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Special Issue "Mapping, Monitoring and Impact Assessment of Land Cover/Land Use Changes in South and South East Asia"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 August 2017

Special Issue Editors

Guest Editor
Dr. Krishna Prasad Vadrevu

1. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
2. Adjunct Professor, Department of Geographical Sciences, University of Maryland, 4321 Hartwick Road, Suite 400, College Park, MD 20740, USA
Website1 | Website2 | E-Mail
Interests: satellite remote sensing of land use/cover changes; land atmospheric interactions; remote sensing of fires; biogeochemical cycling; agroecosystems
Guest Editor
Dr. Rama Nemani

Ecological Forecasting Laboratory, NASA Ames Research Center, MS 242-4, Moffett Field, CA 94035, USA
Website | E-Mail
Interests: ecological forecasting; collaborative computing; big-data analysis
Guest Editor
Prof. Chris Justice

Dept. of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Website | E-Mail
Interests: global change research; land use/cover change; satellite based agriculture monitoring; satellite based fire monitoring; terrestrial observing systems/remote sensing
Guest Editor
Dr. Garik Gutman

NASA Headquarters, Washington, DC 20546, USA
Website | E-Mail
Interests: remote sensing of land use/cover change, land-atmospheric interactions; big-data processing; remote sensing of environment

Special Issue Information

Rapidly occurring Land-Cover/Land Use Changes (LC/LUC) in South/Southeast Asia are powered by demand for food for its growing population. Several areas in the region are transitioning from largely agrarian to urban societies due to increased industrialization. There is an increasing concern that LC/LUC caused by urbanization, deforestation, and agricultural intensification in the region may affect local, regional, and global climate and atmospheric chemistry. Monitoring of land use/cover changes in the region is essential for the sustainable management of natural resources, environmental protection, air quality, agricultural planning, and food security.

Papers for the current Special Issue can address fundamental research in LC/LUC, development of crosscutting conceptual frameworks, applied tools (involving geospatial technologies), modeling, fieldwork case studies, and interdisciplinary research. Papers should explicitly relate to LC/LUC, drivers, and impacts on environment including modeling aspects. Papers that address both the biophysical and human dimensions of LC/LUC are particularly encouraged, and they should have significant remote-sensing component with focus on South/Southeast Asia. Some of the important themes are listed below:

  • Applications of coarse (NOAA AVHRR, MODIS), moderate (Landsat or similar) and fine (QuickBird or similar) resolution remote sensing data for LC/LUC mapping, monitoring and impact assessment studies.
  • Remote sensing of forest cover changes and impacts on biogeochemical cycling;
  • Agricultural land use change mapping and monitoring including remote sensing of crop growth stages, crop calendars, farming practices and impacts on water/energy balance.
  • LC/LUC, urbanization and associated impacts (urban climate, air and water pollution, etc.);
  • LUCC, fires, biomass burning and pollution impacts;
  • Mapping and monitoring of land management practices, disturbances and interactions with atmosphere;
  • Detecting long term trends in LUCC and impacts on hydrological variables, such as runoff, evapotranspiration, and soil moisture;
  • Spatio-temporal data mining, modeling and analysis for LUCC data and impact assessment studies;
  • New tools and methods for LUCC data generation and dissemination.

Dr. Krishna Prasad Vadrevu
Dr. Rama Nemani
Prof. Chris Justice
Dr. Garik Gutman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (12 papers)

