scholarly journals Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia

2020 ◽  
Vol 6 (4) ◽  
pp. 2487-2493 ◽  
Author(s):  
Hazem T. Abd El-Hamid ◽  
Guan Hong
Keyword(s):  
Remote Sensing ◽  
Spectral Data ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Salt Content ◽  
Soil Salinization ◽  
Northern Region ◽  
Semiarid Region ◽  
First Order ◽  
Remote Sensing Monitoring

Abstract Soil salinization affects negatively on agricultural productivity in the semiarid region of Ningxia. In this study, the performance of inversion model to determine soil salinization was assessed using some analysis and reflectance of wavelength. About 42 vegetation samples and 42 soil samples were collected for model extraction. Hyper-spectral data processing method was used to analyze spectral characteristics of different levels of salinization area vegetation. Spectral data were transformed in 16 different approaches, including root mean squares, logarithm, inversion logarithm, and first-order differentiation. After the transformation, the obtained soil and vegetation characteristics spectra correlate well with soil salt content, built soil index, and many vegetation indices. Nonlinear regression was employed to establish soil salinization remote sensing monitoring model. By comparing various spectral transformations, the first-order differential of soil spectral was the most sensitive to soil salinization degrees. The model of the current research was based on salinity index (SI) and improved soil-adjusted vegetation index (MSAVI). The correlation between simulated values and measured values was 0.758. Therefore, remote sensing monitoring derived from MSAVI–SI can greatly improve the dynamic and periodical monitoring of soil salinity in the study area.

scholarly journals Remote Sensing Monitoring and Driving Force Analysis of Salinized Soil in Grassland Mining Area

2022 ◽  
Vol 14 (2) ◽  
pp. 741
Author(s):  
Zhenhua Wu ◽  
Mingliang Che ◽  
Shutao Zhang ◽  
Linghua Duo ◽  
Shaogang Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Salt Content ◽  
Feature Space ◽  
Mining Area ◽  
Soil Salinization ◽  
Driving Factors ◽  
Remote Sensing Monitoring ◽  
Saline Land ◽  
Semi Arid ◽  
Salinized Soil

To deal with the problem of soil salinization that exists widely in semi-arid grassland, the Shengli Coalfield in Xilinhot City was selected as the study area. Six periods of Landsat remote sensing data in 2002, 2005, 2008, 2011, 2014, and 2017 were used to extract the salinity index (SI) and surface albedo to construct the SI-Albedo feature space. The salinization monitoring index (SMI) was used to calculate and classify the soil salinization grades in the study area. The soil salinization status and its dynamic changes were monitored and analyzed. Combined with the logistic regression model, the roles of human and natural factors in the development of soil salinization were determined. The results were as follows: (1) The SMI index constructed using the SI-Albedo feature space is simple and easy to calculate, which is conducive to remote sensing monitoring of salinized soil. R2 of the SMI and soil salt content in the 2017 data from the study area is 0.7313, which achieves good results in the quantitative analysis and monitoring of soil salinization in the Xilinhot Shengli Coalfield. (2) The study area is a grassland landscape. However, grassland landscapes are decreasing year by year, and town landscapes, mining landscapes, and road landscapes are greatly increased. The areas of soil salinization reversion in the Shengli mining area from 2002–2005, 2005–2008, 2008–2011, 2011–2014, 2014–2017, and 2002–2017 were 65.64 km2, 1.03 km2, 18.44 km2, 0.9 km2, 7.52 km2, and 62.33 km2, respectively. The overall trend of soil salinization in the study area was reversed from 2002 to 2017. (3) The driving factors of salinized land from 2002 to 2008 are as follows: the distance to the nearest town landscape > the distance to the nearest mining landscape > the distance to the nearest road landscape. The driving factors of salinized land from 2008 to 2017 are as follows: the distance to nearest mining landscape > the distance to the nearest water landscape > the distance to nearest town landscape > altitude > aspect. Coal exploitation and town expansion have occupied a large amount of saline land, and petroleum exploitation and abandoned railway test sites have intensified the development of saline land. This study provides a reference for the treatment and protection of soil salinization in semi-arid grassland mining areas.

