4,052 research outputs found

    Soil erosion in the Alps : causes and risk assessment

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    The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

    Mapping landscape-scale peatland degradation using airborne lidar and multispectral data

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    This is the final version. Available on open access from Springer via the DOI in this recordContext An increased interest in the restoration of peatlands for delivering multiple benefits requires a greater understanding of the extent and location of natural and artificial features that contribute to degradation. Objectives We assessed the utility of multiple, fine-grained remote sensing datasets for mapping peatland features and associated degraded areas at a landscape-scale. Specifically, we developed an integrated approach to identify and quantify multiple types of peatland degradation including: anthropogenic drainage ditches and peat cuttings; erosional gullies and bare peat areas. Methods Airborne LiDAR, CASI and aerial image datasets of the South West UK, were combined to identify features within Dartmoor National Park peatland area that contribute to degradation. These features were digitised and quantified using ArcGIS before appropriate buffers were applied to estimate the wider ecohydrologically affected area. Results Using fine-scale, large-extent remotely sensed data, combined with aerial imagery enabled key features within the wider expanse of peatland to be successfully identified and mapped at a resolution appropriate to future targeted restoration. Combining multiple datasets increased our understanding of spatial distribution and connectivity within the landscape. An area of 29 km2 or 9.2% of the Dartmoor peatland area was identified as significantly and directly ecohydrologically degraded. Conclusions Using a combination of fine-grained remotely sensed datasets has advantages over traditional ground survey methods for identification and mapping of anthropogenic and natural erosion features at a landscape scale. The method is accurate, robust and cost-effective particularly given the remote locations and large extent of these landscapes, facilitating effective and targeted restoration planning, management and monitoring.Dartmoor National Park AuthorityDartmoor Peatland PartnershipDuchy of CornwallEnvironment AgencyForestry CommissionMinistry of DefenceNatural EnglandSouth West partnership for Environmental and Economic Prosperity (SWEEP)South West WaterNatural Environment Research Council (NERC

    Spatio-temporal appraisal of water-borne erosion using optical remote sensing and GIS in the Umzintlava catchement (T32E), Eastern Cape, South Africa.

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    Globally, soil erosion by water is often reported as the worst form of land degradation owing to its adverse effects, cutting across the ecological and socio-economic spectrum. In general, soil erosion negatively affects the soil fertility, effectively rendering the soil unproductive. This poses a serious threat to food security especially in the developing world including South Africa where about 6 million households derive their income from agriculture, and yet more than 70% of the country’s land is subject to erosion of varying intensities. The Eastern Cape in particular is often considered the most hard-hit province in South Africa due to meteorological and geomorphological factors. It is on this premise the present study is aimed at assessing the spatial and temporal patterns of water-borne erosion in the Umzintlava Catchment, Eastern Cape, using the Revised Universal Soil Loss Equation (RUSLE) model together with geospatial technologies, namely Geographic Information System (GIS) and remote sensing. Specific objectives were to: (1) review recent developments on the use of GIS and remote sensing technologies in assessing and deriving soil erosion factors as represented by RUSLE parameters, (2) assess soil erosion vulnerability of the Umzintlava Catchment using geospatial driven RUSLE model, and (3) assess the impact of landuse/landcover (LULC) change dynamics on soil erosion in the study area during the period 1989-2017. To gain an understanding of recent developments including related successes and challenges on the use of geospatial technologies in deriving individual RUSLE parameters, extensive literature survey was conducted. An integrative methodology, spatially combining the RUSLE model with Systeme Pour l’Obsevation de la Terre (SPOT7) imagery within a digital GIS environment was used to generate relevant information on erosion vulnerability of the Umzintlava Catchment. The results indicated that the catchment suffered from unprecedented rates of soil loss during the study period recording the mean annual soil loss as high as 11 752 t ha−1yr−1. Topography as represented by the LS-factor was the most sensitive parameter to soil loss occurring in hillslopes, whereas in gully-dominated areas, soil type (K-factor) was the overriding factor. In an attempt to understand the impact of LULC change dynamics on soil erosion in the Umzintlava Catchment from the period 1989-2017 (28 years), multi-temporal Landsat data together with RUSLE was used. A post-classification change detection comparison showed that water bodies, agriculture, and grassland decreased by 0.038%, 1.796%, and 13.417%, respectively, whereas areas covered by forest, badlands, and bare soil and built-up area increased by 3.733%, 1.778%, and 9.741% respectively, during the study period. The mean annual soil loss declined from 1027.36 t ha−1yr−1 in 1989 to 138.71 t ha−1yr−1 in 2017. Though soil loss decreased during the observed period, there were however apparent indications of consistent increase in soil loss intensity (risk), most notably, in the elevated parts of the catchment. The proportion of the catchment area with high (25 – 60 t ha−1yr−1) to extremely high (>150 t ha−1yr−1) soil loss risk increased from 0.006% in 1989 to 0.362% in 2017. Further analysis of soil loss results by different LULC classes revealed that some LULC classes, i.e. bare soil and built-up area, agriculture, grassland, and forest, experienced increased soil loss rates during the 28 years study period. Overall, the study concluded that the methodology integrating the RUSLE model with GIS and remote sensing is not only accurate and time-efficient in identifying erosion prone areas in both spatial and temporal terms, but is also a cost-effective alternative to traditional field-based methods. Although successful, few issues were encountered in this study. The estimated soil loss rates in Chapter 3 are above tolerable limits, whereas in Chapter 4, soil loss rates are within tolerable limits. The discrepancy in these results could be explained by the differences in the spatial resolution of SPOT (5m * 5m) and Landsat (30m * 30m) images used in chapters 3 and 4, respectively. Further research should therefore investigate the impact of spatial resolution on RUSLE-estimated soil loss in which case optical sensors including Landsat, Sentinel, and SPOT images may be compared

    Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface cover

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    The amount and intensity of runoff on catchment scale are strongly determined by the presence of impervious land-cover types, which are the predominant cover types in urbanized areas. This paper examines the impact of different methods for estimating impervious surface cover on the prediction of peak discharges, as determined by a fully distributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe River catchment in the southeastern part of Brussels. The study shows that detailed information on the spatial distribution of impervious surfaces, as obtained from remotely sensed data, produces substantially different estimates of peak discharges than traditional approaches based on expert judgment of average imperviousness for different types of urban land use. The study also demonstrates that sub-pixel estimation of imperviousness may be a useful alternative for more expensive high-resolution mapping for rainfall-runoff modelling at catchment scale

    Use of SAR data for hydro-morphological characterization in sub-Saharan Africa: a case study

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    In this paper we present the rationale and the preliminary results of a research project devoted to the appropriate and innovative use of remotely sensed data for water management in semi-arid regions. The study area is the district of Yatenga, northern Burkina Faso in the sub- Saharan belt of West Africa, where extreme climate conditions cause several problems: drought, floods, soil erosion. The data comes from the Italian Space Agency (ASI) Cosmo-Skymed program, which provides high resolution (1 meter) Synthetic Aperture Radar (SAR) images. Crucial peculiarity of the project is the use of open source software for data processing and hydrological modeling. Two different hydrological models have been selected. The Soil and Water Assessment Tool (SWAT) to be employed for the design of appropriate water management plans and soil erosion mitigation measures. The Width Function Instantaneous Unit Hydrograph (WFIUHD) model can to employed for the prevision of flood events and therefore for the planning of risk mitigation. The paper shows the preliminary results of the project obtained by the processing of the first available high resolution SAR data. In particular, the first step is the realization of a Digital Elevation Model (DEM). GIS tools have been set up for the DEMprocessing in order to derive the needed hydro-morphological basin attributes to support the geo-morphological rainfall- runoff (WFIUHD) modelin

    Integrated Land Use Change Analysis For Soil Ersion Study In Ulu Kinta Catchment [S623. B354 2006 f rb].

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    Ulu Kinta catchment has experienced rapid changes in land use and land cover from 1991 to 2004. These changes have resulted in increased upland erosion and higher concentrations of suspended sediment within the catchment. The goal of this research was to investigate the application of integrated satellite remote sensing and Geographic Information Systems (GIS) techniques to assess land cover changes and the estimation of soil erosion in the water catchment. Kawasan tadahan Ulu Kinta telah mengalami perubahan yang ketara di dalam penggunaan tanah dan liputan tanah dari tahun 1991 hingga 2004. Perubahan ini telah meningkatkan hakisan tanah dan meninggikan kepekatan bahan asing yang terampai di dalam kawasan tadahan. Tujuan kajian ini adalah untuk mengkaji penggunaan bersama teknik penderiaan jauh satelit dan Sistem Maklumat Geografi (GIS) untuk menilai perubahan litupan bumi dan anggaran penghakisan tanah untuk kawasan tadahan air

    Assessment of the impacts of clear-cutting on soil loss by water erosion in Italian forests: First comprehensive monitoring and modelling approach

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    Abstract As a member of the European Union, Italy has committed to the maintenance and protection of its forests based on sustainable forest development and management practices. According to Eurostat, Italy has the seventh largest forest surface available for wood supply in the EU-28, which is equal to 8.086 million hectares. For 2012, the Italian National Institute of Statistics estimated the total roundwood production of Italy to be 7.7 million m3, from a harvested forest surface of 61,038 ha. Large parts of the country's forests, mainly located in vulnerable mountainous landscapes that are highly sensitive to environmental changes, are subject to anthropogenic disturbance driven by wood supply interests. Despite the extensive logging activities and the well-known impacts that such management practices have on the soil-related forest ecosystems, there is a lack of spatially and temporally explicit information about the removal of trees. Hence, this study aims to: i) assess the soil loss by water erosion in Italian forest areas, ii) map forest harvests and iii) evaluate the effects of logging activities in terms of soil loss by means of comprehensive remote sensing and GIS modelling techniques. The study area covers about 785.6 × 104 ha, which corresponds to the main forest units of the CORINE land cover 2006 database (i.e. broad-leaved forests, coniferous forests and mixed forests). Annual forest logging activities were mapped using Landsat imagery. Validation procedures were applied. A revised version of the Universal Soil Loss Equation (USLE) was used to predict the soil loss potential due to rill and inter-rill processes. To ensure a thorough modelling approach, the input parameters were calculated using the original methods reported in the USDA handbooks. The derived high-resolution data regarding forest cover change shows that 317,535 ha (4.04% of the total forest area in Italy) were harvested during the period under review. The predicted long-term annual average soil loss rate was 0.54 Mg ha− 1 yr− 1. The average rate of soil loss in forests that remained undisturbed during the modelled period is equal to 0.33 Mg ha− 1 yr− 1. Notably, about half of the soil loss (45.3%) was predicted for the logged areas, even though these cover only about 10.6% of the Italian forests. The identified erosion hotspots may represent a serious threat for the soil-related forest ecosystems, and are in contrast to the EC Thematic Strategy for Soil Protection and Water Framework Directive

