TOPOLOGICAL RELATIONS-BASED DETECTION OF SPATIAL INCONSISTENCY IN GLOBELAND30

Land cover is one of the fundamental data sets on environment assessment, land management and biodiversity protection, etc. Hence, data quality control of land cover is extremely critical for geospatial analysis and decision making. Due to the similar remote-sensing reflectance for some land cover types, omission and commission errors occurred in preliminary classification could result to spatial inconsistency between land cover types. In the progress of post-classification, this error checking mainly depends on manual labour to assure data quality, by which it is time-consuming and labour intensive. So a method required for automatic detection in post-classification is still an open issue. From logical inconsistency point of view, an inconsistency detection method is designed. This method consist of a grids extended 4-intersection model (GE4IM) for topological representation in single-valued space, by which three different kinds of topological relations including disjoint, touch, contain or contained-by are described, and an algorithm of region overlay for the computation of spatial inconsistency. The rules are derived from universal law in nature between water body and wetland, cultivated land and artificial surface. Through experiment conducted in Shandong Linqu County, data inconsistency can be pointed out within 6 minutes through calculation of topological inconsistency between cultivated land and artificial surface, water body and wetland. The efficiency evaluation of the presented algorithm is demonstrated by Google Earth images. Through comparative analysis, the algorithm is proved to be promising for inconsistency detection in land cover data.

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Dynamics of land use and land cover changes in Huluka watershed of Oromia Regional State, Ethiopia

ENVIRONMENTAL SYSTEMS RESEARCH ◽

10.1186/s40068-021-00218-4 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Gemechu Shale Ogato ◽

Amare Bantider ◽

Davide Geneletti

Keyword(s):

Land Use ◽

Sustainable Development ◽

Land Cover ◽

Water Body ◽

Average Rate ◽

Land Cover Changes ◽

Cultivated Land ◽

Forest Land ◽

Planning And Management ◽

Bare Land

Abstract Background Land use and land cover changes in urbanized watersheds of developing countries like Ethiopia are underpinned by the complex interaction of different actors, driving forces, and the land itself. Land conversion due to residential development, economic growth, and transportation is identified as the most serious environmental pressure on urbanized landscapes of the world. It results in the degradation of natural vegetation and significant increases in impervious surfaces. The purpose of the study was to analyze spatio-teporal changes in land use and land cover in the Huluka watershed with implications to sustainable development in the watershed. Results Forest land, cultivated land, urban built-up, bush/shrub land, bare land, grassland, and water body were identified as the seven types of land use and land cover in the Huluka watershed. Forest land decreased by 59.3% at an average rate of 164.52 ha/year between 1979 and 2017. Bush/ shrub land decreased by 68.2% at an average rate of 318.71 ha/year between 1979 and 2017. Grassland decreased by 32.7% at an average rate of 228.65 ha/year between 1979 and 2017. Water body decreased by 5.1% at an average rate of 1.06 ha/year between 1979 and 2017. Urban built-up area increased by 351% at an average rate of 16.20 ha/year between 1979 and 2017. Cultivated land increased by 105.3% at an average rate of 692.76 ha/year between 1979 and 2017. Bare land increased by 41.9% at an average rate of 4.00 ha/year between 1979 and 2017. Infrastructural and agricultural expansion, increased demand for wood, local environmental and biophysical drivers, rapid human population growth, economic drivers, technological drivers, policy and institutional drivers, and local socio-cultural drivers were perceived by residents as drivers of land use and land cover changes. Increased flooding risk, increased soil erosion, increased sedimentation into water resources like lakes and rivers, decrease in soil fertility, loss of biodiversity, loss of springs, decrease in annual rainfall, and increase in heat during the dry season were perceived by residents as negative local effects of land use and land cover changes. Conclusions Changes in land use and land cover in the study water shade imply the need for integrating sustainable watershed planning and management into natural resources management strategies. In other words, practices of appropriate land use planning and management, family planning, participatory planning and management, appropriate environmental impact assessment (EIA), and proper planning and management of development projects and programmes are of paramount importance to promote sustainable development in the Huluka watershed and beyond.

