scholarly journals Aplikasi Penginderaan Jauh dan SIG dalam Penilaian Potensi Erosi Permukaan secara Kualitatif di Daerah Tangkapan Waduk Kedung Ombo

2011 ◽  
Vol 25 (2) ◽  
pp. 152
Author(s):  
Arina Miardini ◽  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Geographic Information Systems ◽  
Soil Erosion ◽  
Soil Type ◽  
Catchment Area ◽  
Drainage Density ◽  
Soil Types ◽  
Slope Gradient ◽  
Slope Direction

The purpose of this study was to determine the potential erosion qualitatively by using SES by using Remote Sensing and Geographic Information Systems in Kedung Ombo’s catchment area so it can be determined which areas of priority should be conserved. The method used is qualitatively analyses through SES method (Soil Erosion Status).) Which is calculated based on five parameters are: slope direction (aspect), slope (slope gradient), the density of the river (drainage density), soil type (Soil types), and land use (landuse/landcover). The result shows that DTW Kedung Ombo has three classes of erosion, which is very low, low and medium. Amounted to 41179.08 ha or 71.31% of the total DTW Kedung Ombo erosion potential is still relatively mild, 13956.01 ha (24.17%), erosion potential is very low and 2608.95 ha (4:52%) were classified as potential erosion.

scholarly journals A Quantitative Analysis of Factors Influencing Organic Matter Concentration in the Topsoil of Black Soil in Northeast China Based on Spatial Heterogeneous Patterns

2021 ◽  
Vol 10 (5) ◽  
pp. 348
Author(s):  
Zhenbo Du ◽  
Bingbo Gao ◽  
Cong Ou ◽  
Zhenrong Du ◽  
Jianyu Yang ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Soil Erosion ◽  
Soil Type ◽  
Northeast China ◽  
Black Soil ◽  
Factors Affecting ◽  
Soil Zone ◽  
Rainfed Farming ◽  
Study Results

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China’s commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited.

More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses

2021 ◽  
pp. 030913332199181
Author(s):  
Andreas Christian Braun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Geographic Information Systems ◽  
Image Classification ◽  
Satellite Image ◽  
Physical Geography ◽  
Data Availability ◽  
Scientific Results ◽  
Normative Considerations

Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.

scholarly journals Properties of humic acids depending on the land use in different parts of Slovakia

2021 ◽  
Author(s):  
Magdalena Banach-Szott ◽  
Bozena Debska ◽  
Erika Tobiasova
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Size Distribution ◽  
Humic Acids ◽  
Forest Ecosystem ◽  
Soil Type ◽  
Soil Types ◽  
Organic Matter Quality ◽  
Degree Of Maturity ◽  
Effects Of Land Use

AbstractMany studies report organic carbon stabilization by clay minerals, but the effects of land use and soil type on the properties of humic acids (HAs) are missing. The aim of the paper is to determine the effects of land use and soil types on the characteristics of HAs, which have a considerable influence on organic matter quality. It was hypothesised that the effect of the land use on HAs properties depends on the particular size distribution. The research was performed in three ecosystems: agricultural, forest, and meadow, located in Slovakia. From each of them, the samples of 4 soil types were taken: Chernozem, Luvisol, Planosol, and Cambisol. The soil samples were assayed for the content of total organic carbon (TOC) and the particle size distribution. HAs were extracted with the Schnitzer method and analysed for the elemental composition, spectrometric parameters in the UV-VIS range, and hydrophilic and hydrophobic properties, and the infrared spectra were produced. The research results have shown that the properties of HAs can be modified by the land use and the scope and that the direction of changes depends on the soil type. The HAs of Chernozem and Luvisol in the agri-ecosystem were identified with a higher “degree of maturity”, as reflected by atomic ratios (H/C, O/C, O/H), absorbance coefficients, and the FT-IR spectra, as compared with the HAs of the meadow and forest ecosystem. However, as for the HAs of Cambisol, a higher “degree of maturity” was demonstrated for the meadow ecosystem, as compared with the HAs of the agri- and forest ecosystem. The present research has clearly identified that the content of clay is the factor determining the HAs properties. Soils with a higher content of the clay fraction contain HAs with a higher “degree of maturity”.

