Remote sensing and GIS based assessment of soil erosion and soil loss risk around hill top surface mines situated in Saranda Forest, Jharkhand

Soil erosion is a naturally occurring process on all land, but nowadays it is accelerating due to anthropogenic activities such as mining, deforestation, construction, nuclear power plants, agricultural pesticides and malpractices, overstocking and overgrazing, etc. So it has become a critical issue to manage and restore the degrading land along with conserving the environment. Soil erosion causes two sets of problems, viz., on-site problems and off-site problems. On-site problems include loss of agricultural land productivity, soil quality, water-holding capacity, nutrient loss, etc., and off-site problems include movement of sediment, which causes flooding and the silting up of reservoirs and river beds, damage to structures, diversion of streams due to deposition, etc. Soil erosion generally causes more major downstream or off-site damage than on-site damage (Krishnabahadur 2009). Soil eroded by water and scouring of sediment gets deposited in river beds, dams, nallas, streams, and so on, thus increasing their level, and can cause floods in different regions. Soil, water and vegetation management at basin scale is required for the mitigation of soil erosion problems (Alejandra 2008).

There are several methods to determine the annual soil erosion and sediment yield rate, each with their pros and cons (Morgan 2005). These models include the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation, the Morgan, Morgan and Finney method (MMF), the Water Erosion Prediction Project, and the Griffith University Erosion Sedimentation System.

In the present study, the USLE and MMF mathematical models were selected to be applied to a small mining watershed for predicting the average annual soil loss within the watershed, because of their simplicity and lower data requirements. Some models are more detailed and process-based soil erosion predictions, which are able to simulate the effects of vegetation on soil erosion in individual storms, but these are too complex and data hungry (Emil et al. 2012). Moreover, USLE produces the best available estimates, not absolutes, and is mostly used for estimating sheet and rill erosion from agricultural fields under specific conditions for a small watershed. It is generally most accurate for medium-textured soils, with slope lengths of less than 130 m and gradients of 3–18%, whereas the MMF model is a physically based-empirical model, providing simplicity, flexibility and a stronger physical base than USLE. Most importantly, it was developed to predict annual soil loss from field-sized areas on hill slopes (Morgan 2005), and, in the present study area, most of the parts of the watershed fall into the high topography region.

Recent advances in remote sensing (RS), geographic information system (GIS) technology and expert systems provide efficient tools to facilitate decision-making for environmental management. These tools are currently integrated to establish an environmental information system, which permits testing and evaluation of alternative management scenarios (Okan & Nilgun 2002). The RS technique makes it possible to measure hydrologic parameters at spatial scales, while GIS integrates the spatial analytical functionality for spatially distributed data (Alejandra 2008). The USLE and MMF models and RS-GIS together provide a sophisticated tool for estimating gross erosion and sediment loads (Okan & Nilgun 2002; Pandey et al. 2007).

Three types of data were used mainly for the study of soil erosion mapping, CartoSat-1 (IRS-P5), RESOURCESAT-1 (IRS P6) LISS IV (Linear Imaging Self-Scanning Sensor) and Landsat Enhanced Thematic Mapper Plus imageries. Toposheets of 1:50,000 scale covering the entire study area were collected from SOI, Kolkata. A soil map of the study area at 1:250,000 scale was collected from the National Bureau of Soil Science and Land Use Planning, Nagpur. Daily rainfall data were procured from the India Meteorological Department, Pune for the period of 2001 and 2008. A Global Positioning System survey and photographs were carried out in the study area for collection of the soil sample, ground truth data, etc.

USLE is a mathematical model (Wischmeier & Smith 1978) used for predicting the long-term average annual soil erosion on a field. The USLE model takes into account several determining factors, such as the soil erodibility factor, rainfall intensity factor, slope length and steepness factor, cover and management factor and support practice factor, and many authors have used this for soil loss prediction (Arnoldus 1977; Rao 1981; Khan et al. 2001; Serwan & Kamaruzaman 2001; Alejandra 2008; Kinnell 2010). The USLE is an empirical equation that computes the mean annual soil loss in tons/ha.

Any given soil surface has an inherent erodibility under standard conditions that depends on soil characteristics. A soil map of the study area is shown in Figure 5(b). For the research study, the organic matter content and soil property were used to decide the value of the K factor. Hence, soil samples were collected from different locations within the watershed, and the organic matter content (%) was estimated and was found to be 2%. The K factor values were selected from those provided by the Science Division, Indian Institute of Remote Sensing (IIRS), Dehradun, and are given in Table 1 (Alejandra 2008).

These C and P factor values have been decided on the basis of values that were suggested by authors Rao (1981), Morgan (2005), Jasrotia & Singh (2006), Alejandra (2008), Ghosh & Guchhait (2012) and Gebreyesus et al. (2014) as in Table 2.

