Use of remote sensing,GIS and C++ for soil erosion assessment in the Shakkar River basin,India

Soil is an essential resource for human livelihoods. Soil erosion is now a global environmental crisis that threatens the natural environment and agriculture. This study aimed to assess the annual rate of soil erosion using distributed information for topography, land use and soil, with a remote sensing (RS) and geographical information system (GIS) approach and comparison of simulated with observed sediment loss. The Shakkar River basin, situated in the Narsinghpur and Chhindwara districts of Madhya Pradesh, India, was selected for this study. The universal soil loss equation (USLE) with RS and GIS was used to predict the spatial distribution of soil erosion occurring in the study area on a grid-cell basis. Thematic maps of rainfall erosivity factor (R), soil erodibility factor (K), topographic factor (LS), crop/cover management factor (C), and conservation/support practice factor (P) were prepared using annual rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and a satellite image of the study area in the GIS environment. The annual rate of soil erosion was estimated for a 15-year period (1992–2006) and was found to vary between 6.45 and 13.74 t ha^?1 year^?1, with an average annual rate of 9.84 t ha^?1 year^?1. The percentage deviation between simulated and observed values varies between 2.68% and 18.73%, with a coefficient of determination (R²) of 0.874.     

Application of USLE in a GIS environment to estimate soil erosion in the Irga watershed,Jharkhand, India

A comprehensive methodology that integrates the Universal Soil Loss Equation (USLE) and Geographic Information System (GIS) was adopted in this study to determine the soil erosion and sediment yield of the Irga watershed in Jharkhand, India. Based on the availability and applicability of data in a GIS-environment, the original equations for the model input parameters were, however, modified by researchers. In the present study, a power-law equation was generated to estimate the rainfall erosivity (R) factor, and the Nash-Sutcliffe model efficiency coefficient used to determine the accuracy of the modified R factor. Average annual soil erosion in the Irga watershed is estimated to be 4.3 t ha^?1 yr^?1. On the other hand, average annual sediment yield of the watershed, estimated using the sediment delivery ratio, was found to be 1.2 t ha^?1 yr^?1. Low sediment yield indicates that most of the eroded soil was deposited within the watershed.     

厦门市土壤保持服务及其时空动态变化分析

我国是世界上水土流失最严重的国家之一,土壤保持服务核算可为环境治理提供科学参考。本文采用通用土壤流失方程(USLE)模拟2010、2015年厦门市土壤保持服务功能,分析其时空动态变化,并对土壤保持服务价值量进行估算。结果表明:2015年厦门市土壤保持能力平均为2 596.72 t/hm^2,土壤保持总量为4.41×10~8t。相比于2010年,2015年单位面积土壤保持能力增加了247.76 t/hm^2,约增加了10.5%。厦门市各区土壤保持均值由大到小排序为:同安区>集美区>海沧区>翔安区>思明区>湖里区。各生态系统类型土壤保持均值由大到小排序为:灌木林地>森林>草地>农田>城市绿地。2015年厦门市土壤保持总价值约为4.37×10¹⁰元。     

Predicting unit plot soil loss in Sicily,south Italy

Predicting soil loss is necessary to establish soil conservation measures. Variability of soil and hydrological parameters complicates mathematical simulation of soil erosion processes. Methods for predicting unit plot soil loss in Sicily were developed by using 5 years of data from replicated plots. At first, the variability of the soil water content, runoff, and unit plot soil loss values collected at fixed dates or after an erosive event was investigated. The applicability of the Universal Soil Loss Equation (USLE) was then tested. Finally, a method to predict event soil loss was developed. Measurement variability decreased as the mean increased above a threshold value but it was low also for low values of the measured variable. The mean soil loss predicted by the USLE was lower than the measured value by 48%. The annual values of the soil erodibility factor varied by seven times whereas the mean monthly values varied between 1% and 244% of the mean annual value. The event unit plot soil loss was directly proportional to an erosivity index equal to , being Q_RR_e the runoff ratio times the single storm erosion index. It was concluded that a relatively low number of replicates of the variable of interest may be collected to estimate the mean for both high and particularly low values of the variable. The USLE with the mean annual soil erodibility factor may be applied to estimate the order of magnitude of the mean soil loss but it is not usable to estimate soil loss at shorter temporal scales. The relationship for estimating the event soil loss is a modified version of the USLE‐M, given that it includes an exponent for the Q_RR_e term. Copyright © 2007 John Wiley & Sons, Ltd.     

