Land Use Cover Data Research Articles

Projecting future land use/land cover by integrating drivers and plan prescriptions: the case for watershed applications

Watershed planning is a pivotal exercise for all jurisdictions irrespective of size, landscape complexity, or other nuances. As a result of the intricate relationship between land use/land cover (LULC) and water resources, it becomes prudent to not only develop historical and contemporary LULC data for watershed planning purposes, but more importantly, the production of future LULC datasets has the potential to better inform watershed planners. This study explored an optimal workflow that can be adopted for the production of baseline LULC input images from a moderate spatial resolution sensor such as Landsat, and the identification, translation, and configuration of land change drivers and regional comprehensive plan prescriptions in the creation of future LULC data for a regional watershed. The study conducted in the Lower Chippewa River Watershed, Wisconsin, USA demonstrated that an object-based hybrid classification approach resulted in the generation of improved projected images with a 15% increase in area under the curve (AUC) value compared to a pixel-based hybrid classification method even though both methods displayed comparable overall image classification accuracies (≤ 1.8%). Results further displayed that configuring anthropogenic drivers in a trend format rather than individual year values can result in a more efficient training of a multi-layer perceptron neural network – Markov Chain model. The calibrated and validated model demonstrated that on average, residential, commercial, institutional, green vegetation/shrub, and industrial LULC are expected to grow through 2050, though at a slower rate (12%) compared to contemporary period (39%), while forest and agricultural lands are slated to decline (−2%).

Land-Use/Land-Cover Changes and Their Influence on the Ecosystem in Chengdu City, China during the Period of 1992–2018

Due to urban expansion, economic development, and rapid population growth, land use/land cover (LULC) is changing in major cities around the globe. Quantitative analysis of LULC change is important for studying the corresponding impact on the ecosystem service value (ESV) that helps in decision-making and ecosystem conservation. Based on LULC data retrieved from remote-sensing interpretation, we computed the changes of ESV associated with the LULC dynamics using the benefits transfer method and geographic information system (GIS) technologies during the period of 1992–2018 following self-modified coefficients which were corrected by net primary productivity (NPP). This improved approach aimed to establish a regional value coefficients table for facilitating the reliable evaluation of ESV. The main objective of this research was to clarify the trend and spatial patterns of LULC changes and their influence on ecosystem service values and functions. Our results show a continuous reduction in total ESV from United States (US) $1476.25 million in 1992, to US $1410.17, $1335.10, and $1190.56 million in 2001, 2009, and 2018, respectively; such changes are attributed to a notable loss of farmland and forest land from 1992–2018. The elasticity of ESV in response to changes in LULC shows that 1% of land transition may have caused average changes of 0.28%, 0.34%, and 0.50% during the periods of 1992–2001, 2001–2009, and 2009–2018, respectively. This study provides important information useful for land resource management and for developing strategies to address the reduction of ESV.

A study of model nighttime ozone bias in air quality modeling

This study investigated the causes behind the nighttime ozone biases in southeast Texas, including background ozone, the land use/land cover (LULC) change and the minimum nighttime eddy diffusivity coefficient (Kzmin) setting. It also tried to shed light on a historical issue of large ozone biases by tracing the history of Community Multiscale Air Quality (CMAQ) model's treatment of Kzmin. The ozone biases associated with the low and active ozone days are distinctively different through examination of their diurnal cycles. Hence the model background ozone bias is likely responsible for ozone bias at the regional level. Significant differences in PBL height, vertical mixing, dry deposition and spatial patterns of ozone biases were found in two LULC cases. Spatial patterns of nighttime eddy diffusivity and dry deposition are consistent with the underlying LULC data. By using the default Kzmin setting which lowering the nighttime vertical mixing, the average ozone bias is reduced as compared to the alternative Kzmin setting. The reduction is 3.7 ppb or 31% when the latest National Land Cover Database (NLCD) 2011 is used. Still biases as high as 20 ppb exist at a number of sites. There are minimal changes in ozone biases in inner city and coastal sites while the changes in some rural/suburban sites are substantial. Also higher sensitivity was observed when preceding day ozone was high. The results showed that both downward mixing of ozone above and dilution of surface NO are important processes for nighttime ozone. By comparing the spatial bias pattern of 2006 and 2015, we are fairly certain the high nighttime eddy diffusivity in the old simulation played a significant role in the large nighttime biases. The impacts from the uncertainties in dry deposition can be medium while uncertainties caused by model horizontal advection are likely less significant. Factors other than background ozone mostly contribute to the local level biases.