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Research

Open AccessArticle Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data
Remote Sens. 2017, 9(4), 366; doi:10.3390/rs9040366
Received: 1 February 2017 / Revised: 4 April 2017 / Accepted: 9 April 2017 / Published: 13 April 2017
Cited by 2 | PDF Full-text (11966 KB) | HTML Full-text | XML Full-text
Abstract
Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface
[...] Read more.
Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 2000–2010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approachAn independent validation dataset was also developed using Google Earth™ imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 ± 7.84 km2. Full article
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Open AccessArticle Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data
Remote Sens. 2017, 9(4), 320; doi:10.3390/rs9040320
Received: 23 December 2016 / Revised: 10 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
PDF Full-text (24021 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We performed a multi-date composite change detection technique using a dense-time stack of Landsat data to map land-use and land-cover change (LCLUC) in Mainland Southeast Asia (MSEA) with a focus on the expansion of boom crops, primarily tree crops. The supervised classification was
[...] Read more.
We performed a multi-date composite change detection technique using a dense-time stack of Landsat data to map land-use and land-cover change (LCLUC) in Mainland Southeast Asia (MSEA) with a focus on the expansion of boom crops, primarily tree crops. The supervised classification was performed using Support Vector Machines (SVM), which are supervised non-parametric statistical learning techniques. To select the most suitable SMV classifier and the related parameter settings, we used the training data and performed a two-dimensional grid search with a three-fold internal cross-validation. We worked in seven Landsat footprints and found the linear kernel to be the most suitable for all footprints, but the most suitable regularization parameter C varied across the footprints. We distinguished a total of 41 LCLUCs (13 to 31 classes per footprint) in very dynamic and heterogeneous landscapes. The approach proved useful for distinguishing subtle changes over time and to map a variety of land covers, tree crops, and transformations as long as sufficient training points could be collected for each class. While to date, this approach has only been applied to mapping urban extent and expansion, this study shows that it is also useful for mapping change in rural settings, especially when images from phenologically relevant acquisition dates are included. Full article
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Open AccessArticle Remote Sensing-Based Assessment of the 2005–2011 Bamboo Reproductive Event in the Arakan Mountain Range and Its Relation with Wildfires
Remote Sens. 2017, 9(1), 85; doi:10.3390/rs9010085
Received: 26 July 2016 / Revised: 28 November 2016 / Accepted: 11 January 2017 / Published: 18 January 2017
PDF Full-text (3440 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Pulse ecological events have major impacts on regional and global biogeochemical cycles, potentially inducing a vast set of cascading ecological effects. This study analyzes the widespread reproductive event of bamboo (Melocanna baccifera) that occurred in the Arakan Mountains (Southeast Asia) from
[...] Read more.
Pulse ecological events have major impacts on regional and global biogeochemical cycles, potentially inducing a vast set of cascading ecological effects. This study analyzes the widespread reproductive event of bamboo (Melocanna baccifera) that occurred in the Arakan Mountains (Southeast Asia) from 2005 to 2011, and investigates the possible relationship between massive fuel loading due to bamboo synchronous mortality over large areas and wildfire regime. Multiple remote sensing data products are used to map the areal extent of the bamboo-dominated forest. MODIS NDVI time series are then analyzed to detect the spatiotemporal patterns of the reproductive event. Finally, MODIS Active Fire and Burned Area Products are used to investigate the distribution and extension of wildfires before and after the reproductive event. Bamboo dominates about 62,000 km2 of forest in Arakan. Over 65% of the region shows evidence of synchronous bamboo flowering, fruiting, and mortality over large areas, with wave-like spatiotemporal dynamics. A significant change in the regime of wildfires is observed, with total burned area doubling in the bamboo-dominated forest area and reaching almost 16,000 km2. Wildfires also severely affect the remnant patches of the evergreen forest adjacent to the bamboo forest. These results demonstrate a clear interconnection between the 2005–2011 bamboo reproductive event and the wildfires spreading in the region, with potential relevant socio-economic and environmental impacts. Full article
