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Erosion following human disturbance threatens ecosystem health and inhibits effective land use. Mountaintop removal/valley fill (MTR/VF) mined landscapes of the Appalachian Coalfields region, USA, provide a unique opportunity to quantify the geomorphic trajectory of disturbed lands. Here we assess how MTR/VF-induced changes to topography and vegetation influence spatiotemporal erosion patterns in five mined watersheds. We use landscape evolution models starting from pre- and post-MTR/VF topographic data to isolate the influence of mining-induced topographic change. We then constrain ranges of erodibility from incision depths of gully features on mine margins, and use those estimates to model the influence of vegetation recovery trends on erosion. Topographic alterations alone reduce total sediment export from mined catchments. Model runs that incorporate the disturbance and recovery of vegetation in mined watersheds show that complete vegetation recovery keeps millennial sediment export from mined catchments within the range of unmined catchments. If vegetation recovery is anything less than complete, vegetation disturbance drives greater total sediment export from mined catchments than unmined catchments. Full vegetation recovery causes sediment fluxes to decline over millennia beyond the recovery period, while watersheds without full recovery experience fluxes that increase over the same time period. Spatiotemporal erosion trends depend on 1) the extent of vegetation recovery and 2) the extent to which MTR/VF creates slope–area disequilibrium. Valley fills and mine scarps experience erosion rates several times higher than those found in the unmined landscapes. Rapid erosion of mined areas drives deposition in colluvial hollows, headwater stream valleys, and below scarps. Our experiments suggest that reclamation focused on maximizing vegetation recovery and reducing hotspots of slope–area disequilibrium would reduce MTR's influence on Appalachian watersheds both

Environmental degradation and soil erosion begins when production forests are harvested. Unfortunately, logging cannot be avoided in plantation forests and since this operation can render the land more susceptible to erosion, any negative impacts need to be addressed properly. Erosion potential is predicted by evaluating the response of land cover, soil and slope to the impact of rainfall and human activities. The role of remote sensing and geographical information systems (GIS) in erosion prediction is to collect information from images and maps; combine and analyse these data so that it is possible to predict the erosion risk. The objective of this study was to produce a method to identify areas most susceptible to erosion and predict erosion risk. It is intended that the method be used particularly by forestry planners and decision makers so that they can improve forest management, especially during logging. The study area was within Kebumen and Banjarnegara districts of Central Java, Indonesia. Imagery used included a Landsat 7 satellite image (28th April 2001) and panchromatic aerial photos (5th July 1993). Other data was derived from topographical, soil, and geological maps, and 10 years of daily rainfall data from 17 rainfall stations. Predicting erosion in this study was done by combining rainfall, slope, geology, and land cover data. The erosion risk was predicted using land cover and soil type and depth. A rainfall map was generated using a thin plate spline method. A slope map was derived from a DEM which was generated by digitizing contours and spot heights from topographic maps. A geological map was derived from Landsat image classification with assistance from a 1:100000 scale geological map; and a land cover map was produced from an interpretation of the Landsat image and aerial photographs. A stratified classification technique was used to delineate land covers in the study area with an accuracy of 44%. The low accuracy could be attributed to the com