A new forest cover map of continental southeast Asia derived from SPOT‐VEGETATION satellite imagery

2004 ◽  
Vol 7 (2) ◽  
pp. 153-162 ◽  
Author(s):  
H.‐J. Stibig ◽  
F. Achard ◽  
S. Fritz
2013 ◽  
Vol 10 (8) ◽  
pp. 12625-12653 ◽  
Author(s):  
H.-J. Stibig ◽  
F. Achard ◽  
S. Carboni ◽  
R. Raši ◽  
J. Miettinen

Abstract. The study assesses the extent and trends of forest cover in Southeast Asia for the period 1990–2000–2010 and provides an overview on the main drivers of forest cover change. A systematic sample of 418 sites (10 km × 10 km size) located at the one-degree geographical confluence points and covered with satellite imagery of 30 m resolution is used for the assessment. Techniques of image segmentation and automated classification are combined with visual satellite image interpretation and quality control, involving forestry experts from Southeast Asian countries. The accuracy of our results is assessed through an independent consistency assessment, performed from a subsample of 1572 mapping units and resulting in an overall agreement of > 85% for the general differentiation of forest cover vs. non-forest cover. The total forest cover of Southeast Asia is estimated at 268 Mha in 1990, dropping to 236 Mha in 2010, with annual change rates of 1.75 Mha (~0.67% and 1.45 Mha (~0.59%) for the periods 1990–2000 and 2000–2010, respectively. The vast majority of forest cover loss (~2/3 for 2000–2010) occurred in insular Southeast Asia. Combining the change patterns visible from satellite imagery with the output of an expert consultation on the main drivers of forest change highlights the high pressure on the region's remaining forests. The conversion of forest cover to cash crop plantations (e.g. oil palm) is ranked as the dominant driver of forest change in Southeast Asia, followed by selective logging and the establishment of tree plantations.


2014 ◽  
Vol 11 (2) ◽  
pp. 247-258 ◽  
Author(s):  
H.-J. Stibig ◽  
F. Achard ◽  
S. Carboni ◽  
R. Raši ◽  
J. Miettinen

Abstract. The study assesses the extent and trends of forest cover in Southeast Asia for the periods 1990–2000 and 2000–2010 and provides an overview on the main causes of forest cover change. A systematic sample of 418 sites (10 km × 10 km size) located at the one-degree geographical confluence points and covered with satellite imagery of 30 m resolution is used for the assessment. Techniques of image segmentation and automated classification are combined with visual satellite image interpretation and quality control, involving forestry experts from Southeast Asian countries. The accuracy of our results is assessed through an independent consistency assessment, performed from a subsample of 1572 mapping units and resulting in an overall agreement of >85% for the general differentiation of forest cover versus non-forest cover. The total forest cover of Southeast Asia is estimated at 268 Mha in 1990, dropping to 236 Mha in 2010, with annual change rates of 1.75 Mha (∼0.67%) and 1.45 Mha (∼0.59%) for the periods 1990–2000 and 2000–2010, respectively. The vast majority of forest cover loss (∼2 / 3 for 2000–2010) occurred in insular Southeast Asia. Complementing our quantitative results by indicative information on patterns and on processes of forest change, obtained from the screening of satellite imagery and through expert consultation, respectively, confirms the conversion of forest to cash crops plantations (including oil palm) as the main cause of forest loss in Southeast Asia. Logging and the replacement of natural forests by forest plantations are two further important change processes in the region.


2020 ◽  
Vol 13 (3-4) ◽  
pp. 1-14
Author(s):  
Fernando Arturo Mendez Garzón ◽  
István Valánszki

