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Special Issue "Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia"

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

Deadline for manuscript submissions: 31 December 2019

Special Issue Editors

Guest Editor
Dr. Krishna Prasad Vadrevu

Remote Sensing Scientist, NASA Marshall Space Flight Center, Huntsville, Alabama, USA
Website | E-Mail
Interests: remote sensing; forest biodiversity; fires; GHG emissions; sustainability
Guest Editor
Dr. Chris Justice

Department of Geographical Sciences, University of Maryland College Park, College Park, MD 20742, USA
Website | E-Mail
Interests: remote sensing of land use/cover changes; fires; agricultural systems; global change research
Guest Editor
Dr. Garik Gutman

NASA Headquarters, NASA Land-Cover/Land-Use Change Program, 300 E Street, SW Washington, DC 20546, USA
Website | E-Mail
Interests: telecoupling of land use systems; Land-atmosphere processes; Land governance; Land change trade-offs for ecosystem services and biodiversity; Land management systems; Urban-rural interactions; Land use and conflict

Special Issue Information

Dear Colleagues,

In several regions of the world, including South/Southeast Asian countries, Land Cover/Land Use Change (LC/LUC) is one of the most important types of environmental change, and it is occurring rapidly. In the region, LC/LUC changes include urban expansion, agricultural land loss, land abandonment, deforestation, logging, reforestation, etc. Documenting these LU/LUC and the causes, consequences, and impacts on the environment gain significance in the region, as the results can be useful for improved land management

Remote sensing, due to its multi-temporal, multi-spectral, repetitive and synoptic coverage capabilities, can be effectively used for mapping, monitoring and assessing the LC/LUC impacts on the environment. Information on the causative factors of LC/LUC can be obtained from the field surveys, which can be linked to the LC/LUC. LC/LUC is an interdisciplinary science theme which requires linking both biophysical and social aspects.

The current Special Issue invites articles on the use of remote sensing and geospatial technologies focusing on South/Southeast Asia in the in the following LC/LUC areas:

  • Use of optical, thermal, multispectral, hyperspectral, LIDAR and airborne remote sensing data for LC/LUC mapping/monitoring, quantifying the causes/consequences including impact assessment studies integrating both biophysical and social datasets;
  • Remote sensing of forest cover changes and impacts on biogeochemical cycling.
  • Agricultural monitoring and land use change mapping including remote sensing of crop growth stage, crop calendars, crop production, farming practices and impacts on water/energy balance.
  • LUCC, urbanization and associated impacts (urban climate, air and water pollution, etc.).
  • LUCC, fires, biomass burning and pollution impacts.
  • Integrating remote sensing data for emission inventories linking bottom-up and top-down approaches.
  • Mapping and monitoring of land management practices, disturbances, and interactions;
  • 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.

Both the regional scientists, as well as international researchers working on the above topics in the South/Southeast Asian region, are invited to contribute to this Special Issue.

Dr. Krishna Prasad Vadrevu
Dr. Garik Gutman
Prof. Chris Justice
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 semimonthly 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 1800 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.

Keywords

  • Land Cover/Land Use Change
  • Biophysical and Social Data Integration
  • Remote Sensing

Published Papers (5 papers)

