Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

Saah, David; Tenneson, Karis; Matin, Mir A.; Uddin, Kabir; Cutter, Peter; Poortinga, Ate; Nguyen, Quyen; Patterson, Matthew; Johnson, Gary W.; Markert, Kel; Flores, Africa; Anderson, Eric; Weigel, Amanda; Ellenberg, Walter L.; Bhargava, Radhika; Aekakkararungroj, Aekkapol; Bhandari, Biplov; Khanal, Nishanta; Housman, Ian W.; Potapov, Peter; Tyukavina, Alexandra; Maus, P.; Ganz, David; Clinton, Nicholas; Chishtie, Farrukh

doi:10.3389/fenvs.2019.00150

Cited by 55 publications

(41 citation statements)

References 43 publications

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“…Training data is tremendously essential in mapping LULC; however, collecting sufficiently accurate training samples is challenging, especially for large-scale areas, long-term history analyses, and data-scarce environments such as Vietnam 73 . If training data are not collected consistently, it can result in misclassification or low accuracies 74 .…”

Section: Satellite-based Covariate Calculationmentioning

confidence: 99%

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Phan

Trung

Truong

et al. 2021

Sci Rep

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Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km2) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km2). The spatial–temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. The explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals.

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Section: Satellite-based Covariate Calculationmentioning

confidence: 99%

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Phan

Trung

Truong

et al. 2021

Sci Rep

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show abstract

“…The data products from this project provide a baseline on LULC change for 1997-2010 that could be used to aid management of water, agriculture, and natural disasters in the region, as well as more in-depth follow-on studies on the LULC change occurring in the LMB since 2010. Such LULC change data sets could also possibly benefit the SERVIR Mekong Hub's Regional Land Cover Monitoring System (Saah et al, 2019b), which was recently applied by Ingalls et al (2018) in LULC change assessment and land resource planning.…”

Section: Implications Of Resultsmentioning

confidence: 99%

Mapping Land Use Land Cover Change in the Lower Mekong Basin From 1997 to 2010

Spruce

Bolten²,

Mohammed

et al. 2020

Front. Environ. Sci.

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“…SERVIR harnesses space technology and geospatial technologies to to help decision makers to integrate geospatial information into their decision-making process. High quality surface water maps will support efforts for the regional land cover monitoring system [82][83][84][85][86], food security [87], monitoring of ecosystems [88,89] and water management applications [90]. The application of the presented surface water maps integrated with near real-time information systems for water have the potential to support effective decision making processes for sustainable landscape management.…”

Section: Future Workmentioning

confidence: 99%

Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

Markert

Mayer

et al. 2020

Remote Sensing

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Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications.

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Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

Cited by 55 publications

References 43 publications

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Mapping Land Use Land Cover Change in the Lower Mekong Basin From 1997 to 2010

Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

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