scholarly journals Patch-Based Forest Change Detection from Landsat Time Series

Forests ◽  
2017 ◽  
Vol 8 (5) ◽  
pp. 166 ◽  
Author(s):  
M. Hughes ◽  
S. Kaylor ◽  
Daniel Hayes
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Forest Change

Methodology to select phenologically suitable Landsat scenes for forest change detection

2011 ◽  
Author(s):  
Do-Hyung Kim ◽  
Raghuram Narashiman ◽  
Joseph O. Sexton ◽  
Chengquan Huang ◽  
John R. Townshend
Keyword(s):  
Change Detection ◽  
Forest Change

scholarly journals Agriculture is the primary driver of tree cover loss across the Forestière region of the Republic of Guinea, Africa

2021 ◽  
Author(s):  
Maegan Fitzgerald ◽  
Janet Nackoney ◽  
Peter V Potapov ◽  
Svetlana Turubanova
Keyword(s):  
West Africa ◽  
Change Detection ◽  
Protected Areas ◽  
Spatial Data ◽  
Tree Cover ◽  
Conservation Strategies ◽  
Forest Change ◽  
Mammal Species ◽  
Detection Model ◽  
Primary Driver

Abstract Biodiversity hotspots are conservation priority areas that feature exceptionally high levels of species endemism and high levels of habitat loss. The Guinean Forests of West Africa hotspot, home to a quarter of all the mammal species of Africa, has experienced high levels of forest loss within its protected areas. Here, we analyzed tree cover loss and its proximate drivers within Guinée Forestière, a high biodiversity region within the Guinean Forests of West Africa hotspot, both inside and outside protected areas. Using Landsat analysis ready data and a regionally calibrated, annual forest change detection model, we mapped tree cover loss occurring across this region from 2000 to 2018. We quantified the area of tree cover loss and identified proximate drivers using a statistical sample of reference data. The total tree cover loss in Guinée Forestière between years 2000 and 2018 was 10,907 km2 (SE 889 km2), which consists of approximately 25% of the region’s total land area. Of this total loss, 364 km2 (SE 91 km2) occurred within protected areas of high biodiversity value. Tree cover loss was not consistent across high biodiversity areas and did not appear to be related to protected area classification. Smallholder agriculture (subsistence and cash crop farming) was the primary driver of tree cover loss across Guinée Forestière. This research provides multitemporal spatial data on tree cover dynamics that is required for effective implementation of sustainable management and biodiversity conservation strategies within the broader socioecological landscape of Guinée Forestière. We also highlight important limitations to consider and address when using remote sensing to automate change detection across landscapes.

scholarly journals Urban forest change detection in Endayesus area, Tigray, Ethiopia

2019 ◽  
Vol 5 (1) ◽  
pp. 1641254
Author(s):  
Berihu Tesfamariam Zeratsion ◽  
Manuel Tejada Moral
Keyword(s):  
Change Detection ◽  
Urban Forest ◽  
Forest Change

scholarly journals Forest change detection based on sub-pixel tree cover estimates using Landsat-OLI and Sentinel 2 data

2020 ◽  
Vol 507 ◽  
pp. 012011
Author(s):  
T Khovratovich ◽  
S Bartalev ◽  
A Kashnitskii ◽  
I Balashov ◽  
A Ivanova
Keyword(s):  
Change Detection ◽  
Tree Cover ◽  
Forest Change ◽  
Sentinel 2

Improved Change Detection of Forests Using Landsat TM and ETM data

2016 ◽  
Vol 1 (1) ◽  
pp. 18-40 ◽  
Author(s):  
Kishor Sonawane ◽  
Vijay Bhagat ◽  
Keyword(s):  
Change Detection ◽  
Land Surface ◽  
Good Accuracy ◽  
Vegetation Indices ◽  
Landsat Tm ◽  
Sustainable Land Management ◽  
Forest Change ◽  
Classification Technique ◽  
Land Surface Water ◽  
Landsat 7

Landsat TM and ETM+ datasets are useful for forest change detection (FCD) at good accuracy level. Classified forest maps have been prepared using NDVI calculated from Landsat-5 TM (2009) and Landsat-7 ETM+ (2002) datasets for FCD using post-classification technique. About 58.59% of reviewed area shows positive changes, 33.69% no-changes and 7.72% negative changes with 77.84% accuracy. This accuracy insists limitations of present FCD analysis. Therefore, improved post-classification technique was formulated for precise FCD using field data and statistical techniques. Information about stable land surface (water bodies, rocky lands, deep forests, etc.) was used for normalisation of exaggerated reflectance in vegetation indices i.e. greenness. About 70.08% land estimated using second approach shows stable vegetation, 23.59% positive changes and 6.33% negative changes. Higher accuracy (95.21%) itself shows improvement in FCD technique and efficient applicability for sustainable land management.

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