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Open AccessArticle Telecouplings in the East–West Economic Corridor within Borders and Across
Remote Sens. 2016, 8(12), 1012; doi:10.3390/rs8121012
Received: 31 July 2016 / Revised: 22 November 2016 / Accepted: 2 December 2016 / Published: 11 December 2016
Cited by 1 | PDF Full-text (13550 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, the concepts of teleconnections and telecoupling have been introduced into land-use and land-cover change literature as frameworks that seek to explain connections between areas that are not in close physical proximity to each other. The conceptual frameworks of teleconnections and
[...] Read more.
In recent years, the concepts of teleconnections and telecoupling have been introduced into land-use and land-cover change literature as frameworks that seek to explain connections between areas that are not in close physical proximity to each other. The conceptual frameworks of teleconnections and telecoupling seek to explicitly link land changes in one place, or in a number of places, to distant, usually non-physically connected locations. These conceptual frameworks are offered as new ways of understanding land changes; rather than viewing land-use and land-cover change through discrete land classifications that have been based on the idea of land-use as seen through rural–urban dichotomies, path dependencies and sequential land transitions, and place-based relationships. Focusing on the land-use and land-cover changes taking place along the East–West Economic Corridor that runs from Dong Ha City in Quang Tri, Vietnam, through Sepon District, Savannakhet, Lao PDR, into Thailand this paper makes use of data gathered from fieldwork and remote sensing analysis to examine telecouplings between sending, receiving and spill-over systems on both sides of the Vietnam-Lao PDR border. Findings are that the telecouplings are driving changes in rural village and urban systems on both sides of the border, and are enabled by a policy environment that has sought to facilitate the cross-border transportation of goods within the region. Full article
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Open AccessArticle Tropical Peatland Burn Depth and Combustion Heterogeneity Assessed Using UAV Photogrammetry and Airborne LiDAR
Remote Sens. 2016, 8(12), 1000; doi:10.3390/rs8121000
Received: 4 October 2016 / Revised: 14 November 2016 / Accepted: 29 November 2016 / Published: 6 December 2016
Cited by 2 | PDF Full-text (15606 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We provide the first assessment of tropical peatland depth of burn (DoB) using structure from motion (SfM) photogrammetry, applied to imagery collected using a low-cost, low-altitude unmanned aerial vehicle (UAV) system operated over a 5.2 ha tropical peatland in Jambi Province on Sumatra,
[...] Read more.
We provide the first assessment of tropical peatland depth of burn (DoB) using structure from motion (SfM) photogrammetry, applied to imagery collected using a low-cost, low-altitude unmanned aerial vehicle (UAV) system operated over a 5.2 ha tropical peatland in Jambi Province on Sumatra, Indonesia. Tropical peat soils are the result of thousands of years of dead biomass accumulation, and when burned are globally significant net sources of carbon emissions. The El Niño year of 2015 saw huge areas of Indonesia affected by tropical peatland fires, more so than any year since 1997. However, the Depth of Burn (DoB) of these 2015 fires has not been assessed, and indeed has only previously been assessed in few tropical peatland burns in Kalimantan. Therefore, DoB remains arguably the largest uncertainty when undertaking fire emissions calculations in these tropical peatland environments. We apply a SfM photogrammetric methodology to map this DoB metric, and also investigate combustion heterogeneity using orthomosaic photography collected using the UAV system. We supplement this information with pre-burn airborne light detection and ranging (LiDAR) data, reducing uncertainty by estimating pre-burn soil height more accurately than from interpolation of adjacent unburned areas alone. Our pre-and post-fire Digital Terrain Models (DTMs) show accuracies of 0.04 and 0.05 m (root-mean-square error, RMSE) respectively, compared to ground-based global navigation satellite system (GNSS) surveys. Our final DoB map of a 5.2 ha degraded peat swamp forest area neighboring Berbak National Park (Sumatra, Indonesia) shows burn depths extending from close to zero to over 1 m, with a mean (±1σ) DoB of 0.23 ± 0.19 m. This lies well within the range found by the few other studies available (on Kalimantan; none are available on Sumatra). Our combustion heterogeneity analysis suggests the deepest burns, which extend