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 Use of Geoinformatics Techniques for the Assessment and Mapping of Soil Salinity: Concepts and Applications

2021 ◽  
Author(s):  
Olumuyiwa Idowu Ojo ◽  
Masengo Francois Ilunga
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Traditional Method ◽  
Soil Degradation ◽  
Remote Sensing Data ◽  
Field Work ◽  
Irrigated Agriculture ◽  
Salinity Data ◽  
Geoinformatics Techniques ◽  
Labour Time

Irrigated agriculture has a major impact on the environment, especially soil degradation. Soil salinity is a critical environmental problem, which has great impact on soil fertility and overall agricultural productivity. Since, soil salinity processes are highly dynamic, the methods of detecting soil salinity hazards should also be dynamic. Remote sensing data are modern tools that provide information on variation over time essential for environmental monitoring and change detection, as they also help in the reduction of conventional time-consuming and expensive field sampling methods, which is the traditional method of monitoring and assessment. This chapter thus reviewed the concepts and applications of remote sensing, GIS-assisted spatial analysis and modelling of the salinity issue in irrigation fields. Generally, compared to the labour, time and money invested in field work devoted to collecting soil salinity data and analysis, the availability and ease of acquiring satellite imagery data and analysis made this concept very attractive and efficient.

scholarly journals RANDOM-FOREST-ENSEMBLE-BASED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES AND NDSM OVER URBAN AREAS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 887-892
Author(s):  
X. F. Sun ◽  
X. G. Lin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Urban Areas ◽  
Vegetation Index ◽  
Conditional Random Field ◽  
Remote Sensing Data ◽  
Intermediate Step ◽  
Sensing Data ◽  
3D Geometry

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample’s category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

scholarly journals Soil Salinity Mapping of Urban Greenery Using Remote Sensing and Proximal Sensing Techniques; The Case of Veale Gardens within the Adelaide Parklands

2018 ◽  
Vol 10 (8) ◽  
pp. 2826 ◽  
Author(s):  
Hamideh Nouri ◽  
Sattar Chavoshi Borujeni ◽  
Sina Alaghmand ◽  
Sharolyn Anderson ◽  
Paul Sutton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Spectral Band ◽  
Green Spaces ◽  
Systematic Observation ◽  
Proximal Sensing ◽  
Mixed Effect ◽  
Urban Greenery

More well-maintained green spaces leading toward sustainable, smart green cities mean that alternative water resources (e.g., wastewater) are needed to fulfill the water demand of urban greenery. These alternative resources may introduce some environmental hazards, such as salt leaching through wastewater irrigation. Despite the necessity of salinity monitoring and management in urban green spaces, most attention has been on agricultural fields. This study was defined to investigate the capability and feasibility of monitoring and predicting soil salinity using proximal sensing and remote sensing approaches. The innovation of the study lies in the fact that it is one of the first research studies to investigate soil salinity in heterogeneous urban vegetation with two approaches: proximal sensing salinity mapping using Electromagnetic-induction Meter (EM38) surveys and remote sensing using the high-resolution multispectral image of WorldView3. The possible spectral band combinations that form spectral indices were calculated using remote sensing techniques. The results from the EM38 survey were validated by testing soil samples in the laboratory. These findings were compared to remote sensing-based soil salinity indicators to examine their competence on mapping and predicting spatial variation of soil salinity in urban greenery. Several regression models were fitted; the mixed effect modeling was selected as the most appropriate to analyze data, as it takes into account the systematic observation-specific unobserved heterogeneity. Our results showed that Soil Adjusted Vegetation Index (SAVI) was the only salinity index that could be considered for predicting soil salinity in urban greenery using high-resolution images, yet further investigation is recommended.

scholarly journals Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data

2018 ◽  
Vol 7 (10) ◽  
pp. 411 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Strong Correlation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Complex Structure ◽  
Remote Sensing Data ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Optical Parameters

As wetlands are one of the world’s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

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