Environmental degradation in the transnational area of Changbai Mountain based on multiple remote sensing data

Rapidly and effectively assessing environmental degradation is essential for promoting regional sustainable development in the transnational area of Changbai Mountain (TACM). However, comprehensively understanding environmental degradation in the TACM is still inadequate. In this study, we developed an environmental degradation index (EDI) by using multiple remote sensing data, including enhanced vegetation index (EVI), gross primary productivity (GPP), land surface temperature (LST), and MODIS surface reflectance products. We then evaluated its performance comparing with the remote sensing ecological index (RSEI), and assessed the environmental degradation across the whole TACM, in the subregions of China, the Democratic People’s Republic of Korea (DPRK), and Russia during 2000-2019. The results indicated that the EDI had the advantages of simplicity and rapidity, which can assess the environmental degradation in the TACM across long-time scales and large spatial extent. The TACM experienced a downward trend of environmental changes from 2000 to 2019. Degraded environment areas (49,329.50 km2) accounted for 30.09% of the entire TACM. The largest area of the degraded environment was on the DPRK’s side (i.e., 25,395.00 km2), which was 5.6 times larger than that on the Russian side and 1.3 times larger than that on the Chinese side. Hotspot areas that experienced significant environmental degradation just covered 17.69% of the land area of the TACM, the area of environmental degradation in them accounted for 33.89% of the total degraded environment across the whole TACM. We suggest that international cooperation policies and measures ought to be enacted to promote regional sustainable development.

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DETERMINATION OF VEGETATION INDEX, LAND SURFACE TEMPERATURE AND PRECIPITATION AMOUNTS USING REMOTE SENSING DATA

JOURNAL OF AGRO PROCESSING ◽

10.26739/2181-9904-2020-5-1 ◽

2020 ◽

Vol 5 (2) ◽

pp. 4-10

Author(s):

Rashid Jaksibaev ◽

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Remote Sensing Data ◽

Sensing Data ◽

Temperature And Precipitation

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Modeling regional evaporation through ANFIS incorporated solely with remote sensing data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-6153-2013 ◽

2013 ◽

Vol 10 (5) ◽

pp. 6153-6192

Author(s):

F.-J. Chang ◽

W. Sun

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Vegetation Index ◽

Remote Sensing Data ◽

Data Allocation ◽

Remotely Sensed Data ◽

Estimation Model ◽

Landsat Images ◽

Inference System ◽

Sensing Data

Abstract. The study aims to model regional evaporation that possesses the ability to present the spatial distribution of evaporation across the whole Taiwan by the adaptive network-based fuzzy inference system (ANFIS) based solely on remote sensing data. The remote sensing data used in this study consist of Landsat image products including Enhanced Vegetation Index (EVI) and land surface temperature (LST). The model construction is designed through two types of data allocation (temporal and spatial) driven with the same ten-year data of EVI and LST derived from Landsat images. Evidences indicate the estimation model based solely on remotely sensed data can effectively detect the spatial variation of evaporation and appropriately capture the evaporation trend with acceptable errors of about 1 mm day−1. The results also demonstrate the composite of EVI and LST input to the proposed estimation model improves the accuracy of estimated evaporation values as compared with the model using LST as the only input, which reveals EVI indeed benefits the estimation process. The results suggest Model-T (temporal input allocation) is suitable for making island-wide evaporation estimation while Model-S (spatial input allocation) is suitable for making evaporation estimation at ungauged sites. An island-wide evaporation map for the whole study area (Taiwan Island) is then derived. It concludes the proposed ANFIS model incorporated solely with remote sensing data can reasonably well generate evaporation estimation and is reliable as well as easily applicable for operational estimation of evaporation over large areas where the network of ground-based meteorological gauging stations is not dense enough or readily available.

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Exploring the Relationship between LST, LULC and NDVI in Saïss Plain using Geospatial Techniques

E3S Web of Conferences ◽

10.1051/e3sconf/202131404001 ◽

2021 ◽

Vol 314 ◽

pp. 04001

Author(s):

Manal El Garouani ◽

Mhamed Amyay ◽

Abderrahim Lahrach ◽

Hassane Jarar Oulidi

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Arable Land ◽

Remote Sensing Data ◽

Landsat Images ◽

Gis And Remote Sensing ◽

Sensing Data

Land use/land cover (LULC) change has been confirmed that have a significant impact on climate through various pathways that modulate land surface temperature (LST) and precipitation. However, there are no studies illustrated this link in the Saïss plain using remote sensing data. Thus, the aim of this study is to monitor the LST relationship between LULC and vegetation index change in the Saïss plain using GIS and Remote Sensing Data. We used 18 Landsat images to study the annual and interannual variation of LST with LULC (1988, 1999, 2009 and 2019). To highlight the effect of biomass on LST distribution, the Normalized Difference Vegetation Index (NDVI) was calculated, which is a very good indicator of biomass. The mapping results showed an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. Based on statistical analyzes, the LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of LULC type. The variation of land surface temperature with NDVI shows a negative correlation. This explains the increase in the surface temperature in rangelands and arable land while it decreases in irrigated crops and arboriculture.

