scholarly journals 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

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.

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

Land ◽  
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.

scholarly journals Specifying the relationship between land use/land cover change and dryness in central Vietnam from 2000 to 2019 using Google Earth Engine

2021 ◽  
Vol 15 (02) ◽  
Author(s):  
Thy T. M. Pham ◽  
The-Duoc Nguyen ◽  
Han T. N. Tham ◽  
Thi N. K. Truong ◽  
Nguyen Lam-Dao ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Central Vietnam ◽  
Google Earth Engine ◽  
The Relationship

scholarly journals Characterizing the Up-To-Date Land-Use and Land-Cover Change in Xiong’an New Area from 2017 to 2020 Using the Multi-Temporal Sentinel-2 Images on Google Earth Engine

2021 ◽  
Vol 10 (7) ◽  
pp. 464
Author(s):  
Jiansong Luo ◽  
Xinwen Ma ◽  
Qifeng Chu ◽  
Min Xie ◽  
Yujia Cao
Keyword(s):  
Land Use ◽  
Land Cover ◽  
State Level ◽  
Google Earth ◽  
Lulc Changes ◽  
Classification Framework ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Spatio Temporal ◽  
Sentinel 2

Land use and land cover (LULC) are fundamental units of human activities. Therefore, it is of significance to accurately and in a timely manner obtain the LULC maps where dramatic LULC changes are undergoing. Since 2017 April, a new state-level area, Xiong’an New Area, was established in China. In order to better characterize the LULC changes in Xiong’an New Area, this study makes full use of the multi-temporal 10-m Sentinel-2 images, the cloud-computing Google Earth Engine (GEE) platform, and the powerful classification capability of random forest (RF) models to generate the continuous LULC maps from 2017 to 2020. To do so, a novel multiple RF-based classification framework is adopted by outputting the classification probability based on each monthly composite and aggregating the multiple probability maps to generate the final classification map. Based on the obtained LULC maps, this study analyzes the spatio-temporal changes of LULC types in the last four years and the different change patterns in three counties. Experimental results indicate that the derived LULC maps achieve high accuracy for each year, with the overall accuracy and Kappa values no less than 0.95. It is also found that the changed areas account for nearly 36%, and the dry farmland, impervious surface, and other land-cover types have changed dramatically and present varying change patterns in three counties, which might be caused by the latest planning of Xiong’an New Area. The obtained 10-m four-year LULC maps in this study are supposed to provide some valuable information on the monitoring and understanding of what kinds of LULC changes have taken place in Xiong’an New Area.

scholarly journals BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine

2018 ◽  
Vol 10 (9) ◽  
pp. 1455 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey Cardille ◽  
Michael Coe
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Google Earth ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Data Set ◽  
Mato Grosso ◽  
Order Of Magnitude ◽  
Advanced Analysis ◽  
Google Earth Engine

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.

scholarly journals LAND USE/LAND COVER CHANGE PREDICTION USING MULTI-TEMPORAL SATELLITE IMAGERY AND MULTI-LAYER PERCEPTRON MARKOV MODEL

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 99-105
Author(s):  
H. T. T. Nguyen ◽  
T. A. Pham ◽  
M. T. Doan ◽  
P. T. X. Tran
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Land Use Land Cover ◽  
Multi Layer Perceptron ◽  
Time Data ◽  
Landsat Images ◽  
Lulc Changes ◽  
The Future ◽  
Over Time

Abstract. This paper aims to predict the trend of land use land cover (LULC) changes in Dak Nong province over time. Data from Landsat images captured in 2009, 2015, and 2018 was employed to analyze and predict the spatial distributions of LULC categories. The Random Forest (RF) was adopted to classify the images into ten different LULC classes. Besides, integration of Multi-Layer Perceptron Markov Neural Network (MLP-NN) with Markov Chain (MC) was applied to predict the future LULC changes in the region based on the change detection over the previous years. For all classified images, overall accuracy (OA) ranged from 77.35% to 84.55% with kappa (K) coefficient index ranging from 0.75 to 0.8. The results revealed that the annual population growth together with social-economic development was regarded as major drives for land conversion in the area. The predicted map showed a significant decrease trend inthe forest classes by 2025, accounting for 23 thousand ha. However, residential areas, rubber, and agricultural land classes are predicted to rise to 460 ha, 3,000 ha, and 20,000 ha, respectively. The simulated model and calibrated area data may be a vital contribution to sustainable development efforts of the local based on the dynamics of LULC and future LULC change scenarios. Overall, ascertaining the complex interface related to changes in land use and its major drivers over time provides useful information predict to explore the future trend of LULC changes, establish alternative land-use schemes and serve as guidelines for urban planning policymakers.

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