Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil

Remote sensing is undergoing a fundamental paradigm shift, in which approaches interpreting one or two images are giving way to a wide array of data-rich applications. These include assessing global forest loss, tracking water resources across Earth’s surface, determining disturbance frequency across decades, and many more. These advances have been greatly facilitated by Google Earth Engine, which provides both image access and a platform for advanced analysis techniques. Within the realm of land-use/land-cover (LULC) classifications, Earth Engine provides the ability to create new classifications and to access major existing data sets that have already been created, particularly at global extents. By overlaying global LULC classifications—the 300-m GlobCover 2009 LULC data set for example—with sharper images like those from Landsat, one can see the promise and limits of these global data sets and platforms to fuse them. Despite the promise in a global classification covering all of the terrestrial surface, GlobCover 2009 may be too coarse for some applications. We asked whether the LULC labeling provided by GlobCover 2009 could be combined with the spatial granularity of the Landsat platform to produce a hybrid classification having the best features of both resources with high accuracy. Here we apply an improvement of the Bayesian Updating of Land Cover (BULC) algorithm that fused unsupervised Landsat classifications to GlobCover 2009, sharpening the result from a 300-m to a 30-m classification. Working with four clear categories in Mato Grosso, Brazil, we refined the resolution of the LULC classification by an order of magnitude while improving the overall accuracy from 69.1 to 97.5%. This “BULC-U” mode, because it uses unsupervised classifications as inputs, demands less region-specific knowledge from analysts and may be significantly easier for non-specialists to use. This technique can provide new information to land managers and others interested in highly accurate classifications at finer scales.

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Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture

Remote Sensing ◽

10.3390/rs12193139 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3139

Author(s):

Chenli Liu ◽

Wenlong Li ◽

Gaofeng Zhu ◽

Huakun Zhou ◽

Hepiao Yan ◽

...

Keyword(s):

Land Use ◽

Tibetan Plateau ◽

Land Cover ◽

Google Earth ◽

Driving Factors ◽

Optical Remote Sensing ◽

Land Use Land Cover ◽

Landsat Images ◽

Lulc Changes ◽

Google Earth Engine

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.

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Monitoring Urban Expansion And Land Use/Land Cover Changes In Banadir, Somalia Using Google Earth Engine (GEE)

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/767/1/012041 ◽

2021 ◽

Vol 767 (1) ◽

pp. 012041

Author(s):

A M Hamud ◽

H Z M Shafri ◽

N S N Shaharum

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Expansion ◽

Google Earth ◽

Land Cover Changes ◽

Land Use Land Cover ◽

Google Earth Engine

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Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽

10.3390/land10020173 ◽

2021 ◽

Vol 10 (2) ◽

pp. 173

Author(s):

Changjun Gu ◽

Yili Zhang ◽

Linshan Liu ◽

Lanhui Li ◽

Shicheng Li ◽

...

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Land Cover ◽

Ecosystem Service ◽

Forest Cover ◽

Google Earth ◽

Forest Cover Change ◽

Sacred Landscape ◽

Lulc Changes ◽

Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

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