Consequences of the Armed Conflict, Forced Human Displacement, and Land Abandonment on Forest Cover Change in Colombia: A Multi-scaled Analysis

Ecosystems ◽  
2013 ◽  
Vol 16 (6) ◽  
pp. 1052-1070 ◽  
Author(s):  
Ana María Sánchez-Cuervo ◽  
T. Mitchell Aide
Keyword(s):  
Armed Conflict ◽  
Forest Cover ◽  
Land Abandonment ◽  
Forest Cover Change ◽  
Human Displacement

scholarly journals Integrating Pixels, People, and Political Economy to Understand the Role of Armed Conflict and Geopolitics in Driving Deforestation: The Case of Myanmar

2021 ◽  
Vol 13 (22) ◽  
pp. 4589
Author(s):  
Kevin M. Woods ◽  
Panshi Wang ◽  
Joseph O. Sexton ◽  
Peter Leimgruber ◽  
Jesse Wong ◽  
...  
Keyword(s):  
Political Economy ◽  
Political Ecology ◽  
Armed Conflict ◽  
Forest Cover ◽  
Integrated Analysis ◽  
Forest Cover Change ◽  
The North ◽  
Deforestation Rates ◽  
Study Sites ◽  
Political Economic

Armed conflict and geopolitics are a driving force of Land Use and Land Cover Change (LULCC), but with considerable variation in deforestation trends between broader and finer scales of analysis. Remotely-sensed annual deforestation rates from 1989 to 2018 are presented at the national and (sub-) regional scales for Kachin State in the north of Myanmar and in Kayin State and Tanintharyi Region in the southeast. We pair our multiscaled remote sensing analysis with our multisited political ecology approach where we conducted field-based interviews in study sites between 2018 and 2020. Our integrated analysis identified three common periods of deforestation spikes at the national and state/region level, but with some notable disparities between regions as well as across and within townships and village tracts. We found the rate and geography of deforestation were most influenced by the territorial jurisdictions of armed authorities, national political economic reforms and timber regulations, and proximity to national borders and their respective geopolitical relations. The absence or presence of ceasefires in the north and southeast did not solely explain deforestation patterns. Rather than consider ceasefire or war as a singular explanatory variable effecting forest cover change, we demonstrate the need to analyze armed conflict as a dynamic multisited and diffuse phenomenon, which is simultaneously integrated into broader political economy and geopolitical forces.

Examining complexities of forest cover change during armed conflict on Nicaragua’s Atlantic Coast

2011 ◽  
Vol 20 (12) ◽  
pp. 2597-2613 ◽  
Author(s):  
Kara Stevens ◽  
Lindsay Campbell ◽  
Gerald Urquhart ◽  
Dan Kramer ◽  
Jiaguo Qi
Keyword(s):  
Armed Conflict ◽  
Forest Cover ◽  
Atlantic Coast ◽  
Forest Cover Change

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 The effects of armed conflict on forest cover changes across temporal and spatial scales in the Colombian Amazon

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Oscar V. Bautista-Cespedes ◽  
Louise Willemen ◽  
Augusto Castro-Nunez ◽  
Thomas A. Groen
Keyword(s):  
Armed Conflict ◽  
Forest Cover ◽  
Spatial Scales ◽  
Armed Forces ◽  
Peace Negotiations ◽  
Post Conflict ◽  
Forest Cover Changes ◽  
The Government ◽  
Colombian Amazon

AbstractThe Amazon rainforest covers roughly 40% of Colombia’s territory and has important global ecological functions. For more than 50 years, an internal war in the country has shaped this region. Peace negotiations between the government and the Revolutionary Armed Forces of Colombia (FARC) initiated in 2012 resulted in a progressive de-escalation of violence and a complete ceasefire in 2016. This study explores the role of different deforestation drivers including armed conflict variables, in explaining deforestation for three periods between 2001 and 2015. Iterative regression analyses were carried out for two spatial extents: the entire Colombian Amazon and a subset area which was most affected by deforestation. The results show that conflict variables have positive relationships with deforestation; yet, they are not among the main variables explaining deforestation. Accessibility and biophysical variables explain more variation. Nevertheless, conflict variables show divergent influence on deforestation depending on the period and scale of analysis. Based on these results, we develop deforestation risk maps to inform the design of forest conservation efforts in the post-conflict period.

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

scholarly journals Effectiveness of Protected Areas in the Pan-Tropics and International Aid for Conservation

Geomatics ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 335-346
Author(s):  
Do-Hyung Kim ◽  
Anupam Anand
Keyword(s):  
Protected Areas ◽  
Forest Conservation ◽  
Forest Cover ◽  
International Aid ◽  
Asian Countries ◽  
Forest Cover Change ◽  
Average Effect ◽  
Tropical Countries ◽  
Conservation Policies ◽  
The Tropics

Evaluation of the effectiveness of protected areas is critical for forest conservation policies and priorities. We used 30 m resolution forest cover change data from 1990 to 2010 for ~4000 protected areas to evaluate their effectiveness. Our results show that protected areas in the tropics avoided 83,500 ± 21,200 km2 of deforestation during the 2000s. Brazil’s protected areas have the largest amount of avoided deforestation at 50,000 km2. We also show the amount of international aid received by tropical countries compared to the effectiveness of protected areas. Thirty-four tropical countries received USD 42 billion during the 1990s and USD 62 billion during the 2000s in international aid for biodiversity conservation. The effectiveness of international aid was highest in Latin America, with 4.3 m2/USD, led by Brazil, while tropical Asian countries showed the lowest average effect of international aid, reaching only 0.17 m2/USD.

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