scholarly journals Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Dereje Gebrie Habte ◽  
Satishkumar Belliethathan ◽  
Tenalem Ayenew
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Catchment Area ◽  
Environmental Conservation ◽  
Land Use Land Cover ◽  
Cultivated Land ◽  
Plantation Forest ◽  
Bare Land

AbstractEvaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

scholarly journals Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiaofang Huang ◽  
Lirong Lin ◽  
Shuwen Ding ◽  
Zhengchao Tian ◽  
Xinyuan Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Variability ◽  
Influencing Factors ◽  
Soil Type ◽  
Soil Survey ◽  
Purple Soil ◽  
Soil Erodibility ◽  
K Value ◽  
K Factor

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

scholarly journals Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Veera Narayana Balabathina ◽  
R. P. Raju ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Soil Erodibility ◽  
Lake Tana ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

scholarly journals Effects of Land Use and Slope Gradient on Soil Erosion in a Red Soil Hilly Watershed of Southern China

2015 ◽  
Vol 7 (10) ◽  
pp. 14309-14325 ◽  
Author(s):  
Zhanyu Zhang ◽  
Liting Sheng ◽  
Jie Yang ◽  
Xiao-An Chen ◽  
Lili Kong ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Southern China ◽  
Red Soil ◽  
Slope Gradient ◽  
Hilly Watershed ◽  
Effects Of Land Use

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.

scholarly journals MODELLING AND MAPPING OF SOIL EROSION ON THE OUED EL MALLEH CATCHMENT USING REMOTE SENSING AND GIS

2013 ◽  
pp. 302-307
Author(s):  
Omar El Aroussi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Land Degradation ◽  
Soil Loss ◽  
Land Use Changes ◽  
Field Studies ◽  
Water Volume ◽  
Water Storage Capacity ◽  
Rusle Model

In Morocco, the spectacular expansion of erosive processes shows increasingly alarming aspects. Due to the considerable costs of detailed ground surveys for studying this phenomenon, remote sensing is an appropriate alternative for analyzing and evaluating the risks of the expansion of soil degradation. According to an FAO study (2001), Erosion threatens 13 million ha of cropland and rangeland in northern Morocco and induces an estimated average water storage capacity loss of 50 million m3 each year through dam silting. The lost water volume could potentially be used to irrigate 5000 to 6000 ha / year. This study analyses soil erosion on the Oued El Malleh catchment, a 34 km2 catchment located in the north of Fez (Morocco). This contribution aims at mapping the spatio-temporal evolution of land use and modelling the erosion and sedimentation processes using the well known RUSLE model. Land use changes were assessed using Landsat-5 TM and Landsat-7 ETM+ images, from the 1987-2011 periods which were validated by field studies. The images were first georeferenced and projected into the Moroccan coordinate system (Merchich North) then processed to evaluate soil loss through a GIS package (Idrisi Andes Software). These static assessments of soil loss were then used in a deposition/sedimentation algorithm to model soil loss propagation to the downstream. The soil loss averages determined by the model vary between 1.09 t/ha/yr as a minimum value for the reforested lands and 169.4 t/ha/yr as a maximum value for the uncultivated lands (badlands). The latter generally correspond to Regosols or low protected soils located on steep slopes. In comparison with RUSLE, the sedimentation model yields lower values of soil losses; only 97.3 t/h/year for the uncultivated lands, and -0.34 t/ha/year in the reforested land, indicating an on-going sedimentation process. By taking into account the temporal variability of erosion and deposition jointly lower values of soil erosion are calculated by the RUSLE model. However, despite this decline, land degradation problems are still important due to the combination of land use and local lithology. The results of this study were used to indentify areas where interventions are needed to limit land degradation processes.

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