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Research

Open AccessArticle
Urbanization and Its Impacts on Land Surface Temperature in Colombo Metropolitan Area, Sri Lanka, from 1988 to 2016
Remote Sens. 2019, 11(8), 957; https://doi.org/10.3390/rs11080957
Received: 25 February 2019 / Revised: 25 March 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
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Abstract
Urbanization has become one of the most important human activities modifying the Earth’s land surfaces; and its impacts on tropical and subtropical cities (e.g., in South/Southeast Asia) are not fully understood. Colombo; the capital of Sri Lanka; has been urbanized for about 2000 [...] Read more.
Urbanization has become one of the most important human activities modifying the Earth’s land surfaces; and its impacts on tropical and subtropical cities (e.g., in South/Southeast Asia) are not fully understood. Colombo; the capital of Sri Lanka; has been urbanized for about 2000 years; due to its strategic position on the east–west sea trade routes. This study aims to investigate the characteristics of urban expansion and its impacts on land surface temperature in Colombo from 1988 to 2016; using a time-series of Landsat images. Urban land cover changes (ULCC) were derived from time-series satellite images with the assistance of machine learning methods. Urban density was selected as a measure of urbanization; derived from both the multi-buffer ring method and a gravity model; which were comparatively adopted to evaluate the impacts of ULCC on the changes in land surface temperature (LST) over the study period. The experimental results indicate that: (1) the urban land cover classification during the study period was conducted with satisfactory accuracy; with more than 80% for the overall accuracy and over 0.73 for the Kappa coefficient; (2) the Colombo Metropolitan Area exhibits a diffusion pattern of urban growth; especially along the west coastal line; from both the multi-buffer ring approach and the gravity model; (3) urban density was identified as having a positive relationship with LST through time; (4) there was a noticeable increase in the mean LST; of 5.24 °C for water surfaces; 5.92 °C for vegetation; 8.62 °C for bare land; and 8.94 °C for urban areas. The results provide a scientific reference for policy makers and urban planners working towards a healthy and sustainable Colombo Metropolitan Area. Full article
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Open AccessArticle
Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017
Remote Sens. 2019, 11(7), 790; https://doi.org/10.3390/rs11070790
Received: 5 March 2019 / Revised: 26 March 2019 / Accepted: 28 March 2019 / Published: 2 April 2019
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Abstract
Although a detailed analysis of land use and land cover (LULC) change is essential in providing a greater understanding of increased human-environment interactions across the coastal region of Bangladesh, substantial challenges still exist for accurately classifying coastal LULC. This is due to the [...] Read more.
Although a detailed analysis of land use and land cover (LULC) change is essential in providing a greater understanding of increased human-environment interactions across the coastal region of Bangladesh, substantial challenges still exist for accurately classifying coastal LULC. This is due to the existence of high-level landscape heterogeneity and unavailability of good quality remotely sensed data. This study, the first of a kind, implemented a unique methodological approach to this challenge. Using freely available Landsat imagery, eXtreme Gradient Boosting (XGBoost)-based informative feature selection and Random Forest classification is used to elucidate spatio-temporal patterns of LULC across coastal areas over a 28-year period (1990–2017). We show that the XGBoost feature selection approach effectively addresses the issue of high landscape heterogeneity and spectral complexities in the image data, successfully augmenting the RF model performance (providing a mean user’s accuracy > 0.82). Multi-temporal LULC maps reveal that Bangladesh’s coastal areas experienced a net increase in agricultural land (5.44%), built-up (4.91%) and river (4.52%) areas over the past 28 years. While vegetation cover experienced a net decrease (8.26%), an increasing vegetation trend was observed in the years since 2000, primarily due to the Bangladesh government’s afforestation initiatives across the southern coastal belts. These findings provide a comprehensive picture of coastal LULC patterns, which will be useful for policy makers and resource managers to incorporate into coastal land use and environmental management practices. This work also provides useful methodological insights for future research to effectively address the spatial and spectral complexities of remotely sensed data used in classifying the LULC of a heterogeneous landscape. Full article
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Open AccessArticle
Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area
Remote Sens. 2019, 11(3), 333; https://doi.org/10.3390/rs11030333
Received: 11 December 2018 / Revised: 31 January 2019 / Accepted: 5 February 2019 / Published: 8 February 2019
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Abstract
Drought is a dry-weather event characterized by a deficit of water resources in a period of year due to less rainfall than normal or overexploitation. This insidious hazard tends to occur frequently and more intensively in sub-humid regions resulting in changes in the [...] Read more.
