Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods

Wagle, Nimisha; Acharya, Tri Dev; Kolluru, Venkatesh; Huang, He; Lee, Dong Ha

doi:10.3390/app10228083

Cited by 43 publications

(15 citation statements)

References 58 publications

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“…Therefore, unsupervised classification can help understand the distribution of samples and the clustering of similar features. It was applied by Wagle et al [70] to generate their training data. Thus, we adopted ISO clustering to obtain 100 classes and applied stratified random sampling to generate 800 points.…”

Section: Reference Datamentioning

confidence: 99%

Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020

Wagle

Acharya

2021

IJGI

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Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technology such as remote sensing (RS) and cloud computing have made LC change mapping efficient. In this study, the LC of Rupandehi District of Nepal were mapped using a Landsat imagery and Random Forest (RF) classifier from 2005 to 2020 using Google Earth Engine (GEE) platform. GEE eases the way in extracting, analyzing, and performing different operations for the earth’s observed data. Land cover classification, Centre of gravity (CoG), and their trajectories for all LC classes: agriculture, built-up, water, forest, and barren area were extracted with five-year intervals, along with their Ecosystem service values (ESV) to understand the load on the ecosystem. We also discussed the aspects and problems of the spatiotemporal analysis of developing regions. It was observed that the built-up areas had been increasing over the years and more centered in between the two major cities. Other agriculture, water, and forest classes had been subjected to fluctuations with barren land in the decreasing trend. This alteration in the area of the LC classes also resulted in varying ESVs for individual land cover and total values for the years. The accuracy for the RF classifier was under substantial agreement for such fragmented LCs. Using LC, CoG, and ESV, the paper discusses the need for spatiotemporal analysis studies in Nepal to overcome the current limitations and later expansion to other regions. Studies such as these help in implementing proper plans and strategies by district administration offices and local governmental bodies to stop the exploitation of resources.

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Section: Reference Datamentioning

confidence: 99%

Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020

Wagle

Acharya

2021

IJGI

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“…Researchers are utilizing GEE in recent years for LC classification. The use of GEE for land cover classification using Landsat8 [24,25,27,28], sentinel2 [29] and combinations of sentinel2 and landsat8 [30] has shown good results. Therefore GEE presents great opportunities in dealing with remote sensing data for LC mapping in Pusad.…”

Section: Related Workmentioning

confidence: 99%

Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine

Desai

Gaikwad

2022

Int. j. electr. comput. eng. syst. (Online)

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Land cover is the most critical information required for land management and planning because human interference on land can be easily detected through it. However, mapping land cover utilizing optical remote sensing is not easy due to the acute shortage of cloud-free images. Google Earth Engine (GEE) is an efficient and effective tool for huge land cover analysis by providing access to large volumes of imagery available within a few days after acquisition in one consolidated system. This article demonstrates the use of Sentinel-1 datasets to create a land cover map of Pusad, Maharashtra using the GEE platform. Sentinel-1 provides Synthetic Aperture Radar (SAR) datasets that have a temporally dense and high spatial resolution, which is renowned for its cloud penetration characteristics and round-the-year observations irrespective of the weather. VV and VH polarization sentinel-1 time series data were automatically classified using a support vector machine (SVM) and Random Forest (RF) machine learning algorithms. Overall accuracies (OA), ranging from 82.3% to 90%, were obtained depending on polarization and methodology used. RF algorithm with VV polarization dataset stands better in comparison to SVM achieving OA of 90% and Kappa coefficient of 0.86. The highest user accuracy was obtained for the water class with both classifiers.

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“…We achieved an overall accuracy of 76% when compared against the historical LU map of 2016. The performance evaluation was good as Aghanashini and Gurupura rivers are heterogeneous catchments (Behera et al, 2018;Venkatesh et al, 2020a;Wagle et al, 2020). A total of 12 drivers categorized under bio-physical, proximity and socio-economic were implemented in this study.…”

Section: Land Use Change Modelingmentioning

confidence: 99%

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

Sharannya

Venkatesh

Mudbhatkal

et al. 2021

Environ Monit Assess

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CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006-2040), mid-future (2041-2070), and farfuture (2071-2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the presentday LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment.

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Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods

Cited by 43 publications

References 58 publications

Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020

Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020

Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

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