LAND USE, MICROCLIMATE, AND SURFACE RUNOFF LINKAGES: SPACE-TIME MODELING FROM ROKEL-SELI RIVER BASIN, SIERRA LEONE

Abstract. This study mainly utilized the Soil and Water Assessment Tool (SWAT) together with SWAT-CUP, both free and open source software (FOSS), to construct a distributed hydrologic flow model for the Rokel-Seli River basin, Sierra Leone, in a bid to spatiotemporally evaluate the role of changes in land use/land cover (LULC) and microclimate on streamflow regimes. The model was informed by LULC data derived from three Landsat satellite images collected in 2002, 2010, and 2016. The LULC data was generated with the aid of several python libraries accessed through the FOSS Anaconda Navigator. LULC change analysis demonstrated that between 2002 and 2016, urban, agricultural, water, and mining lands expanded significantly but forest cover reduced the most (−5.7 %). While average annual surface runoff dramatically increased from 2002 to 2010 (31.7 %), the period after the reservoir construction (2010–2016) recorded lower increase in surface runoff (0.9 %). Result of the study suggested that the construction of a major reservoir to support hydroelectricity in concert with significant loss of forest cover and shrub played a greater role in increasing surface runoff compared with the influence of microclimate.

Protected area effectiveness against land development in Spain

Land use-land cover (LULC) changes towards artificial covers are one of the main global threats to biodiversity conservation. In this comprehensive study, we tested a number of methodological and research hypotheses, and a new covariate control technique in order to address common protected area (PA) assessment issues and accurately assess whether different PA networks have had an effect at preventing development of artificial LULCs in Spain, a highly biodiverse country that has experienced massive socioeconomic transformations in the past two decades. We used digital census data for four PA networks designated between 1990 and 2000: Nature Reserves (NRs), Nature Parks (NPs), Sites of Community Importance (SCIs) and Special Protection Areas (SPAs). We analysed the effect of explanatory variables on the ecological effectiveness of protected polygons (PPs): Legislation stringency, cummulative legal designations, management, size, age and bio-physical characteristics. A multiple Before-After-Control-Impact (BACI) semi-experimental research design was used whereby artificial land cover increase (ALCI) and proportional artificial land cover increase (PALCI) results were compared inside and outside PAs, using 1 km and 5 km buffer areas surrounding PAs as controls. LULC data were retrieved from Corine Land Cover (CLC) 1990 and 2006 data. Results from three spatial-statistical models using progressively restrictive criteria to select control areas increasingly more accurate and similar to the assessed PPs were compared. PAs were a generally effective territorial policy to prevent land development in Spain. NRs were the most effective PA category, with no new artificial land covers in the assessed period, although exact causality could not be attributed due to legal overlaps. SPAs were the least effective category, with worse ALCI data than their control areas. Legal protection was effective against land development, which was influenced by most bio-physical variables. However, cumulative legal designations and PA management did not seem to influence land development. The spatial-statistical technique used to make cases and control environmentally similar did not produce consistent outcomes and should be refined.

Effects of riverine landscape changes on pollination services: A case study on the River Minho, Portugal

Riverine landscapes provide numerous ecosystem services, of which pollination is essential to increase the yield, quality, and stability of crops and the biodiversity of wild flora. Pollinators’ behaviour and their community dynamics are often affected by the spatial distribution of floral and nesting resources at the landscape scale, and are thus sensitive to land-use land-cover (LULC) changes and management practices.The main objective of this study is to understand how changes in riparian vegetation and LULC at River Minho affect pollination services. For this, an approach based on the temporal analysis of historical cartography (1898) and current satellite imagery (2016) was used. We developed a novel Pollination Suitability Index for Riverine Landscapes (PSIRL) using LULC data as a proxy of habitat suitability for insect pollinators. PSIRL scores were derived from expert judgment of the local habitat conditions, floral diversity and field surveys. The PSIRL values obtained for both periods were compared using pairwise T-tests.We detected an overall reduction of the pollination services in the riverine landscape of River Minho from 1898 to 2016. However, at local level we observed gains and losses of pollination suitability as a result of distinct LULC changes. There was a marked decrease in the PSIRL in the most downstream and upstream riverine sections due to generalized conversion of scrublands, broadleaved-forests and heterogeneous agricultural areas to artificial surfaces and managed forests dominated by maritime pine and eucalyptus. However, in the middle section of the river, the area occupied by scrublands, riparian and broadleaved forests increased, resulting in an increase of the foraging resources for insects, and a consequent increase of the PSIRL.This index can be used to monitor and capture changes in the pollination suitability of complex riverine landscapes and support further land and river management decisions. The historical cartography proved to be a valuable source of information to characterize LULC change and hence to assess the evolutionary trajectory of pollination ecosystem services, at global and local level.