Drought is a dry-weather event characterized by a deficit of water resources in a period of year due to less rainfall than normal or overexploitation. This insidious hazard tends to occur frequently and more intensively in sub-humid regions resulting in changes in the landscape, transitions in agricultural practices and other environmental-social issues. The study area is in the sub-humid region of the northern coastal zone of Binh Thuan province, Vietnam—Tuy Phong district. This area is indicated as a subject of prolonged droughts during 6-month dry seasons, which have occurred more frequently in recent years. Associated with economic transitions in agricultural practicing, urbanization, and industrialization, prolonged droughts have resulted in rapid changes in land use and land cover (LULC) in Tuy Phong, especially in three coastal communes: Binh Thanh, Lien Huong, and Phuoc The. A bi-temporal analysis using high-resolution data, the 2011 WorldView2 and the 2016 GeoEye1, was examined to assess LULC changes from observed severe droughts in those three communes. Results showed a dramatic reduction in the extent of hydrological systems (about 20%), and significant increases of tree canopies in urban areas and near the coastal areas (approximately 76.8%). Paddy fields declined by 51% in 2016; such areas transitioned to inactive status or were alternated for growing drought-tolerant plants. Shrimp farming experienced a recognizable decrease by approximately 44%. The 2014 map and field observations during summer 2016 provide references for object-based classification and validation. Overall agreement of results is about 85%. Full article
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Open AccessArticle
Investigative Spatial Distribution and Modelling of Existing and Future Urban Land Changes and Its Impact on Urbanization and Economy
Remote Sens. 2019, 11(2), 105; https://doi.org/10.3390/rs11020105
Received: 15 October 2018 / Revised: 4 January 2019 / Accepted: 6 January 2019 / Published: 9 January 2019
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Abstract
Land use and land cover (LULC) change analysis is a critical instrument for studying urban growth across the world. Our objectives were to produce historical LULC maps during the 1988–2016 period for spatial and temporal analysis, forecast future LULC until 2040 by using [...] Read more.
Land use and land cover (LULC) change analysis is a critical instrument for studying urban growth across the world. Our objectives were to produce historical LULC maps during the 1988–2016 period for spatial and temporal analysis, forecast future LULC until 2040 by using the Markov model, and identify the impact of LULC on urbanization. Two scenes of Landsat-5 TM for 1988 and 2001 and one scene of Landsat-8 OLI for 2016 were processed and used. The Normalized Difference Vegetation Index (NDVI) model with precise class value ranges was applied to produce land cover maps with six classes of water, built-up, barren land, shrub and grassland, sparse vegetation, and dense vegetation. LULC maps for the years of 1988 and 2001 were used to develop an LULC transformation matrix. It was used to drive an LULC transformation probability matrix using a Markov model for future forecasting of LULC in 2014, 2027, and 2040. The accuracy of 2016 LULC classes was estimated by comparing it against Markov modeled classes. It was found that the areas for: (i) water decreased from 1.43% to 0.51%; (ii) built-up increased from 9.58% to 20.80%; (iii) barren land decreased from 29.50% to 13.40%; (iv) shrub and grass land decreased from 30.57% to 21.10%; (v) sparse vegetation increased from 18% to 20.10%; and (vi) dense vegetation increased from 10.57% to 24.10%. The variations in LULC classes could be noticed by 2040 as compared to 1988. This LULC variation revealed that the water could decrease to 5.32 km2 from 25.37 km2; the built-up could increase to 625.16 km2 from 168.29 km2; the barren land could decrease to 137.53 km2 from 514.13 km2; the shrub and grassland could decrease to 297.68 km2 from 539.46 km2; the sparse vegetation could decrease to 297.68 km2 from 539.46 km2; and the dense vegetation could increase to 409.65 km2 from 191.51 km2. The LULC classification accuracy was 90.27% and 95.11% for 1988 and 2001, respectively. The co-efficient of determination (R2) was found to be 0.90 for 2016 LULC classes obtained from Landsat-8 and derived from a Markov model. For District Lahore, the LULC changes could be related to increasing population and intense migration trends, which had progressive impact on infrastructure development, industrial and economic growth, and detrimental effects on water resources. Full article
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Open AccessArticle
Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles
Remote Sens. 2018, 10(7), 1101; https://doi.org/10.3390/rs10071101
Received: 15 June 2018 / Revised: 2 July 2018 / Accepted: 8 July 2018 / Published: 11 July 2018
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Abstract
Urban expansion has long been a research hotspot and is often based on individual cities, but rarely has research conducted a comprehensive comparison between coastal and inland metropoles for understanding different spatial patterns of urban expansions and driving forces. We selected coastal metropoles [...] Read more.
Urban expansion has long been a research hotspot and is often based on individual cities, but rarely has research conducted a comprehensive comparison between coastal and inland metropoles for understanding different spatial patterns of urban expansions and driving forces. We selected coastal metropoles (Shanghai and Shenzhen in China, and Ho Chi Minh City in Vietnam) and inland metropoles (Ulaanbaatar in Mongolia, Lanzhou in China, and Vientiane in Laos) with various developing stages and physical conditions for examining the spatiotemporal patterns of urban expansions in the past 25 years (1990–2015). Multitemporal Landsat images with 30 m spatial resolution were used to develop urban impervious surface area (ISA) distributions and examine their dynamic changes. The impacts of elevation, slope, and rivers on spatial patterns of urban expansion were examined. This research indicates that ISA is an important variable for examining urban expansion. Coastal metropoles had much faster urbanization rates than inland metropoles. The spatial patterns of urban ISA distribution and expansion are greatly influenced by physical conditions; that is, ISA is mainly distributed in the areas with slopes of less than 10 degrees. Rivers are important geographical factors constraining urban expansion, especially in developing stages, while bridges across the rivers promote urban expansion patterns and rates. The relationships of spatial patterns of urban ISA distribution and dynamics with physical conditions provide scientific data for urban planning, management, and sustainability. Full article
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