scholarly journals ASSESSMENT AND MAPPING OF LANDFILLS ON SOILS IN THE SERPUKHOV DISTRICT (MOSCOW REGION)

2021 ◽  
Vol 47 (4) ◽  
pp. 181-185
Author(s):  
Azamat Suleymanov ◽  
Evgeny Abakumov ◽  
Igor Zakharenko ◽  
Ruslan Suleymanov
Keyword(s):  
Remote Sensing ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Soil Texture ◽  
Soil Cover ◽  
Remote Sensing Data ◽  
Spatial Location ◽  
Soil Types ◽  
Gis And Remote Sensing ◽  
Sensing Data

Cartographic materials are an important tool for different purposes. Environmental maps are essential for various activities aimed at protecting the environment. The work presents the experience of creating a map called “Landfills in the Serpukhov district” using GIS and remote sensing data. Garbage wastes polygons sites are divided into three types: municipal solid waste, illegal landfills and biological waste. The soil cover of the region is mainly represented by Retisols and Luvic Retic Phaeozem soils. The map allowed us to evaluate the current situation and the spatial location of landfills on different soil types (including the variation of soil texture).

scholarly journals Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

2020 ◽  
Vol 12 (16) ◽  
pp. 2660
Author(s):  
Philip Marzahn ◽  
Swen Meyer
Keyword(s):  
Remote Sensing ◽  
Soil Texture ◽  
Land Surface ◽  
Temporal Frequency ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Test Site ◽  
Soil Parameters ◽  
Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

scholarly journals Estimation of soil types by non linear analysis of remote sensing data

2008 ◽  
Vol 15 (1) ◽  
pp. 115-126 ◽  
Author(s):  
C. Hahn ◽  
R. Gloaguen
Keyword(s):  
Remote Sensing ◽  
Soil Type ◽  
Remote Sensing Data ◽  
Soil Types ◽  
Support Vector ◽  
Sensing Data ◽  
Vector Machines ◽  
Non Linear ◽  
Input Variables ◽  
Type Classification

Abstract. The knowledge of soil type and soil texture is crucial for environmental monitoring purpose and risk assessment. Unfortunately, their mapping using classical techniques is time consuming and costly. We present here a way to estimate soil types based on limited field observations and remote sensing data. Due to the fact that the relation between the soil types and the considered attributes that were extracted from remote sensing data is expected to be non-linear, we apply Support Vector Machines (SVM) for soil type classification. Special attention is drawn to different training site distributions and the kind of input variables. We show that SVM based on carefully selected input variables proved to be an appropriate method for soil type estimation.

Sign in / Sign up

Export Citation Format

Share Document