scholarly journals Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

2016 ◽  
Vol 46 (1) ◽  
pp. 13-24 ◽  
Author(s):  
Everton Hafemann FRAGAL ◽  
Thiago Sanna Freire SILVA ◽  
Evlyn Márcia Leão de Moraes NOVO
Keyword(s):  
Time Series ◽  
Forest Cover ◽  
Ecosystem Functions ◽  
Support Vector ◽  
Forest Loss ◽  
Forest Cover Change ◽  
Disturbance And Recovery ◽  
Global Classification ◽  
Natural Losses ◽  
Historical Forest

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

scholarly journals Mapping Mangrove Forest Change in Nijhum Dwip Island

2019 ◽  
Vol 11 (1-2) ◽  
pp. 217-225
Author(s):  
MM Rahman ◽  
MAT Pramanik ◽  
MI Islam ◽  
S Razia
Keyword(s):  
Mangrove Forest ◽  
Forest Cover ◽  
Storm Surges ◽  
Forest Loss ◽  
Forest Conversion ◽  
Forest Cover Change ◽  
Forest Change ◽  
Change Analysis ◽  
Coastal Belt ◽  
Land Covers

Mangroves have been planting in the coastal belt of Bangladesh to protect the inhabitants of the coastal areas from cyclones and storm surges. Nijhum Dwip is located at the southern part of Hatiya Island. Most part of the island has been planted with the mangroves in the 1970s and 1980s; while parts of the mangroves have been deforested during the past few decades. The objectives of this research were to delineate and quantify the changes in the extent of mangroves in the island. The Landsat data of 1989, 2001, 2010 and 2018 have been utilized in the study. Three major land covers, namely forest, water and other land have been interpreted and delineated by using on-screen digitizing. The quantity of mangrove forest loss in the island is estimated as 1,024 ha, while 395 ha were afforested during 1989-2018. In the decadal change analysis, it was revealed that net forest cover change was higher in 2000s compared to other two decades and it was -425 ha. The result of the study is helpful to understand the extent and pattern of forest conversion in the island and to halt further forest loss and conserve the remaining forest. J. Environ. Sci. & Natural Resources, 11(1-2): 217-225 2018

scholarly journals Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

2022 ◽  
Vol 14 (2) ◽  
pp. 322
Author(s):  
Dmitry V. Ershov ◽  
Egor A. Gavrilyuk ◽  
Natalia V. Koroleva ◽  
Elena I. Belova ◽  
Elena V. Tikhonova ◽  
...  
Keyword(s):  
Time Series ◽  
Field Data ◽  
Forest Cover ◽  
Arable Land ◽  
Google Earth ◽  
Soviet Period ◽  
Ecosystem Functions ◽  
Landsat Data ◽  
Agricultural Lands ◽  
Post Soviet Period

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.

scholarly journals Migration, Remittances, and Forest Cover Change in Rural Guatemala and Chiapas, Mexico

Land ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 88 ◽  
Author(s):  
Arild Angelsen ◽  
Mariel Aguilar-Støen ◽  
John Ainembabazi ◽  
Edwin Castellanos ◽  
Matthew Taylor
Keyword(s):  
Remote Sensing ◽  
Rural Communities ◽  
Forest Cover ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Forest Loss ◽  
Household Surveys ◽  
Forest Cover Change ◽  
Household Members ◽  
Migration Flows

This article investigates how migration and remittances affect forest cover in eight rural communities in Guatemala and Chiapas, Mexico. Based on household surveys and remote sensing data, we found little evidence to support the widespread claim that migration takes pressure off forests. In the Chiapas sites, we observed no significant changes in forest cover since 1990, while in the Guatemalan sites, migration may have increased demand for agricultural land, leading to an average annual forest loss of 0.73% during the first decade of the millennium. We suggest that when attractive opportunities exist to invest in agriculture and land expansion, remittances and returnee savings provide fresh capital that is likely to increase pressure on forests. Our study also has implications for the understanding of migration flows; in particular, migration has not implied an exodus out of agriculture for the remaining household members nor for the returning migrants. On the contrary, returning migrants are more likely to be involved in farming activities after their return than they were before leaving.

Management effectiveness positively influences forest conservation outcomes in protected areas in Mexico

2020 ◽  
Author(s):  
Kathryn Powlen ◽  
Michael C. Gavin ◽  
Kelly W. Jones
Keyword(s):  
Protected Areas ◽  
Forest Cover ◽  
Forest Loss ◽  
Forest Cover Change ◽  
Management Effectiveness ◽  
Multiple Stakeholders ◽  
Management Quality ◽  
Quasi Experimental ◽  
Equal Sharing ◽  
Careful Design

Understanding the factors that drive protected area outcomes is critical to increase the success of global conservation efforts. Until recently, our understanding of the influence of management effectiveness has been restricted by the limited availability of standardized management data and study design limitations of prior evaluations. Here we use a quasi-experimental matching approach to test the influence of management effectiveness on forest cover change inside 46 protected areas in Mexico. We test the influence of five management categories, including context and planning, administration and finance, use and benefits, governance and social participation, and management quality, as well as an overall effectiveness score, using a subgroup analysis and an interaction term in post-matching multiple linear regression. Our results show that protected areas with higher management effectiveness have a greater effect on reducing deforestation compared to those with low management effectiveness, but that both types of protected areas experience less forest loss compared to similar unprotected areas. We find this trend in all five of the management categories and the overall score, with administration and finance scores having the greatest effect on forest loss outcomes. Our findings suggest careful design and planning, effective participation from multiple stakeholders and equal sharing of benefits, and sufficient human and financial capital can improve the effectiveness of protected areas in preventing forest loss.