长江上游小流域土壤侵蚀动态模拟与分析

以长江上游甘肃省尚沟流域为研究区,在遥感影像和GIS空间分析技术支撑下,根据USLE因子算法生成各因子栅格图,借助地图代数运算,估算了尚沟流域1998年和2004年的土壤侵蚀量,并对2004年土壤侵蚀与其环境背景因子进行叠加和空间统计分析。在此基础上,构建了与GIS软件平台集成的地理元胞自动机,模拟了该流域2004年、2010年和2020年土壤侵蚀空间演化情形。结果表明:研究区平均侵蚀量从1998年的6598.1t/km2下降到2004年的5923.3t/km2,侵蚀面积净减少172.3hm2,输沙量减少9.15×104t;1300～1400m的海拔高程带、25～35°坡度带、南坡和旱耕地是发生水土流失的主要区域;经模拟,2010年总侵蚀面积为93.49km2,侵蚀总量73.15×104t,侵蚀模数为5126t/km2,土壤侵蚀状况总体上将有所减缓。     

基于GIS和USLE的东圳库区土壤侵蚀量预测研究

运用GIS、遥感技术与USLE模型相结合预测东圳库区流域土壤侵蚀量,利用GIS平台建立东圳库区基础地理数据库,以10×10m为栅格单元对库区进行离散化,在GIS空间分析功能支持下,结合USLE土壤侵蚀预测模型,根据合适的USLE因子算法生成栅格图层,通过图层运算,预测了东圳库区土壤侵蚀量,进而标识库区土壤侵蚀严重区域,为库区非点源污染控制及水土流失治理提供定量依据,并通过建立实用的泥沙输移比模型结合水文监测数据,对预测结果进行验证。根据USLE方程,采用改进后的SDR方程,预测的渡里流域年输沙量与流域实际监测得到的年输沙量仅相差14.08%,说明本文采用USLE方程模拟流域土壤侵蚀结果可信,可定量评价、拟定水土保持措施方案。     

基于GIS和RS的四川省彭州市土壤侵蚀敏感性评价

根据彭州市独特的自然地理特征,结合美国通用土壤流失方程(USLE),选择降雨侵蚀力(R)、土壤可蚀性(K)、坡度坡长(LS)和地表覆盖(C)4个自然因子作为彭州市土壤侵蚀敏感性评价的指标,在GIS和RS技术支持下,对彭州市土壤侵蚀敏感性并进行综合评价.结果表明:彭州市土壤侵蚀敏感性以不敏感(37.78％)、轻度敏感(19.22％)和极敏感(22.09％)为主,中度敏感(10.57％)和高度敏感(10.33％)比例相对较小;区域内土壤侵蚀敏感性的空间分布具有垂直地带性,其中最不敏感区主要分布在东南平原地区,极敏感区主要分布在北部坡度大于25.的亚高山、低山和深丘地区.研究结果将为区域内土壤侵蚀状况的宏观把握和水土保持政策的制定提供科学依据.     

Assessment of soil erosion and sediment delivery ratio using remote sensing and GIS： a case study of upstream Chaobaihe River catchment, north China

Soil erosion in catchment areas reduces soil productivity and causes a loss of reservoir capacity. Several parametric models have been developed to predict soil erosion at drainage basins, hill slopes and field levels. The well-known Universal Soil Loss Equation （USLE） represents a standardized approach. Miyun reservoir, which sits on Chaobaihe River, is the main surface source of drinking water for Beijing, the capital of China. Water and soil loss are the main reasons for sediment to enter a reservoir. Sediment yield is assessed using a version of the universal soil loss equation modified by Chinese researchers. All year 2001 and 2002 data for factors in the equation are obtained from remote sensing or collected to form an analysis database. These factors are computed and mapped using Geographic Information System tools. Based on the complex database, the modified model is developed. Through pixel-based computing the sediment yield per hydrological unit is calculated. The model does not consider sediment deposition occurring on hillslopes. Gross soil loss is often higher than the sum of those measured at catchment outlets. The sediment delivery ratio （SDR） per hydrological unit is also computed. This study analyzes the main contributions of sediment yields on sub-basins of the Chaobaihe River to the Miyun Reservoir, and discusses the possible reasons for the difference between SDRs in 2001 and 2002 at different outlets. The result shows that in the upper basin of the Miyun Reservoir, in 2001 the area of erosion that could be neglected was 8,202.76 km^2, the area of low erosion 3,269.59 km^2, the area of moderate erosion 3,400.97 km^2, the area of high erosion 436.89 km^2, the area of strong erosion 52.19 km^2 and the area of severe erosion 3.13 km^2. The highest soil loss was 70,353 t/km^2. yr in Fengning County in 2001, followed by 64,418 t/km^2. yr by Chicheng County in 2001. The SDR in 2002 was lower than that in 2001. The main reasons are the decreasing rainfall erosivity and total runoff.     

Estimating rainfall erosivity by incorporating seasonal variations in parameters into the Richardson model

Rainfall erosivity is an important climatic factor for predicting soil loss. Through the application of high-resolution pluviograph data at 5 stations in Huangshan City, Anhui Province, China, we analyzed the performance of a modified Richardson model that incorporated the seasonal variations in parameters α and β. The results showed that (1) moderate to high seasonality was presented in the distribution of erosive rainfall, and the seasonality of rainfall erosivity was even stronger; (2) seasonal variations were demonstrated in both parameters α and β of the Richardson model; and (3) incorporating and coordinating the seasonality of parameters α and β greatly improved the predictions at the monthly scale. This newly modified model is therefore highly recommended when monthly rainfall erosivity is required, such as, in planning soil and water conservation practices and calculating the cover-management factor in the Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE).     

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 0-1 t ha~(-1) yr~(-1) 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(1984-2013) 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.