A novel calculation method of subsidence waterlogging spatial information based on remote sensing techniques and surface subsidence prediction

2022 ◽  
pp. 130366
Author(s):  
Xiaojun Zhu ◽  
Zhengyuan Ning ◽  
Hua Cheng ◽  
Pengfei Zhang ◽  
Ru Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Calculation Method ◽  
Spatial Information ◽  
Surface Subsidence ◽  
Subsidence Prediction ◽  
Remote Sensing Techniques

scholarly journals Using Remote Sensing Techniques in Environmental Management

2016 ◽  
Vol 73 (2) ◽  
pp. 230
Author(s):  
Mihai Valentin Herbei ◽  
Roxana Herbei ◽  
Laura Smuleac ◽  
Tudor Salagean
Keyword(s):  
Remote Sensing ◽  
Environmental Management ◽  
Information System ◽  
Geographical Information System ◽  
Geographical Information Systems ◽  
Spatial Information ◽  
Modern Technology ◽  
Geographical Information ◽  
Remote Sensing Techniques ◽  
Modern Solution

The Geographical Information Systems technology is used in many fields where the spatial information is very important and relevant, that means in all fields that use a system for saving, analyzing and representing the data which are processed. The aim of this paper is using modern technology for monitoring the environment. Geographical Information System together with remote sensing have a very important role in decision process regarding the environment. Integration of remote sensing images in a Geographical Information System which enables complex spatial analysis is a useful and modern solution for environmental management and decision-making process. Satellite images contain various information that can support environmental monitoring, images that can be analyzed and interpreted in various ways by using the Geographical Information System tools.

scholarly journals Thermokarst Landscape Development Detected by Multiple-Geospatial Data in Churapcha, Eastern Siberia

2021 ◽  
Vol 9 ◽  
Author(s):  
Yoshihiro Iijima ◽  
Takahiro Abe ◽  
Hitoshi Saito ◽  
Mathias Ulrich ◽  
Alexander N. Fedorov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Surface Subsidence ◽  
Integrated Analysis ◽  
Permafrost Degradation ◽  
Eastern Siberia ◽  
Geospatial Information ◽  
Gis And Remote Sensing ◽  
The Past ◽  
Central Yakutia

Thermokarst is a typical process that indicates widespread permafrost degradation in yedoma landscapes. The Lena-Aldan interfluvial area in Central Yakutia in eastern Siberia is now facing extensive landscape changes with surface subsidence due to thermokarst development during the past few decades. To clarify the spatial extent and rate of subsidence, multiple spatial datasets, including GIS and remote sensing observations, were used to analyze the Churapcha rural locality, which has a typical yedoma landscape in Central Yakutia. Land cover classification maps for 1945 and 2009 provide basic information on anthropogenic disturbance to the natural landscape of boreal forest and dry grassland. Interferometric synthetic aperture radar (InSAR) with ALOS-2/PALSAR-2 data revealed activated surface subsidence of 2 cm/yr in the disturbed area, comprising mainly abandoned agricultural fields. Remote sensing with an unmanned aerial system also provided high-resolution information on polygonal relief formed by thermokarst development at a disused airfield where InSAR analysis exhibited extensive subsidence. It is worth noting that some historically deforested areas have likely recovered to the original landscape without further thermokarst development. Spatial information on historical land-use change is helpful because most areas with thermokarst development correspond to locations where land was used by humans in the past. Going forward, the integrated analysis of geospatial information will be essential for assessing permafrost degradation.

scholarly journals Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

2018 ◽  
pp. 195
Author(s):  
Pedro Perez Cutillas ◽  
Gonzalo G. Barberá ◽  
Carmelo Conesa García
Keyword(s):  
Remote Sensing ◽  
Environmental Variables ◽  
Input Data ◽  
Vegetation Index ◽  
Mineralogical Composition ◽  
Soil Erodibility ◽  
Estimation Algorithms ◽  
K Factor ◽  
Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han   mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

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