Integrating global land-cover and soil datasets to update saturated hydraulic conductivity parameterization in hydrologic modeling

Soil properties play an important role in watershed hydrology and environmental modeling. In order to model realistic hydrologic processes, it is necessary to obtain compatible soil data. This study introduces a new method that integrates global soil databases with land use/land cover (LULC) databases to better represent saturated hydraulic conductivity (Ks) which is one of the most important soil properties in hydrologic modeling. The Ks is modified by means of uniting physical infiltration mechanisms with hydrologic soil-LULC complexes from lookup tables from USDA-SCS (1985). This approach enables assimilation of available coarse resolution soil parameters by the finer resolution global LULC datasets. In order to test the performance of the proposed approach, it has been incorporated into the Watershed Environmental Hydrology (WEHY) model to simulate hydrologic conditions over the Cache Creek Watershed (CCW) and Shasta Dam Watershed (SDW) in Northern California by means of different soil datasets. Soil dataset S1 was obtained from the local soil database including SSURGO (Web soil survey, USDA). The second soil dataset (S2) is the global ISRIC soil data SoilGrids-1km obtained from World Soil Information. Soil dataset S4 is global FAO soil data. The third (S3) and fifth (S5) soil datasets were calculated by integrating the LULC into global soil datasets (S2, S4), respectively. The results of this study suggest that the proposed approach can provide a fine resolution soil dataset through integration of LULC and soil data, which can improve the estimation of soil hydraulic parameters and the performance of hydrologic modeling over the target watersheds. Within this framework, the new approach of this study can be applied widely in many parts of the world by means of the global soil and LULC databases.

A three-pronged approach for identifying source and extent of nitrate contamination in groundwater

A method to delineate the nitrate (NO3−) contamination of a study area by sequential and statistical use of dual isotope data, principal component analysis (PCA), and land use/land cover (LULC) data was demonstrated using data from Eumseong, Korea. First, a dual isotope approach was applied to identify the possible NO3− sources and quantify their contribution to NO3− contamination using Bayesian statistics. Second, a PCA was performed to discriminate and evaluate the impact of NO3− contamination on chemical evolution in the aquifer. Lastly, we incorporated the LULC data into a regression analysis to identify the contribution of various land uses to NO3− recharge. Some samples had NO3− and iron (Fe) concentrations above the local drinking water quality standard, and the distributions of potassium (K+), sulfate (SO42−), Fe, and manganese (Mn) were skewed significantly. Trends from the dual isotope analysis suggested three major sources of NO3− contamination. Among the three sources, Bayesian statistics indicated that the NO3− contamination was largely attributable to influx from manure/sewage and soil nitrogen (N). Based on the PCA, following screening for skewed data, a contamination indicator was extracted. The indicator exhibited positive correlations with NO3−, chlorine (Cl−), strontium (Sr2+), calcium (Ca2+), sodium (Na+), magnesium (Mg2+), K+, and SO42−, suggesting a single strong source. The regression analysis, using the LULC information, determined that agriculture activities in nonpaddy areas were responsible for the NO3− recharge. This study identified the benefits of combining dual isotope analysis, PCA, and LULC data for discriminating and evaluating sources of NO3− contamination when diverse contaminants are involved in geochemical evolution.

Land Use/Land Cover Change Modeling and the Prediction of Subsequent Changes in Ecosystem Service Values in a Coastal Area of China, the Su-Xi-Chang Region

Monitoring the impact of current Land Use/Land Cover (LULC) management practices on future Ecosystem Services (ESs) provisioning has been emphasized because of the effect of such practices on ecological sustainability. We sought to model and predict the impacts of future LULC changes on subsequent changes in Ecosystem Service Value (ESV) in fragile environments undergoing complex LULC changes, Su-Xi-Chang region. After mapping and classifying the LULC for the years 1990, 2000, and 2010 using GIS and remote sensing, a Cellular Automata (CA)–Markov model was employed to model future LULC changes for the year 2020. ESV was predicted using the projected LULC data and the modified ES coefficients adopted by Xie et al. (2003). The projected results of the changes in LULC reveal that construction land expanded extensively, mainly at the expense of farmland, wetland, and water bodies. The predicted results of the ESVs indicate that water bodies and farmland are the dominant LULC categories, accounting for 90% of the total ESV. Over the study period, ESVs were diminished by 7.3915 billion CNY, mostly because of the decrease in farmland, water bodies, and wetland. A reasonable land use plan should be developed with an emphasis on controlling construction land encroachment on farmland, wetlands, and water bodies. The rules of ecological protection should be followed in LULC management to preserve ecological resources.