to ~1.3 m, occur around tree roots. We use these DoB data within the Intergovernmental Panel on Climate Change (IPCC) default equation for fire emissions to estimate mean carbon emissions as 134 ± 29 t·C∙ha−1 for this peatland fire, which is in an area that had not had a recorded fire previously. This is amongst the highest per unit area fuel consumption anywhere in the world for landscape fires. Our approach provides significant uncertainty reductions in such emissions calculations via the reduction in DoB uncertainty, and by using the UAV SfM approach this is accomplished at a fraction of the cost of airborne LiDAR—albeit over limited sized areas at present. Deploying this approach at locations across Indonesia, sampling a variety of fire-affected landscapes, would provide new and important DoB statistics for producing optimized carbon and greenhouse gas (GHG) emissions estimates from peatland fires. Full article
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Open AccessArticle Assessment of Mining Extent and Expansion in Myanmar Based on Freely-Available Satellite Imagery
Remote Sens. 2016, 8(11), 912; doi:10.3390/rs8110912
Received: 30 July 2016 / Revised: 22 October 2016 / Accepted: 28 October 2016 / Published: 3 November 2016
PDF Full-text (1938 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Using freely-available data and open-source software, we developed a remote sensing methodology to identify mining areas and assess recent mining expansion in Myanmar. Our country-wide analysis used Landsat 8 satellite data from a select number of mining areas to create a raster layer
[...] Read more.
Using freely-available data and open-source software, we developed a remote sensing methodology to identify mining areas and assess recent mining expansion in Myanmar. Our country-wide analysis used Landsat 8 satellite data from a select number of mining areas to create a raster layer of potential mining areas. We used this layer to guide a systematic scan of freely-available fine-resolution imagery, such as Google Earth, in order to digitize likely mining areas. During this process, each mining area was assigned a ranking indicating our certainty in correct identification of the mining land use. Finally, we identified areas of recent mining expansion based on the change in albedo, or brightness, between Landsat images from 2002 and 2015. We identified 90,041 ha of potential mining areas in Myanmar, of which 58% (52,312 ha) was assigned high certainty, 29% (26,251 ha) medium certainty, and 13% (11,478 ha) low certainty. Of the high-certainty mining areas, 62% of bare ground was disturbed (had a large increase in albedo) since 2002. This four-month project provides the first publicly-available database of mining areas in Myanmar, and it demonstrates an approach for large-scale assessment of mining extent and expansion based on freely-available data. Full article
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Open AccessArticle Differential Heating in the Indian Ocean Differentially Modulates Precipitation in the Ganges and Brahmaputra Basins
Remote Sens. 2016, 8(11), 901; doi:10.3390/rs8110901
Received: 30 July 2016 / Revised: 27 September 2016 / Accepted: 12 October 2016 / Published: 31 October 2016
PDF Full-text (6496 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Indo-Pacific sea surface temperature dynamics play a prominent role in Asian summer monsoon variability. Two interactive climate modes of the Indo-Pacific—the El Niño/Southern Oscillation (ENSO) and the Indian Ocean dipole mode—modulate the amount of precipitation over India, in addition to precipitation over Africa,
[...] Read more.
Indo-Pacific sea surface temperature dynamics play a prominent role in Asian summer monsoon variability. Two interactive climate modes of the Indo-Pacific—the El Niño/Southern Oscillation (ENSO) and the Indian Ocean dipole mode—modulate the amount of precipitation over India, in addition to precipitation over Africa, Indonesia, and Australia. However, this modulation is not spatially uniform. The precipitation in southern India is strongly forced by the Indian Ocean dipole mode and ENSO. In contrast, across northern India, encompassing the Ganges and Brahmaputra basins, the climate mode influence on precipitation is much less. Understanding the forcing of precipitation in these river basins is vital for food security and ecosystem services for over half a billion people. Using 28 years of remote sensing observations, we demonstrate that (i) the tropical west-east differential heating in the Indian Ocean influences the Ganges precipitation and (ii) the north-south differential heating in the Indian Ocean influences the Brahmaputra precipitation. The El Niño phase induces warming in the warm pool of the Indian Ocean and exerts more influence on Ganges precipitation than Brahmaputra precipitation. The analyses indicate that both the magnitude and position of the sea surface temperature anomalies in the Indian Ocean are important drivers for precipitation dynamics that can be effectively summarized using two new indices, one tuned for each basin. These new indices have the potential to aid forecasting of drought and flooding, to contextualize land cover and land use change, and to assess the regional impacts of climate change. Full article
