon, as well as the rate of deforestation, have been too high by as much as 4-fold. A complete assessment of the entire Legal Amazon using over 200 Landsat images measures 251 $\times$ 10\sp3 km\sp2 deforestation as of 1988, or approximately 6% of the closed forests of the region. The average annual rate of deforestation between 1978 and 1988 was 18 $\times$ 10\sp3 km\sp2 yr\sp{-1}. These findings are important to carbon cycle research. They suggest the estimates of carbon emissions from the Amazon for the late 1980s have been too high, since the area of regrowth is large and rates of deforestation are lower than previously believed

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

Achieving mitigation and adaptation to climate change through sustainable agroforestry practices in Africa

Peer reviewedPublisher PD

Dendrochronological potential and productivity of tropical tree species in Western Kenya

This study focuses on tropical tree growth rates in Western Kenya. The dendrochronological potential of each study species was determined by visual examination of rings, and then cumulative growth trajectories for diameter were synthesized for species of sufficient sample size (n ≥ 3), based on ring-width chronologies. The 14 tree species considered were: Acacia mearnsii, Bridelia micrantha, Combretum molle, Croton macrostachyus, Cupressus lustianica, Eucalyptus camaldulensis, Eucalyptus grandis, Eucalyptus saligna, Grevillea robusta, Mangifera indica, Markhamia lutea, Persia Americana, Syzygium cumini, and Trilepisium madagascariensis. The species with the highest dendrochronological potential included Acacia mearnsii, Cupressus lusitanica, the Eucalyptus spp. and Mangifera indica, which are all non-native species that successfully crossdated. The results also indicated that the species with highest dendrochronological potential had strong radial growth synchrony, which was reflected in high inter-tree correlation and (or) high growth variance explained by the first principal component axis. Furthermore, A. mearnsii and E. camaldulensis were sensitive to annual precipitation and moisture index. The species with the lowest dendrochronological potential were Grevillea robusta and Markhamia lutea. In terms of productivity, the three fastest growing species in the study, based on annual diameter increment, were Eucalyptus camaldulensis, Eucalyptus grandis, and Acacia mearnsii. This study also has great potential to extrapolate historical patterns of diameter growth to understanding annual aboveground biomass and carbon dynamics in Western Kenya.This item is part of the Tree-Ring Research (formerly Tree-Ring Bulletin) archive. For more information about this peer-reviewed scholarly journal, please email the Editor of Tree-Ring Research at [email protected]

Influence of Climate Variation on Growth of Tropical Tree Species in Western Kenya

Aims: Growth-climate relationships were examined in 7 tropical tree species growing in the Yala river basin of western Kenya: Acacia mearnsii, Cupressus lusitanica, Eucalyptus camaldulensis, Eucalyptus grandis, Eucalytus saligna, Mangifera indica, and Markhamia lutea. Methodology: Standardized basal area increments were correlated with monthly and seasonal (3 month periods) climate variables (precipitation, mean temperature, Climate Moisture Index) obtained from nearby meteorological stations. Results: A majority of the tree species (M. indica, C. lusitanica, E. camaldulensis, and E. saligna) showed positive correlations with monthly and seasonal precipitation and moisture index during periods of the long and short rainy seasons.  This study also revealed significant correlations between monthly and seasonal temperature data and radial growth of M. indica, M. lutea and E. grandis.  Growth of M. lutea was negatively affected by cool growing season conditions while M. indica and E. grandis experienced high temperature stress.  Conclusion: Associations between radial growth of tropical tree species and temperature are generally rare in warm tropical regions, and for some of the species examined in this study that are non-native (i.e., M. indica and E. grandis), strongly suggests that they may be growing outside the optimal temperature conditions of their native geographical range

Sampling global deforestation databases: The role of persistence

Amazon, deforestation, persistence, random sampling, remote sensing,

Can Input Subsidy Programs Promote Climate Smart Agriculture in Africa?

Climate smart agriculture (CSA) has emerged as an approach to enhance the resilience of farming systems to the effects of climate change. CSA is defined by three principle objectives: 1) sustainably increasing agricultural productivity and incomes; 2) adapting and building resilience to climate change; and 3) reducing and/or removing greenhouse gases emissions, where possible

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

Can Input Subsidy Programs Promote Climate Smart Agriculture in Africa?

Climate smart agriculture (CSA) has emerged as an approach to enhance the resilience of farming systems to the effects of climate change. CSA is defined by three principle objectives: 1) sustainably increasing agricultural productivity and incomes; 2) adapting and building resilience to climate change; and 3) reducing and/or removing greenhouse gases emissions, where possible. In Africa there is particular interest in identifying strategies to encourage farmers to adopt practices and technologies that enable their farms to be more resilient and productive, while at the same time identifying system-wide collective action to promote a wide range of ex ante risk management activities and ex post coping strategies