The MMF model to predict annual soil loss from field-sized areas on hillslopes was developed by Morgan et al. (1984). The approach was revised by Morgan (2001). The model classifies the soil erosion process into a water phase and a sediment phase (Morgan 2005). The sediment phase is a simplification of the scheme described by Meyer & Wischmeier (1969). The water phase involves calculation of rainfall energy and runoff volume within the watershed; these are the inputs to a sediment phase that comprises three predictive equations, one for the rate of particle detachment by rain splash, one for the rate of particle detachment by runoff, and one for the transport capacity of overland flow (Morgan 2005). All the parameters that are required for the MMF model are provided in Tables 2 and 3. All the maps are analyzed in the GIS platform to achieve the appropriate and spatial soil erosion prediction within the watershed. The model uses 12 operating functions, for which 19 input parameters are required (Morgan 2005). Like USLE, the model cannot be used to predict soil loss from individual storms or from gully erosion. There are several authors (Jasrotia & Singh 2006; Ghosh & Guchhait 2012; Gebreyesus et al. 2014), who have used the MMF model to predict soil loss.

ER = effective rainfall (mm); LD = leaf drainage (mm); DT = direct through fall (mm); KE = kinetic energy of the rainfall (J m⁻²); Q = volume of overland flow (mm); R = annual or mean annual rainfall (mm); R_n = number of rain days per year; MS = soil moisture content at field capacity; BD = bulk density of the top soil layer (Mg m⁻³); EHD = effective hydrological depth of soil (m); K = soil detachability index (g J⁻¹); COH = cohesion of the surface soil (kPa);

C = crop cover management factor; CC = percentage canopy cover; GC = percentage ground cover; PH = plant height (m); N = Number of consecutive years; F = annual rate of soil particle detachment by raindrop impact (kg m⁻²); H = annual rate of soil particle detachment by runoff (kg m⁻²); J = annual rate of total soil particle detachment (kg m⁻²); G = annual transport capacity of overland flow (kg m⁻²); SD = total soil depth (m); S = slope steepness (°); W = rate of increase in soil depth by weathering at the rock–soil interface (mm yr⁻¹); V = rate of increase in effective hydrological layer (mm yr⁻¹); Et/Eo = ratio of actual (Et) to potential (Eo) evapotranspiration; I = typical value for the intensity of erosive rain (mm hr⁻¹), 25 for tropical climates.

Soil erosion maps based on the USLE model of the study area were generated from the union of all the factors of USLE (Equation (1)). Average annual soil losses were grouped into different scales as suggested by Singh et al. (1992) and Dabral et al. (2008) for Indian conditions. They were classified into different scales such as slight (0 to 5 t ha⁻¹ yr⁻¹), moderate (5–10 t ha⁻¹ yr⁻¹), high (10–20 t ha⁻¹ yr⁻¹), very high (20–40 t ha⁻¹ yr⁻¹), severe (40–80 t ha⁻¹ yr⁻¹) and very severe (greater than 80 t ha⁻¹ yr⁻¹), and the percentages of the area that are covered by each scale are shown in Table 4.

Average annual soil losses were estimated by using the grid cells of the watershed to identify the critical areas that are susceptible to more soil erosion for the purposes of environmental planning and management.

Average annual soil loss from all the grid cells within the watershed was estimated to be 48.56 t ha⁻¹ yr⁻¹ in 2001 and 78.38 t ha⁻¹ yr⁻¹ in 2008. The percentage increase in average annual soil erosion between 2001 and 2008 is about 61%, as shown in Table 5. This has happened due to a marked increase in, among other things, mining activities and deforestation without any preventive measures, which led to migration of sediment from high elevation situated mines to settlement on the low land areas, especially on the river bank, agricultural and forest-dominated area, and consequently the agricultural land, forest and grass area which were converted to barren land.

Average annual soil loss within the watershed varies from place to place; maximum soil erosion was found in the mining region for both years 2001 and 2008. In 2001 other areas outside the mining region are less prone to soil erosion, but it expanded to almost all the watershed areas in 2008 due to siltation and sediment migration from the waste disposal of mining areas, which further acted as the eroding agent in the lower parts of the watershed. Thus more areas of the watershed are susceptible to soil erosion in 2008 compared to 2001.

A comparative analysis of the USLE and MMF mathematical model-based soil erosion predictions is presented in Table 6, which shows a comparison of pixels soil loss within the watershed.

The MMF-based soil erosion predictions are on average higher compared with those from the USLE model. In the MMF model, the pixel showing the highest value for soil detachment was 800 t ha⁻¹ yr⁻¹ (2001) and 706 t ha⁻¹ yr⁻¹ (2008), whereas the lowest value was 0 t ha⁻¹ yr⁻¹ in both years. In the case of the USLE model, the pixel showing the highest value of soil erosion was 10,564 t ha⁻¹ yr⁻¹ (2001) and 13,338 t ha⁻¹yr⁻¹ (2008) and the lowest was 0 t ha⁻¹ yr⁻¹ in both years. There is a lot of variation between the results predicted by both models in both years; the overall coefficient of variance is higher in the USLE (3.88 and 1.94) model than the MMF (0.71 and 0.85) model. Increases in the rate of soil detachment are different in both models over the past 7 years, however both models predict the increase in soil erosion within the watershed.