Use of a dark object concept and support vector machines to automate forest cover change analysis

2008 ◽  
Vol 112 (3) ◽  
pp. 970-985 ◽  
Author(s):  
Chengquan Huang ◽  
Kuan Song ◽  
Sunghee Kim ◽  
John R.G. Townshend ◽  
Paul Davis ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Forest Cover ◽  
Support Vector ◽  
Forest Cover Change ◽  
Change Analysis ◽  
Object Concept ◽  
Dark Object ◽  
Vector Machines

scholarly journals Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels

2019 ◽  
Vol 11 (5) ◽  
pp. 556 ◽  
Author(s):  
Charlie Marshak ◽  
Marc Simard ◽  
Michael Denbina
Keyword(s):  
Time Series ◽  
Landsat Imagery ◽  
Optical Data ◽  
Support Vector ◽  
Forest Loss ◽  
Alos Palsar ◽  
Square Grid ◽  
Radar Images ◽  
L Band ◽  
Single Pixel

We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed using an ensemble of Support Vector Machines (SVMs) trained on local radar backscatter features derived from superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Québec, Canada. We evaluate four spatial arrangements including (1) single pixels, (2) square grid cells, (3) superpixels based on segmentation of the radar images, and (4) superpixels derived from ancillary optical Landsat imagery. Detection of forest loss using superpixels outperforms single pixel and regular square grid cell approaches, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Québec forestry data and Hansen et al. forest loss products. Our results indicate that this approach can be applied to monitor forest loss across large study areas using L-band radar instruments such as ALOS/PALSAR, particularly when combined with superpixels generated from ancillary optical data.

scholarly journals Object-Based Classification of Forest Disturbance Types in the Conterminous United States

2019 ◽  
Vol 11 (5) ◽  
pp. 477 ◽  
Author(s):  
Lian-Zhi Huo ◽  
Luigi Boschetti ◽  
Aaron Sparks
Keyword(s):  
United States ◽  
Forest Cover ◽  
Forest Disturbance ◽  
Global Scale ◽  
Management Policy ◽  
Forest Loss ◽  
Forest Cover Change ◽  
Forest Change ◽  
Goods And Services ◽  
Moderate Resolution Imaging Spectroradiometer

Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes.

scholarly journals Quantifying the Actual Impacts of Forest Cover Change on Surface Temperature in Guangdong, China

2020 ◽  
Vol 12 (15) ◽  
pp. 2354
Author(s):  
Wenjuan Shen ◽  
Jiaying He ◽  
Chengquan Huang ◽  
Mingshi Li
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Forest Cover ◽  
Temperature Response ◽  
Forest Loss ◽  
Forest Cover Change ◽  
Local Surface ◽  
Forest Change ◽  
Gain And Loss

Forest cover change is critical in the regulation of global and regional climate change through the alteration of biophysical features across the Earth’s surface. The accurate assessment of forest cover change can improve our understanding of its roles in the regulation processes of surface temperature. In spite of this, few researchers have attempted to discern the varying effects of multiple satellite-derived forest changes on local surface temperatures. In this study, we quantified the actual contributions of forest loss and gain associated with evapotranspiration (ET) and albedo to local surface temperature in Guangdong Province, China using an improved spatiotemporal change pattern analysis method, and explored the interrelationships between surface temperature and air temperature change. We specifically developed three forest change products for Guangdong, combining satellite observations from Landsat, PALSAR, and MODIS for comparison. Our results revealed that the adjusted simple change detection (SCD)-based Landsat/PALSAR forest cover data performed relatively well. We found that forest loss and gain between 2000 and 2010 had opposite effects on land surface temperature (LST), ET, and albedo. Forest gain led to a cooling of −0.12 ± 0.01 °C, while forest loss led to a warming of 0.07 ± 0.01 °C, which were opposite to the anomalous change of air temperature. A reduced warming to a considerable cooling was estimated due to the forest gain and loss across latitudes. Specifically, mid-subtropical forest gains increased LST by 0.25 ± 0.01 °C, while tropical forest loss decreased LST by −0.16 ± 0.05 °C, which can demonstrate the local differences in an overall cooling. ET induced cooling and warming effects were appropriate for most forest gain and loss. Meanwhile, the nearby temperature changes caused by no-change land cover types more or less canceled out some of the warming and cooling. Albedo exhibited negligible and complex impacts. The other two products (i.e., the GlobeLand30 and MCD12Q1) affect the magnitude of temperature response due to the discrepancies in forest definition, methodology, and data resolution. This study highlights the non-negligible contributions of high-resolution maps and a robust temperature response model in the quantification of the extent to which forest gain reverses the climate effects of forest loss under global warming.

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