SPATIAL MODELING FOR SOIL EROSION ASSESSMENT IN UPPER LAM PHRA PHLOENG WATERSHED, NAKHON RATCHASIMA, THAILAND (original) (raw)

Soil Erosion Mapping of Katteri Watershed using Universal Soil Loss Equation and Geographic Information System

Soil erosion assessment for watershed management is a world-wide concern for ecologists and land users. In this study soil erosion was predicted using Universal Soil Loss Equation (USLE) for Katteri watershed in Nilgiris, Tamilnadu. Topographically the study area falls under steep slope and undulating terrain comprising 49% of tea plantation, 23% of horticultural crops, and 24% of forest area. The IRS-IC LISS III satellite imagery was classified and used for preparing the landuse/landcover which estimates cover management factor (C) and the landuse practice factor (P). Using digital elevation model (DEM) as input, the slope length factor (LS) was determined by AML programme. Soil erodibility (K) values were measured for all mapped soil types in the study area. The rainfall erosivity factor (R) was directly computed from rainfall intensities. All the above mentioned USLE factors, with associated attribute data, were digitally encoded in a GIS database and converted into 30 m grid cells. Simultaneous overlaid operation on these five layers produced a resultant layer of soil erosion rate in t ha -1 yr -1 . This research confirms that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Katteri watershed.

Soil Erosion Mapping of Katteri Watershed using Universal Soil Loss Equation and GIS

Soil erosion assessment for watershed management is a world-wide concern for ecologist and land users. In this study soil erosion was predicted using Universal Soil Loss Equation (USLE) for Katteri watershed in Nilgiris, Tamilnadu. Topographically the study area falls under steep slope and undulating terrain comprising 49% of tea plantation, 23% of horticultural crops, and 24% of forest area. The IRS-IC LISS III satellite imagery was classified and used for preparing the landuse/landcover which estimates cover management factor (C) and the landuse practice factor (P). Using digital elevation model (DEM) as input, the slope length factor (LS) was determined by AML programme. Soil erodibility (K) values were measured for all mapped soil types in the study area. The rainfall erosivity factor (R) was directly computed from rainfall intensities. All the above mentioned USLE factors, with associated attribute data, were digitally encoded in a GIS database and converted into 30 m grid cells. Simultaneous overlaid operation on these five layers produced a resultant layer of soil erosion rate in t acre -1 yr -1 . This research confirms that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Katteri watershed.

Estimation of soil erosion in a semi-arid watershed of Tamil Nadu (India) using revised universal soil loss equation (rusle) model through GIS

Soil erosion is one of the serious issues threatening the environment. This degrading phenomenon deteriorates the soil fertility and drastically affects the agricultural practices. As a consequence, the productivity of soil is affected unquestionably. In this regard, there is a need to take up conservation and management measures which can be applied to check further soil erosion. Even though, soil erosion is a mass process spread across the watershed, it is not economically viable to implement conservation techniques to the entire watershed. However, a method is a prerequisite to identify the most vulnerable areas and quantify the soil erosion. In this context, Revised Universal Soil Loss Equation (RUSLE) has been adopted to estimate soil erosion in the semi-arid Andipatti Watershed of Tamil Nadu, India. This model takes into consideration the parameters including runoff-rainfall erosivity factor (R), soil erodability Factor (K), topographic factor (LS), cropping management factor (C), and support practice factor (P). All these layers are prepared in a geographical information system (GIS) platform using various data sources and data preparation methods. The results of the study indicate that the annual average soil loss within the watershed is about 6 t/ha/yr (metric ton per hectare per year). Higher soil erosion is observed in the landuse classes of gullied wasteland, open scrub forest and degraded plantation. The soil erosion risk is extremely higher on the steep slopes and adjoining foot hills. Based on the average soil erosion values of different landuse classes and characteristics of land, a proposed landuse map was prepared. The estimated soil erosion and the proposed landuse map could be an effective input for drawing sustainable watershed development measures.

Estimation of soil erosion using Revised Universal Soil Loss Equation and GIS

A comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques were adopted to determine the soil erosion vulnerability of a forested mountainous sub-watershed in Kerala, India. The spatial pattern of annual soil erosion rate was obtained by integrating geo-environmental variables in a raster-based GIS method. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the area. The resultant map of annual soil erosion shows a maximum soil loss of 282.2 t/h/yr with a close relation to grassland areas, degraded forests and deciduous forests on the steep side-slopes. The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning and management in the environmentally sensitive mountainous areas.

Assessment of land cover and land use change impact on soil loss in a tropical catchment by using multitemporal SPOT-5 satellite images and Revised Universal Soil Loss Equation model

