scholarly journals Monitoring ecosystem restoration of multiple surface coal mine sites in China via Landsat images on Google Earth Engine

2020 ◽  
Author(s):  
Huihui Wangh ◽  
Miaomiao Xie ◽  
Hanting Li ◽  
Qianqian Feng ◽  
Cui Zhang
Keyword(s):  
Coal Mine ◽  
Land Surface ◽  
Vegetation Index ◽  
Ecosystem Restoration ◽  
Google Earth ◽  
Landsat Images ◽  
Surface Coal Mine ◽  
Google Earth Engine ◽  
Mine Sites ◽  
Mine Restoration

The restoration of surface mining is a key to meet the global ecosystem restoration target. With increased data accessibility and computing tool capabilities, it becomes possible to expand mine restoration monitoring from single mine sites to multiple mine sites on a large scale. This study constructed a new index, Mine Landscape Restoration Index (MLRI), by coupling Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) to simultaneously monitor the restoration of regional multiple mine sites. We analyze historical and future trends of restoration using Mann-Kendall test, Sen’ slope, and Hurst exponent for MLRI time series. The restoration effects of 46 surface coal mine sites located in the northwestern ecologically fragile region of China from 2000 to 2019 were assessed, based on 3675 Landsat images on Google Earth Engine. The results showed that MLRI was effective in identifying restoration areas and processes in surface mine sites, which was validated by high-resolution images and field investigation of mine samples. The restoration area overall percentage was significantly higher in mines started mining before 2000 than after 2000. According to the restoration effects, we clustered the 46 sites into high, medium, and low restoration area percentage clusters with 13, 11, and 22 mine sites, respectively. Individual clusters have aggregation characteristics within each mine region, but are distributed irregularly across the different six mine regions. This study provides a new approach to monitoring the restoration of surface coal mine sites and inform government managers in developing mine restoration programs and sustainable mining development plans.

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

scholarly journals The Response of Land Surface Temperature to the Changing Land-Use Land-Cover in a Mountainous Landscape under the Influence of Urbanization: Gilgit City as a case study in the Hindu Kush Himalayan Region of Pakistan

2019 ◽  
Vol 10 (3) ◽  
pp. 40-49
Author(s):  
Aftab Ahmed Khan ◽  
Syed Najam ul Hassan ◽  
Saranjam Baig ◽  
Muhammad Zafar Khan ◽  
Amin Muhammad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Linear Regression Analysis ◽  
Northern Region ◽  
Google Earth ◽  
Landsat Images

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes.

scholarly journals The Response of Land Surface Temperature to the Changing Land-Use Land-Cover in a Mountainous Landscape under the Influence of Urbanization: Gilgit City as a case study in the Hindu Kush Himalayan Region of Pakistan

2019 ◽  
Vol 10 (3) ◽  
pp. 40-49
Author(s):  
Aftab Ahmed Khan ◽  
Syed Najam ul Hassan ◽  
Saranjam Baig ◽  
Muhammad Zafar Khan ◽  
Amin Muhammad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Linear Regression Analysis ◽  
Northern Region ◽  
Google Earth ◽  
Landsat Images

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes.

scholarly journals Using Landsat 8 Images Along with Google Earth Engine for Investigation the Location of the Crashed Aircrafts

2018 ◽  
Author(s):  
Azad Rasul
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat Images ◽  
Green Area ◽  
Google Earth Engine ◽  
Index Value ◽  
Research Period

Remote sensing data and techniques utilized for various purposes including natural disasters such as earthquake as well as flood. The research aims to consume liberates Landsat 8 images for investigating crashed airplanes such as MH370. Overall approximately 300 Landsat images with less than 10% clouds utilized in addition processed through Google Engine Platform. Due to the materials as well as the color of airplane body different from the area which is a plane crashed there, moreover, it should be the characteristics of the plane shapefile different in terms of albedo, temperature as well as vegetation index value. The research observed Landsat 8 data as well as methods utilized in this research, especially, NDVI, albedo in addition to band 4, capable to distinguish between the plane and its surrounding green area. Therefore, our result confirms during the research period, there was no plane on the location as well as MH370 not crashed in this site.

scholarly journals Analisis Fenomena Pulau Panas Perkotaan Kota Bandung Menggunakan Google Earth Engine