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Land use/Land Cover and Change Detection in Chikodi Taluk, Belagavi District, Karnataka using Object based Image Classification

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1290.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 1222-1227

Keyword(s):

Land Use ◽

Land Cover ◽

Change Detection ◽

Image Classification ◽

Water Body ◽

Accuracy Assessment ◽

Confusion Matrix ◽

Google Earth ◽

Land Use Land Cover ◽

Object Based

The study examines land use land cover and change detection in Chikodi taluk, Belagavi district, Karnataka. Land use land cover plays an important role in the study of global change. Due to fast urbanization there is variation in natural resources such as water body, agriculture, wasteland land etc. These environment problems are related to land use land cover changes. And for the sustainable development it is mandatory to know the interaction of human activities with the environment and to monitor the change detection. In present study for image classification Object Based Image Analysis (OBIA) method was adapted using multi-resolution segmentation for the year 1992, 1999 and 2019 imagery and classified into four different classes such as agriculture, built-up, wasteland and water-body. Random points (200) were generated in ArcGIS environment and converted points into KML layer in order to open in Google Earth. For the accuracy assessment confusion matrix was generated and result shows that overall accuracy of land use land cover for 2019 is 83% and Kappa coefficient is 0.74 which is acceptable. These outcomes of the result can provide critical input to decision making environmental management and planning the future.

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MAPPING WETLANDS OF DONGTING LAKE IN CHINA USING LANDSAT AND SENTINEL-1 TIME SERIES AT 30M

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1971-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1971-1976 ◽

Cited By ~ 1

Author(s):

L. Xing ◽

X. Tang ◽

H. Wang ◽

W. Fan ◽

X. Gao

Keyword(s):

Time Series ◽

Land Cover ◽

Water Body ◽

Land Surface ◽

Dongting Lake ◽

Sensor Data ◽

Optical Data ◽

Svm Classifier ◽

Threshold Method ◽

Land Cover Types

Mapping and monitoring wetlands of Dongting lake using optical sensor data has been limited by cloud cover, and open access Sentinal-1 C-band data could provide cloud-free SAR images with both have high spatial and temporal resolution, which offer new opportunities for monitoring wetlands. In this study, we combined optical data and SAR data to map wetland of Dongting Lake reserves in 2016. Firstly, we generated two monthly composited Landsat land surface reflectance, NDVI, NDWI, TC-Wetness time series and Sentinel-1 (backscattering coefficient for VH and VV) time series. Secondly, we derived surface water body with two monthly frequencies based on the threshold method using the Sentinel-1 time series. Then the permanent water and seasonal water were separated by the submergence ratio. Other land cover types were identified based on SVM classifier using Landsat time series. Results showed that (1) the overall accuracies and kappa coefficients were above 86.6&thinsp;% and 0.8. (3) Natural wetlands including permanent water body (14.8&thinsp;%), seasonal water body (34.6&thinsp;%), and permanent marshes (10.9&thinsp;%) were the main land cover types, accounting for 60.3&thinsp;% of the three wetland reserves. Human-made wetlands, such as rice fields, accounted 34.3&thinsp;% of the total area. Generally, this study proposed a new flowchart for wetlands mapping in Dongting lake by combining multi-source remote sensing data, and the use of the two-monthly composited optical time series effectively made up the missing data due to the clouds and increased the possibility of precise wetlands classification.

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Land Cover Changes and Their Driving Mechanisms in Central Asia from 2001 to 2017 Supported by Google Earth Engine

Remote Sensing ◽

10.3390/rs11050554 ◽

2019 ◽

Vol 11 (5) ◽

pp. 554 ◽

Cited By ~ 17

Author(s):

Yunfeng Hu ◽

Yang Hu

Keyword(s):

Land Cover ◽

Central Asia ◽

Industrial Development ◽

Google Earth ◽

Natural Vegetation ◽

Cultivated Land ◽

Driving Mechanisms ◽

Google Earth Engine ◽

Landsat Satellite ◽

Land Changes

Limited research has been published on land changes and their driving mechanisms in Central Asia, but this area is an important ecologically sensitive area. Supported by Google Earth Engine (GEE), this study used Landsat satellite imagery and selected the random forest algorithm to perform land classification and obtain the annual land cover datasets of Central Asia from 2001 to 2017. Based on the temporal datasets, the distributions and dynamic trends of land cover were summarized, and the key factors driving land changes were analyzed. The results show that (1) the obtained land datasets are reliable and highly accurate, with an overall accuracy of 0.90 ± 0.01. (2) Grassland and bareland are the two most prominent land cover types, with area proportions of 45.0% and 32.9% in 2017, respectively. Over the past 17 years, bareland has displayed an overall reduction, decreasing by 2.6% overall. Natural vegetation (grassland, forest, and shrubland), cultivated land, water bodies and wetlands have displayed increasing trends at different rates. (3) The amount of precipitation and degree of drought are the driving factors that affect natural vegetation. The changes in cultivated land are mainly affected by precipitation and anthropogenic drivers. The effects of increasing urban populations and expanding industrial development are the factors driving the expansion of urban regions. The advantages and uncertainties arising from the land mapping and change detection method and the complexity of the driving mechanisms are also discussed.