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The Sector Development at Tanjung Sakti, Mount Dempo, Bengkulu by Using Integration of Remote Sensing Data and TOPEX Gravity Satellite

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/873/1/012015 ◽

2021 ◽

Vol 873 (1) ◽

pp. 012015

Author(s):

Zahrah Athirah ◽

Muhammad Dhery Mahendra

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Land Surface ◽

Vegetation Index ◽

Hot Springs ◽

Gravity Data ◽

Remote Sensing Data ◽

Landsat 8 ◽

Volcanic Complex ◽

Sensing Data

Abstract Mount Dempo is the highest volcano in South Sumatra, which lies between the Bukit Barisan mountains and Gumai. The mountain located in Dempo Makmur Village, Sub-district of Pagar Alam, Lahat Regency, South Sumatra is located at an altitude of 3173 meters above sea level with coordinates of 4.03 ° S 103.13 °E. Mount Dempo’s morphology is formed by pyroclastic deposits consisting of Tuff and Sand rocks. Mount Dempo’s vegetation is dominated by Cassia sp. and Camellia sinensis for upper vegetation, while Strobilanthes hamiltoniana and Strophanthus membranifolium dominate the undergrowth. The purpose of this study is to identify geological structures to predict geothermal prospect areas by integrating remote sensing data and TOPEX Gravity Satellite Data. The remote sensing data used in this study is Landsat 8. This data is used to analyze Land Surface Temperature (LST) from a single thermal infrared band, surface emissivity based on Normalization Difference Vegetation Index (NDVI) from the study area and determine structure delineation. Gravity Satellite Data is used to map gravity anomalies in the volcanic complex of Mount Dempo. Gravity data processing produces a high anomaly zone in the northern part of the study area and is predicted as a prospect area because it is assumed to be related to the plutonic body. High density contrast indicates that there is an error in that area. In line with the error, there are several hot springs because the error serves as a pathway for geothermal fluid to rise to the surface. The study believes that with all the facts stated above, the spots which are located in Tanjung Sakti, Mount Dempo district are very prospective to be developed as a geotourism complex, in which could also increase the welfare of the local citizens.

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Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey

The Scientific World JOURNAL ◽

10.1155/2014/142939 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 25

Author(s):

Osman Orhan ◽

Semih Ekercin ◽

Filiz Dadaser-Celik

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Satellite Remote Sensing ◽

Vegetation Index ◽

Salt Lake ◽

Remote Sensing Data ◽

Lake Basin ◽

Sensing Data

The main purpose of this paper is to investigate multitemporal land surface temperature (LST) changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI), vegetation condition index (VCI), and temperature vegetation index (TVX) were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable within situmeasurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R2) values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about2∘C) in the Salt Lake Basin area during the 28-year period (1984–2011). Analysis of air temperature data also showed increases at a rate of 1.5–2∘Cduring the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.

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Method of drought evaluation for a territory by the land surface temperature and vegetation relationship from remote sensing data

Science & Technology Development Journal - Engineering and Technology ◽

10.32508/stdjet.v2i4.610 ◽

2020 ◽

Vol 2 (4) ◽

pp. First

Author(s):

Van Tran-Thi ◽

Ha Nguyen Ngan ◽

Viet Ha Quoc ◽

Long Nguyen Hoang ◽

Bao Ha Duong Xuan

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Current Trend ◽

Remote Sensing Data ◽

Food Sources ◽

Sensing Data ◽

Vegetation Characteristics

Facing the current trend of climate change, which is difficult to control, human life, as well as food sources, are increasingly seriously threatened by droughts that occur more frequently. Understanding the region's drought will help people avoid risks. The paper presents research on the method of assessing drought situations based on the integration of land surface temperature and vegetation characteristics in the drought index according to the Temperature Vegetation Dryness Index TVDI from remote sensing data. Landsat satellite images were used with image processing methods to test the drought assessment method for the test area of Di Linh district, Lam Dong province. The study period was the dry season in 2018. Reflective bands were used to determine vegetation cover as representative of soil moisture supplying water to crops. Vegetation characteristics are represented by Normalized Differential Vegetation Index NDVI. In contrast, the thermal infrared band is used to calculate the surface temperature. The results showed that the bare land and sparsely populated areas exhibited a higher level of drought than the vegetated areas. The research results demonstrate the ability of remote sensing technology to support the monitoring of drought in a space for a region, in order to help people make the right management decisions in planning.