The USLE model revealed a higher percentage of the area of the watershed in the erosion class with a soil loss rate of <5 t ha⁻¹ yr⁻¹, whereas the MMF model revealed a higher percentage of the area of the watershed in the erosion class with a soil loss rate of <80 t ha⁻¹ yr⁻¹. Based on Figures 9 and 13, in general the MMF model predicted higher soil loss rates compared with the USLE model.

The correlation coefficient between the MMF and USLE models revealed a low R² equal to 0.10 in 2001 and 0.36 in 2008, which also means that the predictions of the two models are significantly different. A higher erosion rate in the watershed may lead to better correlation between these models. In saying that, both models predicted an increase in the soil erosion rate within the watershed. Deforestation and excavation in the mining region are the main factors contributing to the enhancement of the soil erosion in that area. After establishing the soil erosion rating classes, it is important to know which types of LULC, slope, soil type etc., contribute most to the soil erosion in order to provide the framework for planning and management of conservation strategies within the watershed.

A region with an erosion rate of more than 5 t ha⁻¹ yr⁻¹ is referred to as a High Erosion Potential Zone (Dabral et al. 2008). From the analysis of the watershed, it has been identified that nearly 77% (by both models) of the watershed area in 2008 is susceptible to soil erosion, having an average annual soil loss of more than 5 t ha⁻¹ yr⁻¹. These areas are required to be treated by adopting soil conservation measures within the watershed, Locations/sites for different soil conservation structures need to be identified based on the land use, land cover, soil map, drainage pattern, soil conservation potential zone and slope map criteria (Dilip & Venkatesh, 2004). The watershed is morphologically high in topography in which mines are situated at the highest elevation of the watershed and from where all waste and dumped soil deposits were migrating downward toward the region of the watershed. The slope at the mines' side to the downward part of the watershed is steep, hence bench terracing and staggered trenching would be necessary in this area. However, for effective management and planning, the sites suitable criterion for the selection of different structures should be based on the soil erosion, soil type, slope, rainfall and lithology of the location within the watershed. This procedure is recommended for the management and planning of the catchment area to protect the river basins and water sources.

RS and GIS tools were used to estimate the average annual soil loss spatially, with specific magnitudes within the watershed for 2001 and 2008. From these, it could be concluded that the average annual soil loss risk is being increased within the watershed, not only throughout the mining areas but also extending to all parts of the watershed. In 2001, the soil erosion map showed that soil erosion was significant in mining areas only. After 7 years, soil erosion had been dispersed over almost the whole watershed due to waste rocks or sediments that may have migrated downwards in the watershed from the hilltop mines. In 2008, average annual soil erosion had increased by 61% (USLE based) and 16% (MMF based) compared with that in 2001. The prominent resources of the watershed are forest and agricultural land. These are reducing due to the increase in other classes such as mine and broken land and silt deposited land, which consequently affects the soil erosion in the watershed. It has been found that MMF-based soil erosion prediction was higher compared to USLE-based soil erosion prediction. The coefficient of variation of soil erosion prediction based on the MMF model was less than the USLE-based soil erosion prediction. The coefficient of correlation shows that in both models, soil erosion predictions are significantly different. However, both models predicted that soil erosion was increasing within the watershed. The method developed for soil erosion mapping provides the basic information that will help in watershed development and management. The development sector can use such information to decide on soil conservation measures, land reclamation and restoration work, environmental management plans, and so on. The main causes of the land degradation and increase in soil erosion in the study area are removal of vegetation, expansion of mining activities, and a lack of regulatory requirements for reclamation. The lack of understanding of the long-term effects of erosion will result in areas being left un-reclaimed. Not only the mining companies, but also the forest department and local self-governments must adopt adequate policies for reclamation and restoration of the erosion-affected areas near mining fields. This problem needs to be seriously studied, through multi-dimensional fields including socio-economic, in order to preserve the newly reclaimed land, maintain soil conservation practices and reduce the soil erosion. It is concluded that a study should be initiated to accurately estimate the impacts of soil erosion near the hilltop mining areas in the country and assess the carrying capacities of the drainage patterns to accommodate the extent of anthropogenic activities in a mining region. Any further industrialization within watersheds must be governed by appropriate policies. It is also concluded that RS and GIS with the USLE model are suitable tools to determine the soil erosion in high terrain and remote parts where field visit analysis is difficult.

The authors acknowledge SAIL-RMD Kiriburu – Meghahatuburu for sponsoring the study and the Department of Mining Engineering, IIT Kharagpur for providing the necessary facilities and support to carry out this research work.