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COMPARISON AND ANALYSIS OF THE ACCURACY OF GEE PLATFORM PIXEL-BASED SUPERVISED CLASSIFICATION-TAKING SHANDONG PROVINCE AS AN EXAMPLE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w20-41-2019 ◽

2019 ◽

Vol XLII-4/W20 ◽

pp. 41-47

Author(s):

C. L. Jia ◽

J. Chen ◽

H. Wu

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Classification Accuracy ◽

Supervised Classification ◽

Shandong Province ◽

Test Accuracy ◽

Cultivated Land ◽

Consistency Ratio ◽

Spatial Consistency ◽

Artificial Surface

Abstract. Remote sensing is going through a basic transformation, in which a wide array of data-rich applications is gradually taking the place of methods interpreting one or two imageries. These applications have been greatly facilitated by Google Earth Engine (GEE), which provides both imagery access and a platform for advanced analysis techniques. Within the field of land cover classification, GEE provides the ability to create fast new classifications, particularly at global extents. Despite the role of indices and other ancillary data in classification, GEE platform pixel-based supervised classification (GEE-PBSC), as a relatively fast and common classification method in remote sensing, was not directly analysed and assessed about accuracy in current researches. We ask how high the classification accuracy of GEE-PBSC is, and which type of land cover is more suitable to be classified by GEE-PBSC method with a credible accuracy. Here we adopt GEE-PBSC method to classify Landsat 5 TM imageries in Shandong province in 2010, and compare the result with GlobeLand30 product in 2010 from three aspects: type composition, type confusion and spatial consistency to assess the classification accuracy. Before the comparison, multiple cross-validation, which shows that the overall average test accuracy is about 74%, is required to ensure the reliability. The comparison experiment shows that the spatial consistency ratio of artificial surface, cultivated land and water is about 99.30%, 85.78% and 73.02% respectively. The pixel purity of artificial surface and cultivated land is about 90.26% and 81.45% respectively. The overall spatial consistency ratio is about 82.04%. Although the GEE-PBSC method can achieve high test accuracy, the result is still far from GlobeLand30 product in 2010. Because the GEE-PBSC only uses the pixel information of imageries and does not integrate other multi-source data to assist classification. In addition, classification result also shows that using GEE-PBSC to classify artificial surface and cropland has obvious advantages over other land classes, and their classification results is close to GlobeLand30.

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Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine

Applied Sciences ◽

10.3390/app10207336 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7336 ◽

Cited By ~ 1

Author(s):

Lili Lin ◽

Zhenbang Hao ◽

Christopher J. Post ◽

Elena A. Mikhailova ◽

Kunyong Yu ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Spatial Pattern ◽

Land Cover Change ◽

Human Activities ◽

Land Cover Classification ◽

Google Earth ◽

Cultivated Land ◽

Island Ecosystems ◽

Google Earth Engine

Island ecosystems are particularly susceptible to climate change and human activities. The change of land use and land cover (LULC) has considerable impacts on island ecosystems, and there is a critical need for a free and open-source tool for detecting land cover fluctuations and spatial distribution. This study used Google Earth Engine (GEE) to explore land cover classification and the spatial pattern of major land cover change from 1990 to 2019 on Haitan Island, China. The land cover classification was performed using multiple spectral bands (RGB, NIR, SWIR), vegetation indices (NDVI, NDBI, MNDWI), and tasseled cap transformation of Landsat images based on the random forest supervised algorithm. The major land cover conversion processes (transfer to and from) between 1990 and 2019 were analyzed in detail for the years of 1990, 2000, 2007, and 2019, and the overall accuracies ranged from 88.43% to 91.08%, while the Kappa coefficients varied from 0.86 to 0.90. During 1990–2019, other land, cultivated land, sandy land, and water area decreased by 30.70%, 13.63%, 3.76%, and 0.95%, respectively, while forest and built-up land increased by 30.94% and 16.20% of the study area, respectively. The predominant land cover was other land (34.49%) and cultivated land (26.80%) in 1990, which transitioned to forest land (53.57%) and built-up land (23.07%) in 2019. Reforestation, cultivated land reduction, and built-up land expansion were the major land cover change processes on Haitan Island. The spatial pattern of forest, cultivated land, and built-up land change is mainly explained by the implementation of a ‘Grain for Green Project’ and ‘Comprehensive Pilot Zone’ policy on Haitan Island. Policy and human activities are the major drivers for land use change, including reforestation, population growth, and economic development. This study is unique because it demonstrates the use of GEE for continuous monitoring of the impact of reforestation efforts and urbanization in an island environment.

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