scholarly journals Assessment of the Ecological Condition of Soil Cover Based on Remote Sensing Data: Erosional Aspect

2021 ◽  
Vol 100 ◽  
pp. 05014
Author(s):  
Andrii Achasov ◽  
Alla Achasova ◽  
Ganna Titenko ◽  
Oleg Seliverstov ◽  
Vladimir Krivtsov
Keyword(s):  
Remote Sensing ◽  
Soil Cover ◽  
Arable Land ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Water Erosion ◽  
Visible Range ◽  
Visual Interpretation ◽  
Sensing Data ◽  
Automated Method

Soil erosion by water is the most important global environmental problem. A modern system for assessing and monitoring soil erosional degradation should be based on the use of remote sensing data. This raises the issue of correct data decoding. The article proposes a method for visual interpretation of eroded soils according to the Sentinel image obtained in the visible range. The authors give some combinations of decoding signs to determine the manifestations of linear and surface water erosion from images. The article shows possible errors in decoding the manifestations of water erosion and gives an example of assessing the erosion of the soil cover based on the results of decoding the Sentinel-2 satellite image. Moderately and heavily eroded soils are reliably distinguished, the area of which, according to the interpretation data, was 2.4% of the area of arable land in the studied territory. In the future, the obtained sample of spectral images of eroded soils can be used to develop an automated method of interpretation based on the principle of "computer vision".

scholarly journals Spatial analysis of protective forest plantations based on geographic information technologies and remote sensing data

2020 ◽  
Vol 26 (2) ◽  
pp. 408-420
Author(s):  
Sergey Antonov
Keyword(s):  
Remote Sensing ◽  
Information Technologies ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Water Erosion ◽  
Geographic Information ◽  
Forest Plantations ◽  
Sensing Data ◽  
Geographic Information Technologies ◽  
Protective Forest

In many agricultural regions of Russia, significant land areas are degraded and the Stavropol Territory is no exception. Protective forest plantations play an important role in protecting the soil from deflation and water erosion. Significant areas of protective forest plantations are in unsatisfactory condition, which leads to a decrease in the effectiveness of their protective function. As a result of the study, a new methodology was developed for assessing the spatial position of protective forest plantations using geographic information technologies and remote sensing data, which has tested in the territory of the Budyonnovsky District of the Stavropol Territory. It has been established that the existing protective forest plantations in the district are not sufficiently effective in protecting the arable land from deflation. Only 5 % of protective forest plantations are located at recommended distances from each other and 10 % are at an optimal angle to the most harmful winds. The low efficiency of protective forest plantations is associated with the peculiarities of their design at the initial stages of creating a protective framework, their achievement of the maximum age, as well as significant damage resulting from human activities. To assess the effectiveness of protective forest plantations in combating water erosion, we developed an original methodology for adjusting the digital elevation model SRTM in order to eliminate local elevations of the relief at the locations of protective forest plantations. It was found that the horizontal indicator of the boundaries in the territory of the Budyonnovsky District is 11 %, which indicates that protective forest plantations in the district were created primarily to protect the arable land from deflation. The presented methodological approaches can be used to adjust existing, and design new protective forest plantations.

scholarly journals Damage Assessment of a Salt Dome in Jizan, Southwestern Saudi Arabia, Using High Spatial Resolution Remote Sensing Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Habes Ghrefat ◽  
Ahmed Hakami ◽  
Elkhedr Ibrahim ◽  
Saad Mogren ◽  
Saleh Qaysi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Spatial Resolution ◽  
Damage Assessment ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Salt Dome ◽  
Visual Interpretation ◽  
Sensing Data ◽  
Residential Neighborhood

The salt dome in Jizan, southwestern Saudi Arabia, has caused several problems related to underground dissolution, particularly in the old part of the city. Examples of these problems include surface collapse, building failure, fracturing, tilting, and road cracking. Analysis of the salt dome using X-ray diffraction (XRD) revealed the dominance of gypsum, anhydrite, and halite. This study evaluates the damage assessment using multitemporal high spatial resolution data of the GeoEye-1, and QuickBird-2 sensors. Change detection technique, textural analysis, and visual interpretation were applied to these data. Analysis of the data recorded before and after a particular damage event revealed that three neighborhoods located above the Jizan salt dome—Al-Ashaima, Shamiya, and Aljabal—were affected to the greatest extent. The entire residential neighborhood of Al-Ashaima was evacuated, and the buildings located in it were demolished. Several buildings in the Shamiya and Aljabal neighborhoods were also demolished. Therefore, high spatial remote sensing data are effective in assessing building damage and for anticipating future damage, thus benefiting decision making for the affected cities.

scholarly journals Aplikasi Penghitungan Gross Primary Production dari Data Penginderaan Jauh

2016 ◽  
pp. 31
Author(s):  
Komang Gede Kurniadi ◽  
I Putu Agung Bayupati ◽  
I Dewa Nyoman Nurweda Putra
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Automatic Data ◽  
Sensing Data ◽  
Metadata File ◽  
Production Calculation

Calculation of Gross Primary Production that utilize remote sensing data is can be done on commercial remote sensing software by manual method. The commercial remote sensing software does not provides a specific feature that allow the user to do the Gross Primary Production calculation. This research is aimed to to build a remote sensing software that can be specifically used to do the Gross Primary Production calculation for Denpasar area. This software accepts remote sensing data as an input, such as satellite image from Landsat 8 OLI and TIRS and metadata file. The formulas and supporting data that required on the Gross Primary Production calculation are implemented on software in order to make an automatic image processing software. There also some additional feature on this software such as automatic data parsing from metadata file, cropping, masking and zoom that could help user to do the Gross Primary Production calculation. The developed software is able to produce information such as Gross Primary Production  value that depicted by a figure with color segmentation, area of the segments and mean, minimum and maximum value of the Gross Primary Production.  

