Spatial Variability of Soil Salinization as Judged from the Comparison of Soil Maps and Remote Sensing Materials for Different Years in Uzbekistan

2012 ◽  
pp. 127-139 ◽  
Author(s):  
Dmitry I. Rukhovich ◽  
Polina V. Koroleva ◽  
Yekaterina V. Vil’chevskaya ◽  
Natalia V. Kalinina ◽  
Galina I. Chernousenko ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Salinization ◽  
Soil Maps

Monitoring and Analysis of Degraded Land in Rasulabad Block of Kanpur Dehat District Using Remote Sensing and GIS Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2413-2421
Author(s):  
Pushkin Kumar
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Land Degradation ◽  
Soil Degradation ◽  
Agricultural Practices ◽  
Soil Samples ◽  
Lower State ◽  
Soil Parameters ◽  
Soil Maps ◽  
Gis Environment

Abstract: Land degradation is seen as a development or additional that reduces current and/or potential soil capability to produce products and goods. This implies a decline from a higher to a lower state due to a decline in land capacity, productivity, and biodiversity loss. This can be both natural and human-induced. Natural causes embody earthquakes, tsunamis, droughts, avalanches, landslides, volcanic eruptions, floods, tornadoes, and wildfires. Whereas human-induced soil degradation results from land clearing and deforestation, inappropriate agricultural practices, improper management of industrial effluents and wastes, over-grazing, careless management of forests, surface mining, urban sprawl, and commercial/industrial development. Inappropriate agricultural practices embody excessive tillage and use of heavy machineries, excessive and unbalanced use of inorganic fertilizers, poor irrigation and water management techniques, chemical or pesticide overuse, inadequate crop residue and organic carbon inputs, and poor crop cycle planning. Some underlying social causes of soil degradation in Asian nation square measure land shortage, decline in per capita land handiness, economic pressure onto land, land occupancy, poverty, and population increase.. The aim of the current study is to prepare baseline data to combat land degradation and conserve land resources in an economical and efficient manner. To assess land degradation with the help of Remote Sensing (RS) and Geographical Information System (GIS) – in Rasulabad Block of Kanpur Dehat district, Uttar Pradesh, different levels of analysis were performed to estimate the extent of land. Degradation to assess saline or salt-free soils and calcareous or sodium soils and to match this data with satellite studies. The spatial variability of these soil parameters was shown in soil maps created in a GIS environment. A temporary study of the 2017 and 2021 Sentinel satellite datasets was done to find the parameters that are responsible for land degradation. The severity of land degradation was calculable quantitatively by analyzing the physico-chemical parameters within the laboratory to see salinity and sodicity of soils and further correlating them with satellite-based studies. The pH varied between 7.1 and 8.2, electrical conductivity (EC) between 0.23 and 0.6 miliSiemens/m and the methyl orange or total alkalinity between 0.095 and 0.225 (HCO3 ) gL-1 as CaCO3. The spatial variability in these soil parameters was pictured through soil maps generated in a GIS environment with the help of IDW Interpolation. The results revealed that the soil in the study area was exposed to salt intrusion, most of the soil samples of the study area were slightly or moderately saline with a few salt-free sites. Moreover, the majority of the soil samples were calcareous and a few samples were alkaline or sodic in nature. Keyword: Land degradation, Sodic land, Saline land, GIS, IDW Interpolation.

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 Reconstructing High-Precision Coral Reef Geomorphology from Active Remote Sensing Datasets: A Robust Spatial Variability Modified Ordinary Kriging Method

2022 ◽  
Vol 14 (2) ◽  
pp. 253
Author(s):  
Qi Wang ◽  
Han Xiao ◽  
Wenzhou Wu ◽  
Fenzhen Su ◽  
Xiuling Zuo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Coral Reefs ◽  
Spatial Variability ◽  
High Precision ◽  
Ordinary Kriging ◽  
Surface Model ◽  
Smoothing Effect ◽  
Geomorphological Parameters ◽  
Reef Geomorphology

Active remote sensing technology represented by multi-beam and lidar provides an important approach for the effective acquisition of underwater coral reef geomorphological information. A spatially continuous surface model of coral reef geomorphology reconstructed from active remote sensing datasets can provide important geomorphological parameters for the research of coral reef geomorphological and ecological changes. However, the surface modeling methods commonly used in previous studies, such as ordinary kriging (OK) and natural neighborhood (NN), often represent a “smoothing effect”, which causes the strong spatial variability of coral reefs to be imprecisely reflected by the reconstructed surfaces, thus affecting the accurate calculation of subsequent geomorphological parameters. In this study, a spatial variability modified OK (OK-SVM) method is proposed to reduce the impact of the “smoothing effect” on the high-precision reconstruction of the complex geomorphology of coral reefs. The OK-SVM adopts a collaborative strategy of global parameter transformation, local residual correction, and extremum correction to modify the spatial variability of the reconstructed model, while maintaining high local accuracy. The experimental results show that the OK-SVM has strong robustness to spatial variability modification. This method was applied to the geomorphological reconstruction of the northern area of a coral atoll in the Nansha Islands, South China Sea, and the performance was compared with that of OK and NN. The results show that OK-SVM has higher numerical accuracy and attribute accuracy in detailed morphological fidelity, and is more adaptable in the geomorphological reconstruction of coral reefs with strong spatial variability. This method is relatively reliable for achieving high-precision reconstruction of complex geomorphology of coral reefs from active remote sensing datasets, and has potential to be extended to other geomorphological reconstruction applications.

scholarly journals Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

2014 ◽  
Vol 11 (23) ◽  
pp. 6827-6840 ◽  
Author(s):  
M. Réjou-Méchain ◽  
H. C. Muller-Landau ◽  
M. Detto ◽  
S. C. Thomas ◽  
T. Le Toan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Forest Degradation ◽  
Forest Carbon ◽  
Mitigation Strategies ◽  
Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

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