scholarly journals Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Yahui Guo ◽  
Jing Zeng ◽  
Wenxiang Wu ◽  
Shunqiang Hu ◽  
Guangxu Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Linear Trend ◽  
Spectral Characteristics ◽  
Growth Conditions ◽  
Temporal Changes ◽  
Vegetation Coverage ◽  
Dynamic Changes ◽  
Spatial And Temporal Changes ◽  
Temporal And Spatial

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

scholarly journals ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 223-227 ◽  
Author(s):  
Yi-Ta Hsieh ◽  
Shou-Tsung Wu ◽  
Chaur-Tzuhn Chen ◽  
Jan-Chang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Aerial Images ◽  
Aerial Image ◽  
Optical Remote Sensing ◽  
Shadow Area ◽  
Vegetation Indexes ◽  
Research Findings

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

scholarly journals The soil-adjusted vegetation index for soil salinity assessment in Uzbekistan

2020 ◽  
Vol 26 (3) ◽  
pp. 324-333
Author(s):  
Nozimjon Teshaev ◽  
Bunyod Mamadaliyev ◽  
Azamjon Ibragimov ◽  
Sayidjakhon Khasanov
Keyword(s):  
Soil Salinity ◽  
Information Technologies ◽  
Vegetation Index ◽  
Arable Land ◽  
Salt Content ◽  
Secondary Data ◽  
Soil Surface ◽  
Soil Salinization ◽  
Arid Zones ◽  
Agronomic Practices

Soil salinization, as one of the threats of land degradation, is the main environmental issue in Uzbekistan due to its aridic climate. One of the most vulnerable areas to soil salinization is Sirdarya province in Uzbekistan. The main human-induced causes of soil salinization are the insufficient operation of drainage and irrigation systems, irregular observations of the agronomic practices, and non-efficient on-farm water use. All of these causes considerably interact with the level of the groundwater, leading to an increase in the level of soil salinity. The availability of historical data on actual soil salinity in agricultural lands helps in formulating validated generic state-of-the-art approaches to control and monitor soil salinization by remote sensing and geo-information technologies. In this paper, we hypothesized that the Soil-Adjusted Vegetation Index-based results in soil salinity assessment give statistically valid illustrations and salinity patterns. As a study area, the Mirzaabad district was taken to monitor soil salinization processes since it is the most susceptible territory of Sirdarya province to soil salinization and provides considerably less agricultural products. We mainly formulated this paper based on the secondary data, as we downloaded satellite images and conducted an experiment against the in-situ method of soil salinity assessment using the Soil-Adjusted Vegetation Index. As a result, highly saline areas decreased by a factor of two during the studied period (2005–2014), while non-saline areas increased remarkably from a negligible value to over 10 000 ha. Our study showed that arable land suitability for agricultural purposes has been improving year by year, and our research held on this district also proved that there was a gradual decrease in high salt contents on the soil surface and land quality has been improved. The methodology has proven to be statistically valid and significant to be applied to other arid zones for the assessment of soil salinity. We assume that our methodology is surely considered as a possible vegetation index to evaluate salt content in arable land of either Uzbekistan or other aridic zones and our hypothesis is not rejected by this research.

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Evaluation of Soil Degradation Based on High Resolution Remote Sensing Data

2005 ◽  
pp. 145-148
Author(s):  
Péter Burai ◽  
János Tamás
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Soil Salinity ◽  
Strong Correlation ◽  
Soil Degradation ◽  
Vegetation Index ◽  
Near Infrared ◽  
Salt Content ◽  
Remote Sensing Data ◽  
Test Site