Abstract Armed conflicts not only affect human populations but can also cause considerable damage to the environment. Its consequences are as diverse as its causes, including; water pollution from oil spills, land degradation due to the destruction of infrastructure, poisoning of soils and fields, destruction of crops and forests, over-exploitation of natural resources and paradoxically and occasionally reforestation. In this way, the environment in the war can be approached as beneficiary, stage, victim or/and spoil of war. Although there are few papers that assess the use of remote sensing methods in areas affected by warfare, we found a gap in these studies, being both outdated and lacking the correlation of remote sensing analysis with the causes-consequences, biome features and scale. Thus, this paper presents a methodical approach focused on the assessment of the existing datasets and the analysis of the connection between geographical conditions (biomes), drivers and the assessment using remote sensing methods in areas affected by armed conflicts. We aimed to find; weaknesses, tendencies, patterns, points of convergence and divergence. Then we consider variables such as biome, forest cover affectation, scale, and satellite imagery sensors to determine the relationship between warfare drivers with geographical location assessed by remote sensing methods. We collected data from 44 studies from international peer-reviewed journals from 1998 to 2019 that are indexed using scientific search engines. We found that 62% of the studies were focused on the analysis of torrid biomes as; Tropical Rainforest, Monsoon Forest / Dry Forest, Tree Savanna and Grass Savanna, using the 64% Moderate-resolution satellite imagery sensors as; Landsat 4-5 TM and Landsat 7 ETM+. Quantitative analysis of the trends identified within these areas contributes to an understanding of the reasons behind these conflicts.


2020 ◽  
Vol 62 (4) ◽  
pp. 288-305
Author(s):  
Addo Koranteng ◽  
Isaac Adu-Poku ◽  
Emmanuel Donkor ◽  
Tomasz Zawiła-Niedźwiecki

AbstractLand use and land cover (LULC) terrain in Ghana has undergone profound changes over the past years emanating mainly from anthropogenic activities, which have impacted countrywide and sub-regional environment. This study is a comprehensive analysis via integrated approach of geospatial procedures such as Remote Sensing (RS) and Geographic Information System (GIS) of past, present and future LULC from satellite imagery covering Ghana’s Ashanti regional capital (Kumasi) and surrounding districts. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Alos and Disaster Monitoring Constellation-DMC) and 2020 (SENTINEL), spanning over a 30-year period were mapped. Five major LULC categories – Closed Forest, Open Forest, Agriculture, Built-up and Water – were delineated premised on the prevailing geographical settings, field study and remote sensing data. Markov Cellular Automata modelling was applied to predict the probable LULC change consequence for the next 20 years (2040). The study revealed that both Open Forest and Agriculture class categories decreased 51.98 to 38.82 and 27.48 to 20.11, respectively. Meanwhile, Built-up class increased from 4.8% to 24.8% (over 500% increment from 1990 to 2020). Rapid urbanization caused the depletion of forest cover and conversion of farmlands into human settlements. The 2040 forecast map showed an upward increment in the Built-up area up to 35.2% at the expense of other LULC class categories. This trend from the past to the forecasted future would demand that judicious LULC resolutions have to be made to keep Ghana’s forest cover, provide arable land for farming activities and alleviate the effects of climate change.


2021 ◽  
Vol 7 ◽  
Author(s):  
Owen M. Exeter ◽  
Thaung Htut ◽  
Christopher R. Kerry ◽  
Maung Maung Kyi ◽  
Me'ira Mizrahi ◽  
...  

Coastal fisheries provide livelihoods and sustenance for millions of people globally but are often poorly documented. Data scarcity, particularly relating to spatio-temporal trends in catch and effort, compounds wider issues of governance capacity. This can hinder the implementation and effectiveness of spatial tools for fisheries management or conservation. This issue is acute in developing and low-income regions with many small-scale inshore fisheries and high marine biodiversity, such as Southeast Asia. As a result, fleets often operate unmonitored with implications for target and non-target species populations and the wider marine ecosystem. Novel and cost-effective approaches to obtain fisheries data are required to monitor these activities and help inform sustainable fishery and marine ecosystem management. One such example is the detection and numeration of fishing vessels that use artificial light to attract catch with nighttime satellite imagery. Here we test the efficiency and application value of nighttime satellite imagery, in combination with landings data and GPS tracked vessels, to estimate the footprint and biomass removal of an inshore purse seine fishery operating within a region of high biodiversity in Myanmar. By quantifying the number of remotely sensed vessel detections per month, adjusted for error by the GPS tracked vessels, we can extrapolate data from fisher logbooks to provide fine-scale spatiotemporal estimates of the fishery's effort, value and biomass removal. Estimates reveal local landings of nearly 9,000 mt worth close to $4 million USD annually. This approach details how remote sensed and in situ collected data can be applied to other fleets using artificial light to attract catch, notably inshore fisheries of Southeast Asia, whilst also providing a much-needed baseline understanding of a data-poor fishery's spatiotemporal activity, biomass removal, catch composition and landing of vulnerable species.


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