2019 ◽  
Author(s):  
Muhammad Malik Ar-Rahiem ◽  
Muhamad Riza Fakhlevi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
Google Earth Engine

Pulau Panas Perkotaan (Urban Heat Island) adalah fenomena antropogenik akibat pengaruh urbanisasi. Kawasan perkotaan yang terbangun memiliki temperatur yang lebih hangat dibandingkan kawasan sekitarnya. Fenomena Pulau Panas Perkotaan di Kota Bandung diteliti menggunakan data Suhu Permukaan Tanah (Land Surface Temperature) yang diakuisisi dari satelit Landsat 8. Lima tahun data satelit dianalisis menggunakan piranti daring Google Earth Engine untuk menganalisis variasi temporal Pulau Panas Perkotaan di Kota Bandung dan sekitarnya. Suhu yang diakuisisi dari satelit dikonversi menjadi estimasi suhu permukaan dengan mempertimbangkan nilai Normalized Difference Vegetation Index. Hasil dari penelitian ini adalah peta persebaran rata-rata dan median suhu permukaan di Cekungan Bandung tahun 2013-2018, serta grafik seri waktu suhu permukaan di 3 jenis tata guna lahan yang mewakili daerah kota (sekitar Jalan Sudirman), hutan kota (Hutan Babakan Siliwangi), dan hutan (Tamah Hutan Raya Djuanda). Suhu rata-rata Kota Bandung pada tahun 2013-2018 adalah 26,93 oC (median seluruh data) dan 25,57oC (rata-rata seluruh data). Sementara perbandingan berdasarkan tata guna lahan; daerah kota memiliki suhu permukaan rata-rata 27,30 oC, daerah hutan kota memiliki suhu 21,31oC, dan daerah hutan memiliki suhu 18,60oC. Peta persebaran suhu panas permukaan dari citra Landsat 8 menunjukkan bahwa daerah hutan secara konsisten memiliki suhu paling rendah, diikuti dengan hutan kota, dan kemudian daerah kota menjadi area yang paling panas dengan suhu maksimal hingga 33,73oC. Penggunaan Google Earth Engine yang berbasis komputasi awan sangat memudahkan pengolahan data citra satelit dalam jumlah besar yang selama ini tidak memungkinkan dilakukan dengan cara konvensional (mengunduh dan memproses di komputer).

scholarly journals Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine

2021 ◽  
Vol 10 (9) ◽  
pp. 587
Author(s):  
Yan Guo ◽  
Haoming Xia ◽  
Li Pan ◽  
Xiaoyang Zhao ◽  
Rumeng Li ◽  
...  
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Cycle ◽  
Google Earth ◽  
Henan Province ◽  
Cropping Intensity ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas.

scholarly journals A Study of Desertification Using Remote Sensing Techniques in Basra Governorate, South Iraq

2021 ◽  
pp. 912-926
Author(s):  
Fadel Abbas Zwain ◽  
Thair Thamer Al-Samarrai ◽  
Younus I. Al-Saady
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arid Zone ◽  
Sand Dunes ◽  
Risk Index ◽  
Anthropogenic Factors ◽  
Landsat Images

Iraq territory as a whole and south of Iraq in particular encountered rapid desertification and signs of severe land degradation in the last decades. Both natural and anthropogenic factors are responsible for the extent of desertification. Remote sensing data and image analysis tools were employed to identify, detect, and monitor desertification in Basra governorate. Different remote sensing indicators and image indices were applied in order to better identify the desertification development in the study area, including the Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Salinity index (SI), Top Soil Grain Size Index (GSI) , Land Surface Temperature (LST) , Land Surface Soil Moisture (LSM), and Land Degradation Risk Index (LDI) which was used for the assessment of degradation severity .Three Landsat images, acquired in 1973, 1993, and 2013, were used to evaluate the potential of using remote sensing analysis in desertification monitoring. The approach applied in this study for evaluating this phenomenon was proven to be an effective tool for the recognition of areas at risk of desertification. The results indicated that the arid zone of Basra governorate encounters substantial changes in the environment, such as decreasing surface water, degradation of agricultural lands (as palm orchards and crops), and deterioration of marshlands. Additional changes include increased salinization with the creeping of sand dunes to agricultural areas, as well as the impacts of oil fields and other facilities.

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