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Open AccessArticle Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region
Remote Sens. 2016, 8(11), 882; doi:10.3390/rs8110882
Received: 31 July 2016 / Revised: 29 September 2016 / Accepted: 21 October 2016 / Published: 25 October 2016
Cited by 2 | PDF Full-text (6037 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We investigated the use of multi-spectral Landsat OLI imagery for delineating mangrove, lowland evergreen, upland evergreen and mixed deciduous forest types in Myanmar’s Tanintharyi Region and estimated the extent of degraded forest for each unique forest type. We mapped a total of 16
[...] Read more.
We investigated the use of multi-spectral Landsat OLI imagery for delineating mangrove, lowland evergreen, upland evergreen and mixed deciduous forest types in Myanmar’s Tanintharyi Region and estimated the extent of degraded forest for each unique forest type. We mapped a total of 16 natural and human land use classes using both a Random Forest algorithm and a multivariate Gaussian model while considering scenarios with all natural forest classes grouped into a single intact or degraded category. Overall, classification accuracy increased for the multivariate Gaussian model with the partitioning of intact and degraded forest into separate forest cover classes but slightly decreased based on the Random Forest classifier. Natural forest cover was estimated to be 80.7% of total area in Tanintharyi. The most prevalent forest types are upland evergreen forest (42.3% of area) and lowland evergreen forest (21.6%). However, while just 27.1% of upland evergreen forest was classified as degraded (on the basis of canopy cover <80%), 66.0% of mangrove forest and 47.5% of the region’s biologically-rich lowland evergreen forest were classified as degraded. This information on the current status of Tanintharyi’s unique forest ecosystems and patterns of human land use is critical to effective conservation strategies and land-use planning. Full article
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Open AccessArticle Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data
Remote Sens. 2016, 8(10), 860; doi:10.3390/rs8100860
Received: 31 July 2016 / Revised: 1 October 2016 / Accepted: 11 October 2016 / Published: 20 October 2016
Cited by 2 | PDF Full-text (3630 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Remote sensing offers a low-cost method for developing spatially continuous crop production statistics across large areas and through time. Nevertheless, it has been difficult to characterize the production of individual smallholder farms, given that the land-holding size in most areas of South Asia
[...] Read more.
Remote sensing offers a low-cost method for developing spatially continuous crop production statistics across large areas and through time. Nevertheless, it has been difficult to characterize the production of individual smallholder farms, given that the land-holding size in most areas of South Asia (<2 ha) is smaller than the spatial resolution of most freely available satellite imagery, like Landsat and MODIS. In addition, existing methods to map yield require field-level data to develop and parameterize predictive algorithms that translate satellite vegetation indices to yield, yet these data are costly or difficult to obtain in many smallholder systems. To overcome these challenges, this study explores two issues. First, we employ new high spatial (2 m) and temporal (bi-weekly) resolution micro-satellite SkySat data to map sowing dates and yields of smallholder wheat fields in Bihar, India in the 2014–2015 and 2015–2016 growing seasons. Second, we compare how well we predict sowing date and yield when using ground data, like crop cuts and self-reports, versus using crop models, which require no on-the-ground data, to develop and parameterize prediction models. Overall, sow dates were predicted well (R2 = 0.41 in 2014–2015 and R2 = 0.62 in 2015–2016), particularly when using models that were parameterized using self-report sow dates collected close to the time of planting and when using imagery that spanned the entire growing season. We were also able to map yields fairly well (R2 = 0.27 in 2014–2015 and R2 = 0.33 in 2015–2016), with crop cut parameterized models resulting in the highest accuracies. While less accurate, we were able to capture the large range in sow dates and yields across farms when using models parameterized with crop model data and these estimates were able to detect known relationships between management factors (e.g., sow date, fertilizer, and irrigation) and yield. While these results are specific to our study site in India, it is likely that the methods employed and the lessons learned are applicable to smallholder systems more generally across the globe. This is of particular interest given that similar high spatio-temporal resolution micro-satellite data will become increasingly available in the coming years. Full article
