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

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 80758

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Special Issue Editors

Dr. Krishna Vadrevu

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Guest Editor

Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL, USA
Interests: remote sensing and GIS applications; land cover/land use changes; land-atmosphere interactions; satellite remote sensing of fires; biogeochemical cycling; biodiversity and ecology; agroecosystems and sustainability
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chris Justice

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Guest Editor

Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Interests: global change research; land use/cover change; satellite-based agriculture monitoring; satellite-based fire monitoring; terrestrial observing systems/remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Garik Gutman

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Guest Editor

NASA Headquarters, NASA Land-Cover/Land-Use Change Program, 300 E Street, SW Washington, DC 20546, USA
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 Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2700 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 (10 papers)

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21 pages, 11376 KiB

Open AccessArticle

Identification of Mung Bean in a Smallholder Farming Setting of Coastal South Asia Using Manned Aircraft Photography and Sentinel-2 Images

by Mustafa Kamal, Urs Schulthess and Timothy J. Krupnik

Remote Sens. 2020, 12(22), 3688; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223688 - 10 Nov 2020

Cited by 7 | Viewed by 4336

Abstract

Mung bean (Vigna radiata) plays an important role providing protein in the rice-based diet of the people in Bangladesh. In the coastal division of Barisal, our study area, the average farm size is less than 0.5 ha and individual fields measure about 0.10 ha. The availability of free Sentinel-2 optical satellite data acquired at a 10 m ground sampling distance (GSD) may offer an opportunity to generate crop area estimates in smallholder farming settings in South Asia. We combined different sources of in situ data, such as aerial photographs taken from a low flying manned aircraft, data collected on the ground, and data derived from satellite images to create a data set for a segment based classification of mung bean. User’s accuracy for mung bean was 0.98 and producer’s accuracy was 0.99. Hence, the accuracy metrics indicate that the random tree classifier was able to identify mung bean based on 10 m GSD data, despite the small size of individual fields. We estimated the mung bean area for 2019 at 109,416 ha, which is about 40% lower than the Department of Agricultural Extension estimates (183,480 ha), but more than four times higher than the 2019 data reported by the Bangladesh Bureau of Statistics (26,612 ha). Further analysis revealed that crop production tends to be clustered in the landscape by crop type. After merging adjacent segments by crop type, the following average cluster sizes resulted: 1.62 ha for mung bean, 0.74 ha for rice (Oryza sativa), 0.68 ha for weedy fallow and 0.40 ha for a category of other crops. This explains why 10 m GSD satellite data can be used for the identification of predominant crops grown in specific regions of South Asia. Full article

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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22 pages, 6057 KiB

Open AccessArticle

CA-Markov Chain Analysis of Seasonal Land Surface Temperature and Land Use Land Cover Change Using Optical Multi-Temporal Satellite Data of Faisalabad, Pakistan

by Aqil Tariq and Hong Shu

Remote Sens. 2020, 12(20), 3402; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203402 - 16 Oct 2020

Cited by 108 | Viewed by 10725

Abstract

Cellular Automata models are used for simulating spatial distributions and Markov Chain models are used for simulating temporal changes. The main aim of this study is to investigate the effect of urban growth on Faisalabad. This research is aimed at predicting seasonal Land-Surface-Temperature (LST) as well as Land-Use and Land-cover (LULC) with a Cellular-Automata-Markov-Chain (CA-Markov-Chain). Landsat 5, 7 and 8 data were used for mapping seasonal LULC and LST distributions during the months of May and November for the years 1990, 1998, 2004, 2008, 2013 and 2018. A CA-Markov-Chain was developed for simulating long-term landscape changes at 10-year time steps from 2018 to 2048. Furthermore, surface temperature during summers and winters were predicted well by Urban Index (UI), a non-vegetation index, demonstrating the highest correlation of R² = 0.8962 and R² = 0.9212 with respect to retrieved summer and winter surface temperature. Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 22.23 to 24.52 km² and from 108.53 to 122.61 km² in 2018 and 2048, as inferred from the changes occurred from 1990 to 2018. Considering UI as the predictor of seasonal LST, we predicted that the summer and winter temperature 24–28 °C and 14–16 °C and regions would decrease in coverage from 10.75 to 3.14% and from 8.81 to 3.47% between 2018 and 2048, while the summer and winter temperature 35–42 °C and winter 26–32 °C regions will increase in the proportion covered from 12.69 to 24.17% and 6.75–15.15% of city. Full article

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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21 pages, 2820 KiB

Open AccessArticle

A Multi Sensor Approach to Forest Type Mapping for Advancing Monitoring of Sustainable Development Goals (SDG) in Myanmar

by Sumalika Biswas, Qiongyu Huang, Anupam Anand, Myat Su Mon, Franz-Eugen Arnold and Peter Leimgruber

Remote Sens. 2020, 12(19), 3220; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193220 - 02 Oct 2020

