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 A long-term perspective on deforestation rates in the Brazilian Amazon

2015 ◽  
Vol XL-7/W3 ◽  
pp. 539-544 ◽  
Author(s):  
M. D. Velasco Gomez ◽  
R. Beuchle ◽  
Y. Shimabukuro ◽  
R. Grecchi ◽  
D. Simonetti ◽  
...  
Keyword(s):  
Land Cover ◽  
Forest Cover ◽  
Brazilian Amazon ◽  
Thematic Mapper ◽  
Tree Cover ◽  
Amazon Forest ◽  
Forest Cover Change ◽  
Visual Interpretation ◽  
Deforestation Rates ◽  
Arc Of Deforestation

Monitoring tropical forest cover is central to biodiversity preservation, terrestrial carbon stocks, essential ecosystem and climate functions, and ultimately, sustainable economic development. The Amazon forest is the Earth’s largest rainforest, and despite intensive studies on current deforestation rates, relatively little is known as to how these compare to historic (pre 1985) deforestation rates. We quantified land cover change between 1975 and 2014 in the so-called Arc of Deforestation of the Brazilian Amazon, covering the southern stretch of the Amazon forest and part of the Cerrado biome. We applied a consistent method that made use of data from Landsat sensors: Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI). We acquired suitable images from the US Geological Survey (USGS) for five epochs: 1975, 1990, 2000, 2010, and 2014. We then performed land cover analysis for each epoch using a systematic sample of 156 sites, each one covering 10 km × 10 km, located at the confluence point of integer degree latitudes and longitudes. An object-based classification of the images was performed with five land cover classes: tree cover, tree cover mosaic, other wooded land, other land cover, and water. The automatic classification results were corrected by visual interpretation, and, when available, by comparison with higher resolution imagery. Our results show a decrease of forest cover of 24.2% in the last 40 years in the Brazilian Arc of Deforestation, with an average yearly net forest cover change rate of -0.71% for the 39 years considered.

scholarly journals Pathways to positive scenarios for the Amazon forest in Pará state, Brazil

2020 ◽  
Vol 20 (suppl 1) ◽  
Author(s):  
Juliana Siqueira-Gay ◽  
Aurora Miho Yanai ◽  
Janeth Lessmann ◽  
Ana Carolina M. Pessôa ◽  
Danilo Borja ◽  
...  
Keyword(s):  
Energy Demand ◽  
Forest Cover ◽  
Urban Expansion ◽  
Social Systems ◽  
Well Being ◽  
Conservation Strategies ◽  
Amazon Forest ◽  
Forest Cover Change ◽  
Land Occupation ◽  
Consumption Behavior

Abstract: Infrastructure projects and agriculture expansion are increasingly threatening forest conservation in Pará state (Brazil). It becomes necessary to address the implications of these activities on the Amazon complex socio-ecological system, considering both material and non-material aspects of Nature´s Contributions to People (NCP). Multiple studies developed future scenarios for the Amazon, but only a few have focused on discussing positive futures derived from policies and interventions based on conservation and human well-being. Here, we aim at understanding the drivers of forest cover change to produce positive scenarios for the future of the Amazon forest in Pará state. By using the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) conceptual framework, we identified as direct drivers of forest cover change: (i) roads construction; (ii) forest degradation; (iii) hydropower projects; (iv) urban expansion; (v) agriculture and pasture expansion; (vi) rural land occupation; (vii) mining; (viii) climate change. As indirect drivers we identified: (i) energy demand; (ii) population growth; (iii) land prices; (iv) commodity demand; (v) consumption behavior. The development of conservation strategies in the borders of deforested areas is needed given the high demand for Nature´s Contributions to People supply. We also propose policies to address the main drivers of forest cover change, influencing land management and consumption behavior in the state. At last, we envision future positive scenarios that would emerge from policy applications and sustainable actions. Based on our study, we discuss the importance of social learning for developing pathways leading to positive futures that consider the integrity and development of both ecological and social systems.

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 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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