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Open AccessArticle Urban Built-up Areas in Transitional Economies of Southeast Asia: Spatial Extent and Dynamics
Remote Sens. 2016, 8(10), 819; doi:10.3390/rs8100819
Received: 3 August 2016 / Revised: 15 September 2016 / Accepted: 27 September 2016 / Published: 2 October 2016
Cited by 1 | PDF Full-text (7025 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Urban built-up area, one of the most important measures of an urban landscape, is an essential variable for understanding ecological and socioeconomic processes in urban systems. With an interest in urban development in transitional economies in Southeast Asia, we recognized a lack of
[...] Read more.
Urban built-up area, one of the most important measures of an urban landscape, is an essential variable for understanding ecological and socioeconomic processes in urban systems. With an interest in urban development in transitional economies in Southeast Asia, we recognized a lack of high-to-medium resolution (<60 m) built-up information for countries in the region, including Vietnam, Laos, Cambodia and Myanmar. In this study, we combined multiple remote sensing data, including Landsat, DMSP/OLS night time light, MODIS NDVI data and other ancillary spatial data, to develop a 30-m resolution urban built-up map of 2010 for the above four countries. Following the trend analysis of the DMSP/OLS time series and the 2010 urban built-up extent, we also quantified the spatiotemporal dynamics of urban built-up areas from 1992 to 2010. Among the four countries, Vietnam had the highest proportion of urban built-up area (0.91%), followed by Myanmar (0.15%), Cambodia (0.12%) and Laos (0.09%). Vietnam was also the fastest in new built-up development (increased ~8.8-times during the 18-year study period), followed by Laos, Cambodia and Myanmar, which increased at 6.0-, 3.6- and 0.24-times, respectively. Full article
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Open AccessArticle Environmental Concerns of Deforestation in Myanmar 2001–2010
Remote Sens. 2016, 8(9), 728; doi:10.3390/rs8090728
Received: 11 June 2016 / Revised: 25 August 2016 / Accepted: 30 August 2016 / Published: 2 September 2016
Cited by 2 | PDF Full-text (13935 KB) | HTML Full-text | XML Full-text
Abstract
Deforestation in Myanmar has recently attracted much attention worldwide. This study examined spatio-temporal patterns of deforestation and forest carbon flux in Myanmar from 2001 to 2010 and environmental impacts at the regional scale using land products of the Moderate Resolution Imaging Spectroradiometer (MODIS).
[...] Read more.
Deforestation in Myanmar has recently attracted much attention worldwide. This study examined spatio-temporal patterns of deforestation and forest carbon flux in Myanmar from 2001 to 2010 and environmental impacts at the regional scale using land products of the Moderate Resolution Imaging Spectroradiometer (MODIS). The results suggest that the total deforestation area in Myanmar was 21,178.8 km2, with an annual deforestation rate of 0.81%, and that the total forest carbon release was 20.06 million tons, with an annual rate of 0.37%. Mangrove forests had the highest deforestation and carbon release rates, and deciduous forests had both the largest deforestation area and largest amount of carbon release. During the study period, the south and southwestern regions of Myanmar, especially Ayeyarwady and Rakhine, were deforestation hotspots (i.e., the highest deforestation and carbon release rates occurred in these regions). Deforestation caused significant carbon release, reduced evapotranspiration (ET), and increased land surface temperatures (LSTs) in deforested areas in Myanmar during the study period. Constructive policy recommendations are put forward based on these research results. Full article
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Open AccessArticle Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?
Remote Sens. 2016, 8(8), 657; doi:10.3390/rs8080657
Received: 12 May 2016 / Revised: 2 August 2016 / Accepted: 8 August 2016 / Published: 15 August 2016
Cited by 1 | PDF Full-text (19352 KB) | HTML Full-text | XML Full-text
Abstract
Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found
[...] Read more.
Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions. Full article
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