Cited by 16 | Viewed by 4479

Abstract

Monitoring forests is important for measuring overall success of the 2030 Agenda because forests play an essential role in meeting many Sustainable Development Goals (SDG), especially SDG 15. Our study evaluates the contribution of three satellite data sources (Landsat-8, Sentinel-2 and Sentinel-1) for mapping diverse forest types in Myanmar. This assessment is especially important because Myanmar is currently revising its classification system for forests and it is critical that these new forest types can be accurately mapped and monitored over time using satellite imagery. Our results show that using a combination of Sentinel-1 and Sentinel-2 yields the highest accuracy (89.6% ± 0.16 percentage point(pp)), followed by Sentinel-2 alone (87.97% ± 0.11 pp) and Landsat-8 (82.68% ± 0.13 pp). The higher spatial resolution of Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands enhances accuracy by 4.83% compared to Landsat-8. The addition of the Sentinel-2 Near Infrared and three Vegetation Red Edge bands further improve accuracy by 0.46% compared to using only Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands. Adding the radar information from Sentinel-1 further increases the accuracy by 1.63%. We were able to map the two major forest types, Upper Moist and Upper Dry Mixed Deciduous Forest, which comprise 90% of our study area. Accuracies for these forest types ranged from 77 to 96% depending on the sensors used, demonstrating the feasibility of using satellite data to map forest categories from a newly revised classification system. Our results advance the ongoing development of the National Forest Monitoring System (NFMS) by the Myanmar Forest Department and United Nations-Food and Agriculture Organization (UN-FAO) and facilitates future monitoring of progress towards the SDGs. Full article

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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17 pages, 5451 KiB

Open AccessArticle

Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

by Thuan Sarzynski, Xingli Giam, Luis Carrasco and Janice Ser Huay Lee

Remote Sens. 2020, 12(7), 1220; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071220 - 10 Apr 2020

Cited by 35 | Viewed by 7857

Abstract

Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia. Full article

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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18 pages, 21013 KiB

Open AccessArticle

Urbanization and Its Impacts on Land Surface Temperature in Colombo Metropolitan Area, Sri Lanka, from 1988 to 2016

by H.P.U. Fonseka, Hongsheng Zhang, Ying Sun, Hua Su, Hui Lin and Yinyi Lin

Remote Sens. 2019, 11(8), 957; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11080957 - 22 Apr 2019

Cited by 54 | Viewed by 7520

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 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

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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26 pages, 4899 KiB

Open AccessArticle

Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017

by Abu Yousuf Md Abdullah, Arif Masrur, Mohammed Sarfaraz Gani Adnan, Md. Abdullah Al Baky, Quazi K. Hassan and Ashraf Dewan

Remote Sens. 2019, 11(7), 790; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070790 - 02 Apr 2019

Cited by 181 | Viewed by 15220

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 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

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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23 pages, 9196 KiB

Open AccessArticle

Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area

by Hoa Thi Tran, James B. Campbell, Randolph H. Wynne, Yang Shao and Son Viet Phan

Remote Sens. 2019, 11(3), 333; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030333 - 08 Feb 2019

Cited by 14 | Viewed by 8390

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 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

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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15 pages, 4613 KiB

Open AccessArticle

Investigative Spatial Distribution and Modelling of Existing and Future Urban Land Changes and Its Impact on Urbanization and Economy

by Tahir Ali Akbar, Quazi K. Hassan, Sana Ishaq, Maleeha Batool, Hira Jannat Butt and Hira Jabbar

Remote Sens. 2019, 11(2), 105; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020105 - 09 Jan 2019

Cited by 70 | Viewed by 7938

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 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

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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22 pages, 3599 KiB

Open AccessArticle

Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles

by Dengsheng Lu, Longwei Li, Guiying Li, Peilei Fan, Zutao Ouyang and Emilio Moran

Remote Sens. 2018, 10(7), 1101; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10071101 - 11 Jul 2018

Cited by 20 | Viewed by 4194

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 (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

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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12 pages, 1516 KiB

Open AccessLetter

Forest and Land Fires Are Mainly Associated with Deforestation in Riau Province, Indonesia

by Hari A. Adrianto, Dominick V. Spracklen, Stephen R. Arnold, Imas S. Sitanggang and Lailan Syaufina

Remote Sens. 2020, 12(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010003 - 18 Dec 2019

Cited by 35 | Viewed by 6736

Abstract

Indonesia has experienced extensive land-cover change and frequent vegetation and land fires in the past few decades. We combined a new land-cover dataset with satellite data on the timing and location of fires to make the first detailed assessment of the association of fire with specific land-cover transitions in Riau, Sumatra. During 1990 to 2017, secondary peat swamp forest declined in area from 40,000 to 10,000 km² and plantations (including oil palm) increased from around 10,000 to 40,000 km². The dominant land use transitions were secondary peat swamp forest converting directly to plantation, or first to shrub and then to plantation. During 2001–2017, we find that the frequency of fire is greatest in regions that change land-cover, with the greatest frequency in regions that transition from secondary peat swamp forest to shrub or plantation (0.15 km⁻² yr⁻¹). Areas that did not change land cover exhibit lower fire frequency, with shrub (0.06 km⁻² yr⁻¹) exhibiting a frequency of fire >60 times the frequency of fire in primary forest. Our analysis demonstrates that in Riau, fire is closely connected to land-cover change, and that the majority of fire is associated with the transition of secondary forest to shrub and plantation. Reducing the frequency of fire in Riau will require enhanced protection of secondary forests and restoration of shrub to natural forest. Full article

(This article belongs to the Special Issue Land Cover/Land Use Change (LC/LUC) – Causes, Consequences and Environmental Impacts in South/Southeast Asia)

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