Spatio-temporal variation of net primary productivity in a rapidly expanding artificial woodland area based on remote-sensing data

Erdkunde ◽  
2021 ◽  
Vol 75 (3) ◽  
pp. 191-207
Author(s):  
Qi Yi ◽  
Yuting Gao ◽  
Hongrong Du ◽  
Junxu Chen ◽  
Liang Emlyn Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Meteorological Data ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Stand Age ◽  
Sensing Data ◽  
Spatio Temporal

The expansion of artificial woodlands in China has contributed significantly to regional land-cover changes and changes in the regional net primary productivity (NPP). This study used Ximeng County in the Yunnan Province as a case study to investigate the overall changes, associated amplitude, and spatio-temporal distribution of NPP from 2000–2015.The Carnegie-Ames-Stanford approach was used in the rapidly expanding artificial woodland area based on MODIS-NDVI data, meteorological data, and Landsat 5 TM data to calculate the NPP. The results show that (1) artificial woodlands experience a 10fold increase and account for 93 % of the land cover transfer, which was mainly from woodland areas. (2) The NPP was 906.2×109 gC·yr-1 in 2000 and 972.0×109 gC·yr-1 in 2015, presenting a total increase of 65.8×109 gC·yr-1 and a mean increase of 52.4 gC·m-2·yr-1 in Ximeng County. (3) The most notable NPP changes take place in the central and the western border regions, with the increasing NPP of artificial woodlands and arable land offsetting the negative effects of the decrease in woodland NPP. (4) The total NPP in the study area kept increasing, primarily due to the growing area of artificial woodlands as well as the stand age of the woods, whereas the mean value change of the NPP is mostly related to the increasing stand age. (5) The artificial woodlands increase the NPP value more than natural woodlands. While protecting and promoting ecologically valuable natural forests at the same time, it seems quite advantageous to establish regional plantations and coordinate their development on a scientific basis with a view to increasing NPP, economic development, but also the ecological stability of this mountain region. Our study reveals the changes in NPP and its distribution in a rapidly expanding area of artificial woodland in southwest China based on remote-sensing data and the CASA model, providing a decision-making basis for rational land-use management, the optimal utilization of land resources, and a county-scale assessment approach.

scholarly journals RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 565-568
Author(s):  
N. Fu ◽  
L. Sun ◽  
H. Z. Yang ◽  
J. Ma ◽  
B. Q. Liao
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Management System ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Database Management System ◽  
Sensor Data ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Effective Organization

Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.

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

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.

scholarly journals Use of Earth Remote Sensing data of monitoring the level of soil fertility

2020 ◽  
Vol 131 (2) ◽  
pp. 78-84
Author(s):  
K.S. Baktybekov ◽  
◽  
G.R. Kabzhanova ◽  
А.А. Aimbetov ◽  
M.T. Alibayeva ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Material Resources ◽  
Northern Kazakhstan ◽  
Spectral Ranges ◽  
Ground Monitoring ◽  
The Relationship

Ground monitoring of soil massifs takes a lot of time, labor and material resources, although it is the most accurate and detailed. When introducing complex methods of monitoring the soil cover, the inclusion of space technologies is mandatory.Remote sensing data carry objective information over large areas, obtained in various spectral ranges. The article discusses the possibility of using remote sensing data for mapping and monitoring changes in the soil cover of Northern Kazakhstan. On the basis of thematic processing of remote sensing data of native satellites, a spatial analysis of the content of main nutrients in the sowing layer of soils was carried out, the relationship was revealed between fertility indicators and the value of vegetation indices for test ranges of the territory of Northern Kazakhstan.

scholarly journals Land use analysis of Lake Khanka basin using remote sensing data

2021 ◽  
Vol 895 (1) ◽  
pp. 012007
Author(s):  
K Yu Bazarov ◽  
E G Egidarev ◽  
N V Mishina
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Paddy Field ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Open Pit ◽  
Geoinformation Systems ◽  
Sensing Data ◽  
Land Use Structure ◽  
Lake Khanka

Abstract The paper presents results of the analysis of the land use map compiled for transboundary Lake Khanka Basin using remote sensing data and geoinformation systems. The map reflects the distribution of 12 land categories in Lake Khanka basin in 2017 (arable land, abandoned arable land, paddy field, abandoned paddy field, shrubs and sparse growth, forest land, open pit, settlements, meadows and pastures, wet meadows and marshes, water bodies, forest cuttings and fire sites). The data of land use structure in the whole Lake’s watershed, in its Russian and Chinese parts are given. Data on the distribution of different land categories in the administrative territories of the rank of districts (Russia) and counties (China) are also presented. The analysis of land use structure showed that about 50 % of the Chinese part of the basin is covered by anthropogenically transformed natural complexes. The share of such lands in the territory of Russia amounts to 28 %. Agriculture is the most important factor in the change of natural complexes in Lake Khanka basin. Before early 1990s, the area of farmland had increased in the basin on both sides of the border, after that there was a significant reduction in cultivated lands, which had lasted for 10 years in the territory of China and for 20 years in Russia. Over the past decade, the area of cultivated areas in the basin and adjacent territories has extended again, which indicates an increase of anthropogenic impact and requires serious attention to monitoring of the ecological state of lands in the basin.

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