Land Degradation & Development

Soil erosion is a common land degradation problem and has disastrous impacts on natural ecosystems and human life. Therefore, researchers have focused on detection of land cover-land use changes (LCLUC) with respect to monitoring and mitigating the potential soil erosion. This article aims to appraise the relationship between LCLUC and soil erosion in the Cameron Highlands (Malaysia) by using multitemporal satellite images and ancillary data. Land clearing and heavy rainfall events in the study area has resulted in increased soil loss. Moreover, unsustainable development and agricultural practices, mismanagement, and lack of land use policies increase the soil erosion rate. Hence, the main contribution of this study lies in the application of appropriate land management practices in relation to water erosion through identification and prediction of the impacts of LCLUC on the spatial distribution of potential soil loss in a region susceptible to natural hazards such as landslide. The LCLUC distribution within the study area was mapped for 2005, 2010, and 2015 by using SPOT-5 temporal satellite imagery and object-based image classification. A projected land cover-land use map was also produced for 2025 through integration of Markov chain and cellular automata models. An empirical-based approach (Revised Universal Soil Loss Equation) coupled with geographic information system was applied to measure soil loss and susceptibility to erosion over the study area for four periods (2005, 2010, 2015, and 2025). The model comprises five parameters, namely, rainfall factor, soil erodibility, topographical factor, conservation factor, and support practice factor. Results exhibited that the average amount of soil loss increased by 31.77 t ha −1 yr −1 from 2005 to 2015 and was predicted to dramatically increase in 2025. The results generated from this research recommends that awareness of spatial and temporal patterns of high soil loss risk areas can help deploy site-specific soil conservation measures and erosion mitigation processes and prevent unsystematic deforestation and urbanization by the authorities.

Soil erosion analysis by RUSLE and sediment yield models using remote sensing and GIS in Kelantan state, Peninsular Malaysia

Soil Research

The present study used pixel-based soil erosion analysis through Revised Universal Soil Loss Equation (RUSLE) and a sediment yield model. The main motive of this study is to find soil erosion probability zones and accordingly prioritise watersheds using remote sensing and Geographic Information System (GIS) techniques in Kelantan state, Peninsular Malaysia. The catchment was divided into 82 watersheds and soil loss of the catchment was calculated. Soil loss and sediment yield were divided into five categories ranging from very low to very high. Maximum area of the very high soil-loss category was observed in uncultivated land and the maximum area of very low soil-loss category was in forest. Soil erosion probability zones were also divided into five categories in which 36.1% of the area experienced zero soil erosion and 20.1% and 17.8% represented very high and high probability zones respectively. The maximum very high and high probability zones were 61.6% and 28.5% of the watershed...

Forecasting Soil Erosion Risk Using GIS and Remote Sensing for the Nam Un Basin, Sakon Nakhon Province, Thailand

Polish Journal of Environmental Studies

Geohazard mapping using remote sensing and GIS is effective. Nam UN classic terrain with soil erosion and other geohazards. The Nam UN Basin's yearly soil loss and high erosion potential are estimated using RUSLE, remote sensing, and GIS. 14.26 t/ha/year of soil erosion is seen on the map. Soil erosion zones are also shown on the map. According to the study, 33.40 percent of the whole area (457.07 kilometers) is prone to severe soil erosion, while 7.72 percent (105.76 kilometers) is prone to high erosion. To decrease soil erosion, decision-makers use soil erosion prognosis analysis. The Analytical Hierarchy Process (AHP) was utilized to identify key soil erosion prone locations by incorporating geo-environmental variables such land use/land cover, geomorphology, Dem, drainage density, slope, elevation, LS factor, rainfall, soil texture, and soil depth. 33.40% of the region is highly prone to soil erosion.

Spatial Soil Erosion Modeling for Sustainable Agriculture Development Using Remote Sensing and GIS Technology

Dhaka University Journal of Biological Sciences, 2015

Middle mountain areas of Nepal Himalaya is seriously suffering from ecological degradation and an estimation of over 240 million cubic meter of top soil is being eroded annually to the Bay of Bengal. Thirteen per cent of Nepal’s watershed area have deteriorated seriously and 10,000 sq km are devoid of sufficient vegetation. Top soil loss from the mountain results in the riverbeds’ raise at a annual rate of 15 ‐ 30 cm and its effect on soil fertility declines. Considering this, an attempt was made to estimate the soil loss using GI Science technology and its correlative interpretation with land system units and land use and cover types from Maheshkhola watershed. Among several empirical and physically‐based erosion models, Revised USLE (RUSLE) using RKLSCP was used to estimate the soil loss in the present analysis. A total of 231,155 ton soil was estimated annually being lost from Maheshkhola watershed. Erosion rates were found highly associated with the slope of land system units. T...

A comparative study of soil erosion models based on GIS and remote sensing

ISH Journal of Hydraulic Engineering, 2020

In most of the developing and underdeveloped countries, the demand of crop is not fulfilled because of less production, and it is further decreasing due to soil erosion. The sheet erosion affects the fertility of soil, and due to other erosion like rill and gully erosion, the land area is reduced. The silent hazard namely soil erosion is reducing the crop production and has become one of the alarming problems of the whole world. Advancement of technology and development of various models based on remote sensing and GIS has played a significant role in the estimation of soil erosion in the catchment using various empirical models. Still it is a challenge to apply those models and investigate soil erosion-prone areas and quantify erosion. In this study, Universal Soil Loss Equation (USLE) and the Unit Stream Power based Soil Erosion/Deposition (USPED) models were used to identify soil erosion in the Upper Lake, Bhopal in India. Different factors affecting soil erosion were studied, and maps of soil loss due to USLE and USPED models were generated. The soil loss using USPED model was found to be 764481.5 tonnes/yr, whereas using USLE model it was found to be 711700 tones/yr.

Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed

The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing, wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, etc. which causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover (LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km 2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation (USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor, topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential (reversible soil loss) or 0e1 t ha À1 yr À1 soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC condition. Results from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions (1984e2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significance of LULC in the control of erosion. Maps generated from the study may be useful to planners and land use managers to take appropriate decisions for soil conservation. Ó 2017, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).