Soil salinity is the main problem of soil degradation in the Grate Plain with cultivated area of 20% affected. Its influence is accelerated on the water managed and irrigated lands. Remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. We have chosen a farm where intensive crop cultivation takes place as a test site as soil degradation can be intensive as a result of land use and irrigation. In order to evaluate soil salt content and biomass analysis, we gathered detailed data from an 100x250 m area. We analyzed the salinity property of the samples. In our research we used a TETRACAM ADC multispectral camera to take high resolution images (0,2-0,5 m) of low altitude (300-500 m). A Normalized Vegetation Index was computed from near infrared (750-950 nm) and red (620-750 nm) bands. This data was compared with the samples of investigated area. Analyzing the images, we evaluated image reliability, and the connection between the bands and the soil properties (pH, salt content). A strong correlation observed between NDVI and soil salinity (EC) makes the multispectral images suitable for construction of salinity map. A further strong correlation was determined between NDVI and yield.

scholarly journals STUDY ON REMOTE SENSING IMAGE CHARACTERISTICS OF ECOLOGICAL LAND: CASE STUDY OF ORIGINAL ECOLOGICAL LAND IN THE YELLOW RIVER DELTA

2018 ◽  
Vol XLII-3 ◽  
pp. 35-38
Author(s):  
G. Q. An
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Vegetation Index ◽  
Yellow River ◽  
Spectral Characteristics ◽  
Texture Features ◽  
Yellow River Delta ◽  
River Delta ◽  
The Yellow River ◽  
The Yellow River Delta

Takes the Yellow River Delta as an example, this paper studies the characteristics of remote sensing imagery with dominant ecological functional land use types, compares the advantages and disadvantages of different image in interpreting ecological land use, and uses research results to analyse the changing trend of ecological land in the study area in the past 30 years. The main methods include multi-period, different sensor images and different seasonal spectral curves, vegetation index, GIS and data analysis methods. The results show that the main ecological land in the Yellow River Delta included coastal beaches, saline-alkaline lands, and water bodies. These lands have relatively distinct spectral and texture features. The spectral features along the beach show characteristics of absorption in the green band and reflection in the red band. This feature is less affected by the acquisition year, season, and sensor type. Saline-alkali land due to the influence of some saline-alkaline-tolerant plants such as alkali tent, Tamarix and other vegetation, the spectral characteristics have a certain seasonal changes, winter and spring NDVI index is less than the summer and autumn vegetation index. The spectral characteristics of a water body generally decrease rapidly with increasing wavelength, and the reflectance in the red band increases with increasing sediment concentration. In conclusion, according to the spectral characteristics and image texture features of the ecological land in the Yellow River Delta, the accuracy of image interpretation of such ecological land can be improved.

Application of Spatial Sampling to Remote Sensing Monitoring of Forest Cover Area

2012 ◽  
Vol 610-613 ◽  
pp. 3732-3737 ◽  
Author(s):  
Ji Ping Zhang ◽  
Lin Bo Zhang ◽  
Bin Gong
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Random Sampling ◽  
Vegetation Index ◽  
Forest Cover ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Spatial Sampling ◽  
Remote Sensing Monitoring ◽  
Study Results

This study combines the sampling technique, geographic information system and remote sensing technique to conduct a sampling survey on forest cover area of Jinggangshan National Nature Reserve in China on the basis of TM remote sensing image. The spatial simple random sampling, spatial stratified sampling and sandwich sampling model are respectively utilized to establish the sampling design. For the spatial simple random sampling model, the spatial autocorrelation analysis method is adopted to determine the spatial autocorrelation coefficient through calculating Moran's I index, while in the spatial stratified sampling and sandwich sampling model, the yearly maximum NDVI (Normalized Difference Vegetation Index) is utilized to conduct the spatial stratification. Through comparison of the sampling accuracy of three sampling models, a higher precision and more reasonable sampling method and sampling model is provided for remote sensing monitoring of forest cover area. The study results show that: sandwich sampling model is featured as the highest sampling accuracy, followed by the spatial stratified sampling and simple random sampling. Under the requirement of same precision, sandwich spatial sampling model can reduce quantity of the sampling points, and create all kinds of report units according to demands of different spatial area, so it is featured as the better suitability.

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