Impacts of incorporating dominant crop rotation patterns as primary land use change on hydrologic model performance

Abstract Crop rotation is a commonly used management practice in the central U.S. whereby different agricultural crops are grown on a farm field in successive years to help maintain the productive capacity of the soil. Understanding hydrologic responses to crop rotation is critical for developing appropriate crop rotation strategies for sustainable water resources management. These responses are not well understood due to limited availability of multi-year, crop-specific land use-land cover data. The study focuses on the Smoky Hill River watershed in west-central Kansas, a typical agro-ecosystem watershed in the Central Great Plains of the U.S. A multi-year land use dataset developed for the state of Kansas was used to identify 3-year crop rotation patterns in the watershed for three different periods (2006–08, 2008–10, 2010–12). Out of 276 unique rotations, 21 rotations were found to be dominant, with rotation patterns of grain sorghum-fallow-winter wheat (G-F-W), winter wheat-winter wheat-fallow (W-W-F), and continuous winter wheat (W-W-W) occupying more than 81% of the cropland in each rotation period. From 2006 to 2012, the coverage of grain sorghum increased by 26% and corn by 305%, while winter wheat decreased by 20%. Three 11-year simulation scenarios based on three 3-year crop rotation patterns were implemented in the SWAT model and separately calibrated for streamflow at two gauge sites. The SWAT model produced good calibration statistics for the periods corresponding to the applied crop rotations, while the statistics tended to decrease for other periods, with Nash-Sutcliffe Efficiency coefficient becoming unsatisfactory for periods of six to eight years outside of the calibration period. Applying a different number of dominant crop rotations to the SWAT model, it was found that the number can be reduced down to the five most dominant without significant loss in model performance. While the optimum number of crop rotations must be evaluated for each agricultural watershed, this finding provides valuable information for watershed model development and calibration, which can help to avoid issues of over-parameterization and equifinality.

Effects of land-use land-cover data resolution and classification methods on SWAT model flow predictive reliability

The aim of this study is to evaluate the predictive reliability of the soil and water assessment tool (SWAT) model based on LULC data spatial resolution and image classification methods. The predictive reliability of the model is primarily evaluated with two descriptive statistics; the p-factor and the r-factor. The p-factor quantifies the percentage of the observed data that a calibrated model captures whereas the r-factor quantifies the level of uncertainty associated with the calibrated model. The hypothesis is that a combination of GIS-based hydrologic modelling and the promise of high-resolution LULC data obtained through object-oriented image analysis (OOIA) significantly improves SWAT flow predictive reliability. Two SWAT models were setup and calibrated at a gauging station located within the study area. After both manual and auto-calibration, results showed that the low-resolution model had a slightly better predictive reliability. The impact of the classification methods is however unclear.

Distribución espacial de las plantaciones forestales al sur de Chile, zona con presencia de una planta de celulosa

The increase in industrial forest plantations in southern Chile and the alteration of the landscape have been an ongoing concern of researchers. In this paper, spatial statistical techniques were used to describe the composition of the territorial patterns of forest plantations, in an area with a pulp mill and with high demand for exotic species such as Pinus radiata and Eucalyptus. GeoDa software was used to calculate global and local Moran indexes. Land-use cover data and the area of the forest plantation polygons updated in the period 2006- 2013 were used. The global Moran index indicates that plantation distribution occurs as a random phenomenon throughout the territory. In its interior there are different structures, represented by enclaves of large areas, small polygons surrounded by others of great size and large polygons surrounded by small ones, as well as a fragmentation throughout the territory, composed of polygons of various sizes and neighborless. The dynamics of change in the period 2006-2013 indicates that plantation cover presented the largest increase (6,578 ha) due to the reduction in native forest and grassland-shrubland covers.

