scholarly journals Integrated Assessment of Forest Cover Change and Above-Ground Carbon Stock in Pugu and Kazimzumbwi Forest Reserves, Tanzania

2013 ◽  
Vol 02 (01) ◽  
pp. 1-9 ◽  
Author(s):  
Japhet J. Kashaigili ◽  
Makarius V. Mdemu ◽  
Augustino R. Nduganda ◽  
Boniface P. Mbilinyi
Keyword(s):  
Integrated Assessment ◽  
Carbon Stock ◽  
Forest Cover ◽  
Forest Cover Change ◽  
Forest Reserves

scholarly journals Implementation of REDD+ in sub-Saharan Africa: state of knowledge, challenges and opportunities

2011 ◽  
Vol 16 (4) ◽  
pp. 381-404 ◽  
Author(s):  
MATIEU HENRY ◽  
DANAE MANIATIS ◽  
VINCENT GITZ ◽  
DAVID HUBERMAN ◽  
RICCARDO VALENTINI
Keyword(s):  
Land Use ◽  
Carbon Stock ◽  
Forest Cover ◽  
Carbon Sink ◽  
Forest Degradation ◽  
Forest Carbon ◽  
Sub Saharan Africa ◽  
Forest Cover Change ◽  
Forest Carbon Stock ◽  
Sub Saharan

ABSTRACTDeforestation and forest degradation represent an important part of global CO2 emissions. The identification of the multiple drivers of land-use change, past and present forest cover change and associated carbon budget, and the presence of locally adapted systems to allow for proper monitoring are particularly lacking in sub-Saharan Africa (SSA). Any incentive system to reduce emissions from deforestation and forest degradation (REDD+) will have to overcome those limits. This paper reviews the main challenges to implementing effective REDD+ mitigation activities in SSA. We estimate that SSA is currently a net carbon sink of approximately 319 TgCO2 yr−1. Forest degradation and deforestation put the forest carbon stock at risk (mean forest carbon stock is 57,679 TgC). Our results highlight the importance of looking beyond the forest sector to ensure that REDD+ efforts are aligned with agricultural and land-use policies.

scholarly journals Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal

2021 ◽  
Vol 13 (11) ◽  
pp. 2131
Author(s):  
Jamon Van Den Hoek ◽  
Alexander C. Smith ◽  
Kaspar Hurni ◽  
Sumeet Saksena ◽  
Jefferson Fox
Keyword(s):  
Remote Sensing ◽  
Geographic Distribution ◽  
Classification Accuracy ◽  
Forest Cover ◽  
Forest Growth ◽  
Forest Cover Change ◽  
Scale Evaluation ◽  
Beneficial Effects ◽  
Affect Detection

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016) TIC. TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.

scholarly journals Synthesizing published knowledge of boreal forest cover change for large-scale landscape dynamics modelling

2003 ◽  
Vol 79 (1) ◽  
pp. 132-146 ◽  
Author(s):  
Dennis Yemshanov ◽  
Ajith H Perera
Keyword(s):  
Boreal Forest ◽  
Large Scale ◽  
Forest Canopy ◽  
Forest Cover ◽  
Forest Succession ◽  
Simulation Models ◽  
Limiting Factors ◽  
Sources Of Information ◽  
Forest Cover Change ◽  
Discrete State

We reviewed the published knowledge on forest succession in the North American boreal biome for its applicability in modelling forest cover change over large extents. At broader scales, forest succession can be viewed as forest cover change over time. Quantitative case studies of forest succession in peer-reviewed literature are reliable sources of information about changes in forest canopy composition. We reviewed the following aspects of forest succession in literature: disturbances; pathways of post-disturbance forest cover change; timing of successional steps; probabilities of post-disturbance forest cover change, and effects of geographic location and ecological site conditions on forest cover change. The results from studies in the literature, which were mostly based on sample plot observations, appeared to be sufficient to describe boreal forest cover change as a generalized discrete-state transition process, with the discrete states denoted by tree species dominance. In this paper, we outline an approach for incorporating published knowledge on forest succession into stochastic simulation models of boreal forest cover change in a standardized manner. We found that the lack of details in the literature on long-term forest succession, particularly on the influence of pre-disturbance forest cover composition, may be limiting factors in parameterizing simulation models. We suggest that the simulation models based on published information can provide a good foundation as null models, which can be further calibrated as detailed quantitative information on forest cover change becomes available. Key words: probabilistic model, transition matrix, boreal biome, landscape ecology

scholarly journals Discrepancies between Global Forest Net Primary Productivity Estimates Derived from MODIS and Forest Inventory Data and Underlying Factors

2021 ◽  
Vol 13 (8) ◽  
pp. 1441
Author(s):  
Jin Han Park ◽  
Jianbang Gan ◽  
Chan Park
Keyword(s):  
Low Income ◽  
Primary Productivity ◽  
Carbon Stock ◽  
Forest Cover ◽  
Net Primary Productivity ◽  
Estimation Methods ◽  
Future Research ◽  
Important Indicator ◽  
Sequestration Potential ◽  
Carbon Losses

The net primary productivity (NPP) of a forest is an important indicator of its potential for the provision of ecosystem services such as timber, carbon, and biodiversity. However, accurately and consistently quantifying global forest NPP remains a challenge in practice. We converted carbon stock changes using the Global Forest Resources Assessment (FRA) data and carbon losses associated with disturbances and timber removals into an NPP equivalent measurement (FRA NPP*) and compared it with the NPP derived from the MODIS satellite data (MOD17 NPP) for the world’s forests. We found statistically significant differences between the two NPP estimates, with the FRA NPP* being lower than the MOD17 NPP; the differences were correlated with forest cover, normalized difference vegetation index (NDVI), and GDP per capita in countries, and may also stem from the NPP estimation methods and scopes. While the former explicitly accounts for carbon losses associated with timber removals and disturbances, the latter better reflects the principles of photosynthesis. The discrepancies between the two NPP estimates increase in countries with a low income or low forest cover, calling for enhancing their forest resource assessment capacity. By identifying the discrepancies and underlying factors, we also provide new insights into the relationships between the MOD17 NPP and global forest carbon stock estimates, motivating and guiding future research to improve the robustness of quantifying global forest NPP and carbon sequestration potential.

Amazon forest cover change mapping based on semantic segmentation by U-Nets

2021 ◽  
Vol 62 ◽  
pp. 101279
Author(s):  
L. Bragagnolo ◽  
R.V. da Silva ◽  
J.M.V. Grzybowski
Keyword(s):  
Forest Cover ◽  
Semantic Segmentation ◽  
Amazon Forest ◽  
Forest Cover Change
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