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Integration of Microwave and Optical/Infrared Derived Datasets from Multi-Satellite Products for Drought Monitoring

Water ◽

10.3390/w12051504 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1504

Author(s):

Zhengdong Wang ◽

Peng Guo ◽

Hong Wan ◽

Fuyou Tian ◽

Linjiang Wang

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Water ◽

Land Surface ◽

North China ◽

Vegetation Index ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Sensing Data ◽

Dryness Index

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the advantages of microwave and optical remote sensing data to improve the original Temperature–Vegetation–Soil Moisture Dryness Index (TVMDI) model, and was constructed based on the Modified Soil-Adjusted Vegetation Index (MSAVI), land surface temperature (LST), and downscaled soil moisture (SM) as the three-dimensional axes. The global land data assimilation system (GLDAS) SM, meteorological data and surface water were used to evaluate and verify the monitoring results. The results showed that iTVMDI had a higher negative correlation with GLDAS SM (R = −0.73) than TVMDI (R = −0.55). Additionally, the iTVMDI was well correlated with both precipitation and surface water, with mean correlation coefficients (R) of 0.65 and 0.81, respectively. Overall, the accuracy of drought estimation can be significantly improved by using multi-source satellite data to measure the required surface variables, and the iTVMDI is an effective method for monitoring the spatial and temporal variations of drought.

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REMOTE SENSING DATA IN MUNICIPAL GOVERNMENT

Stavební obzor - Civil Engineering Journal ◽

10.14311/cej.2021.04.0073 ◽

2021 ◽

Vol 30 (4) ◽

Author(s):

Jaroslav Nýdrle

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Vegetation Index ◽

Spatial Information ◽

Relevant Literature ◽

Remote Sensing Data ◽

Landsat 8 ◽

Remotely Sensed Data ◽

Sensing Data ◽

Sentinel 2

This article focuses on the issue of using data obtained through remote sensing methods in the administrative district of the municipality with extended powers of Liberec (the Czech Republic). The first part of the article discusses the question of using Earth remote sensing data for city agendas in general. Then, it presents a questionnaire, created for evaluating the needs of the Liberec municipality. This questionnaire, focusing on the use of remotely sensed data, was created on the basis of a review of relevant literature. Based on the results of the questionnaire, the following spatial information requirements were chosen to be addressed: land surface temperature map - LST (Landsat 8), vegetation index - NDVI (Sentinel 2, Planet Scope), normalized difference water index - NDWI, NDWI 2 (Sentinel 2), normalized difference built-up index - NDBI (Sentinel 2). All data obtained during the creation of this study have become part of the database of the Urban Planning and GIS Department and are available to employees of the City of Liberec.

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Urban Biophysical Quality Modelling Based On Remote Sensing Data In Semarang, Indonesia

GEOGRAPHY ENVIRONMENT SUSTAINABILITY ◽

10.24057/2071-9388-2020-173 ◽

2021 ◽

Vol 14 (3) ◽

pp. 14-23

Author(s):

Iswari Nur Hidayati ◽

Karunia Pasya Kusumawardani ◽

A. G. Ayudyanti ◽

R. R. Prabaswara

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Industrial Revolution ◽

Environmental Changes ◽

Rapid Assessment ◽

Remote Sensing Data ◽

Urban Ecosystems ◽

Residential Areas ◽

Sensing Data ◽

Housing Complex

Cities are centres of economic growth with fascinating dynamics, including persistent urbanisation that encroaches adjacent arable lands to build urban physical features and sustain services offered by urban ecosystems. Even though industrial revolution, economic dynamics, and environmental changes affect spatial feasibility for housing, complex urban growth is always followed by the development of environmentally friendly cities. However, with such quality having multiple facets, it is necessary to assess and map liveable areas from a more comprehensive and objective perspective. This study aimed to assess, map and identify the biophysical quality of an urban environment using a straightforward technique that allows rapid assessment for early detection of changes in the quality. It proposed a multi-index approach termed the urban biophysical environmental quality (UBEQ) based on spectral characteristic of remote sensing data for residential areas calculated using various data derived from remote sensing. Statistical analyses were performed to test data reliability and normality. Further, many indices were analysed, then employed as indicators in UBEQ modelling and tested with sensitivity and factor analysis to obtain the best remote sensing index in the study area. Based on PCA Results, it was found that the built-up land index and vegetation index mainly contributed to the UBEQ index. The generated model had 86.5% accuracy. Also, the study area, Semarang City, had varying UBEQ index values, from high to low levels.

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