A Three-Layered Graph-Based Learning Approach for Remote Sensing Image Retrieval

With the emergence of huge volumes of high-resolution remote sensing images produced by all sorts of satellites and airborne sensors, processing and analysis of these images require effective retrieval techniques. To alleviate the dramatic variation of the retrieval accuracy among queries caused by the single image feature algorithms, we developed a novel graph-based learning method for effectively retrieving remote sensing images. The method utilizes a three-layer framework that integrates the strengths of query expansion and fusion of holistic and local features. In the first layer, two retrieval image sets are obtained by, respectively, using the retrieval methods based on holistic and local features, and the top-ranked and common images from both of the top candidate lists subsequently form graph anchors. In the second layer, the graph anchors as an expansion query retrieve six image sets from the image database using each individual feature. In the third layer, the images in the six image sets are evaluated for generating positive and negative data, and SimpleMKL is applied to learn suitable query-dependent fusion weights for achieving the final image retrieval result. Extensive experiments were performed on the UC Merced Land Use-Land Cover data set. The source code has been available at our website. Compared with other related methods, the retrieval precision is significantly enhanced without sacrificing the scalability of our approach.

Spatial variations in water quality of river Ganga with respect to land uses in Varanasi.

Water quality of a river is a function of surrounding environment and land use due to its connectivity with land, resulting in pollutants finding their way through land. This necessitates a spatially explicit study of river ecology. The paper presents a pioneer study to establish and explore the linkage between land use and water quality of river Ganga in Varanasi district. The land use land cover (LULC) map of 20km of river stretch for buffer radii of 1000m in Varanasi revealed that riparian vegetation is negligible in the district. The hierarchical cluster analysis of LULC data suggested that there are two major land use categories, viz., urban and agriculture. The land use wise principal component analysis (PCA) suggested that urbanized areas are major contributor of metals, whereas agricultural land contributes organic matter into the river. The Spearman correlation study revealed that with rising urbanization, the pollutant load into the river increased compared to that from agricultural land use. The statistical analysis of the data clearly concluded that water quality of river Ganga at Varanasi was a function of adjacent land use. The study provides an insight anticipating the Indian government to embrace the relationship of land use to river water quality while formulating policies for the upcoming River Regulation Zone.

The response of runoff and sediment loading in the Apalachicola River, Florida to climate and land use land cover change

AbstractThe response of runoff and sediment loading in the Apalachicola River under projected climate change scenarios and land use land cover (LULC) change is evaluated. A hydrologic model using the Soil and Water Assessment Tool was developed for the Apalachicola region to simulate daily runoff and sediment load under present (circa 2000) and future conditions (2100) to understand how parameters respond over a seasonal time frame to changes in climate, LULC, and coupled climate/LULC. The Long Ashton Research Station‐Weather Generator was used to downscale temperature and precipitation from three general circulation models, each under Intergovernmental Panel on Climate Change (IPCC) carbon emission scenarios A2, A1B, and B1. Projected 2100 LULC data provided by the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center was incorporated for each corresponding IPCC scenario. Results indicate that climate change may induce seasonal shifts to both runoff and sediment loading. Changes in LULC showed that more sediment load was associated with increased agriculture and urban areas and decreased forested regions. A nonlinear response for both runoff and sediment loading was observed by coupling climate and LULC change, suggesting that both should be incorporated into hydrologic models when studying the future conditions. The outcomes from this research can be used to better guide management practices and mitigation strategies.

Designing an Experiment to Investigate Subpixel Mapping as an Alternative Method to Obtain Land Use/Land Cover Maps

Various subpixel mapping (SPM) methods have been proposed as downscaling techniques to reduce uncertainty in classifying mixed pixels. Such methods can provide category maps of a higher spatial resolution than the original input images. The aim of this study was to explore and validate the potential of SPM as an alternative method for obtaining land use/land cover (LULC) maps of regions where high-spatial-resolution LULC maps are unavailable. An experimental design was proposed to evaluate the feasibility of SPM for providing the alternative LULC maps. A case study was implemented in the Jingjinji region of China. SPM results for spatial resolutions of 500–100 m were derived from a single 1-km synthetic fraction image using two representative SPM methods. The 1-km synthetic fraction image was assumed to be error free. Accuracy assessment and analysis showed that overall accuracies of the SPM results were reduced from about 85% to 75% with increasing spatial resolution, and that producer’s accuracies varied considerably from about 62% to 93%. SPM performed best when handling areal features in comparison with linear and point features. The highest accuracies were achieved for areas with the lowest complexity. The study concluded that the results from SPM could provide an alternative LULC data source with acceptable accuracy, especially in areas with low complexity and with a large proportion of areal features.