    Remote Sensing Of Rice-Based Irrigated Agriculture: A Review

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    The ‘Green Revolution’ in rice farming of the late 1960’s denotes the beginning of the extensive breeding programs that have led to the many improved rice varieties that are now planted on more than 60% of the world’s riceland (Khush, 1987). This revolution led to increases in yield potential of 2 to 3 times that of traditional varieties (Khush, 1987). Similar trends have also been seen in the Irrigation Areas and Districts of southern New South Wales (NSW) as the local breeding program has produced many improved varieties of rice adapted to local growing conditions since the 1960’s (Brennan et al., 1994). Increases in area of rice planted, rice quality, and paddy yield resulted (Brennan et al., 1994). Increased rice area, however, has led to the development of high water tables and risk of large tracts of land becoming salt-affected in southern NSW (Humphreys et al., 1994b). These concerns have led to various environmental regulations on rice in the region, culminating in 1994 when restrictions on rice area, soil suitability, and water consumption were fully enacted (Humphreys et al., 1994b). Strict environmental restrictions in combination with large areas of land make the management of this region a difficult task. Land managers require, among other things, a way of regulating water use, assessing or predicting crop area and productivity, and making management decisions in support of environmentally and economically sustainable agriculture. In the search for more time and cost effective methods for attaining these goals, while monitoring complex management situations, many have turned to remote sensing and Geographic Information System (GIS) technologies for assistance. The spectral information and spatial density of remote sensing data lends itself well to the measurement of large areas. Since the launch of LANDSAT-1 in 1972, this technology has been used extensively in agricultural systems for crop identification and area estimation, crop yield estimation and prediction, and crop damage assessment. The incorporation of remote sensing and GIS can also help integrate management practices and develop effective management plans. However, in order to take advantage of these tools, users must have an understanding of both what remote sensing is and what sensors are now available, and how the technology is being used in applied agricultural research. Accordingly, a description of both follows: first a description of the technology, and then how it is currently being applied. The applications of remote sensing relevant to this discussion can be separated into crop type identification; crop area measurement; crop yield; crop damage; water use/ moisture availability (ma) mapping; and water use efficiency monitoring/mapping. This report focuses on satellite remote sensing for broad-scale rice-based irrigation agricultural applications. It also discusses related regional GIS analyses that may or may not include remote sensing data, and briefly addresses other sources of finer-scale remote sensing and geospatial data as they relate to agriculture. Since a complete review of the remote sensing research was not provided in the rice literature alone, some generic agricultural issues have been learned from applications not specifically dealing with rice. Remote sensing specialists may wish to skip to section 2

    Modelling in ungauged catchments using PyTOPKAPI : a case study of Mhlanga catchment.

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    Masters Degree. University of KwaZulu-Natal, Durban.Hydrological modeling of rainfall-runoff processes is a powerful tool used in various water resources applications, including the simulation of water yield from ungauged catchments. Many rivers in developing countries are poorly gauged or fully ungauged. This gives rise to a challenge in the calibration and validation of hydrological models. This study investigated the applicability of PyTOPKAPI, a physically based distributed hydrological model, in simulating runoff in ungauged catchments, using the Mhlanga River as a case study. This study is the first application of the PyTOPKAPI model to simulate daily runoff on an ungauged catchment in South Africa. The PyTOPKAPI model was parameterised using globally available digital elevation data (DEM), satellite-derived land cover, soil type data and processed hydro-meteorological data collected from various sources. Historical 30-year (1980-2009) quaternary monthly streamflow (from a well-tested and calibrated model) and daily meteorological variables (rainfall, temperature, humidity and so on) were obtained. The rainfall data were subjected to double mass curve test to check for consistency. The monthly streamflow was transposed to the catchment and disaggregated to daily streamflow time step. The PyTOPKAPI model was calibrated using an average runoff ratio as an alternative to matching streamflow data that is usually used for model calibrations. The simulated results were thereafter compared with the disaggregated monthly quaternary data. The model results show good overall performance when compared with the average runoff ratio, monthly disaggregated streamflow and the expected mean annual runoff in the catchment. In general, PyTOPKAPI can be used to predict runoff response in ungauged catchments, and thus may be adopted for water resources management applications
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