Assessing the long-term impact of urbanization on run-off using a remote-sensing-supported hydrological model

Land use/land cover (LULC) change in Dongguan, a city belonging to the Pearl River Delta metropolitan area, and its impact on the run-off of this city since the rapid urbanization period starting in 1979 are analysed in this article. Historic remote-sensing imagery (acquired in 1979, 1989, 2000, 2006, and 2013) was processed by the Support Vector Machine (SVM) method to obtain LULC data, and a long-term hydrologic impact assessment (L-THIA) model was applied to evaluate the long-term effects of LULC changes on surface run-off. The results show the urbanized area in Dongguan has increased more than 52% from 1979 to 2013 and the percentages of annual surface run-off depth and annual surface run-off coefficient are 58% and 5.83%, respectively. The increase in annual surface run-off is related to urbanization, and the centre area of the city has experienced the largest increase in annual surface run-off. The 32 towns’ local indicators of spatial association show centre towns are hot spots of increases in annual surface run-off in Dongguan city.

Combining national forest type maps with annual global tree cover maps to better understand forest change over time: Case study for Thailand

National and global land use/land cover (LULC)/LULC change (LULCC) data sets often have different strengths and weaknesses for monitoring forest change over time. For example, a national-level map may be very detailed in terms of number and type of forest-related LULC classes, but infrequently updated compared to a global map with fewer LULC classes (e.g. percent tree cover maps or forest/non-forest maps). So, additional useful information might be gained by integrating national and global LULC data sets. As a demonstration, in this study a national forest type map of Thailand from the year 2000 was combined with annual global tree cover maps for the years 2000–2012 to obtain multi-temporal information on forest change in Thailand and to create a baseline estimate of forest change to 2020 (i.e. with no new policy interventions). Results showed that all forest types experienced declines in area from 2000 to 2012, with the greatest area losses for Mixed Deciduous Forests (−137,765 ha) and the greatest percentage losses for Swamp Forests (−5.8%). Annual forest losses, in general, increased at a near-linear rate from 2000 to 2012, and are projected to increase from 39,290 ha/year in 2012 to 51,775 ha/year by the end of 2015 (an increase of 31.8%) and 66,945 ha/year by 2020 (an increase of 70.4%) based on linear extrapolation of the historical trend. For comparison, net forest loss is currently around 5,211,000 ha/year at the global level and 677,000 ha/year at the South and Southeast Asia regional level (Food and Agriculture Organization of the United Nations, 2010b). The methods presented here provide a computationally-simple approach to annually update existing forest maps and estimate future forest change using free global tree cover data.

Estimation of Soil loss Using USLE Model for Kulhan Watershed, Chattisgarh- A Case Study

Soil erosion is the common land degradation problem in the worldwide because of its economic use and environmental impacts. To estimate soil erosion and to establish soil erosion management plans, many computer models have been developed and used. Remote Sensing (RS) and Geographic Information System (GIS) being a well-known tool available for dealing with the major water resources problems, it is used in the present study. The objective of the present study is to estimate the annual soil loss using USLE model for Kulhan watershed of Shivnath basin, sub-basin of Mahanadi basin, Chhattisgarh using RS and GIS techniques. Land useland cover and topographical data for the study area was derived using GIS and carried out geographical data analysis. Data collection includes for the study were annual rainfall data, digital elevation model (DEM) from shuttle radar topographic mission (SRTM), land-use classification map and soil series maps were obtained from Chhattisgarh Council of Science and Technology (CCOST), Raipur, Chhattisgarh.The five major input parameters used in the study are rainfall erosivity factor (R), Length slope factor (LS), soil erodability factor (K), vegetation cover factor (C) and erosion control factor (P). The rainfall erosivity factor had been determined from annual rainfall data of study area. The soil survey data was used to develop the soil erodability factor and DEM of study area was used to generate topographic factor (LS). The value of cover management factor and support practice factor were obtained from land use land cover map. After generation of input parameters, analysis was performed for estimation of soil erosion using USLE model by spatial information analysis approach. The quantitative soil loss (t/ha/year) ranges were estimated and classified the watershed into different levels of soil erosion severity and also soil erosion index map was developed. The watershed is classified according to Indian condition as suggested by Singh et, al.(2002) into different erosion classes such as (>5) slight, (5-10) moderate, (10-20) high, (20-40) very high, (40-80) sever, (>80) very severe. It was found that average annual soil erosion for study area is 0.1783t/ha/yr and 83.97% of total area is under slight erosion risk class and only 0.45% of total area is under very severe class which is near the bank of main stream line.

On the effects of scale for ecosystem services mapping.

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.