Spatial modelling to establish priorities for erosion control in commercial forestry plantations.

Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2002Commercial forestry is recognized for both its economic contribution as well as its environmental impact. Of particular concern, is the soil erosion and sedimentation of watercourses associated with forestry plantations. Environmental laws regulate many of the activities of the forestry sector. It is critical that the forestry sector ensure that its operations are compliant with the legal requirements that govern its use of natural resources. In pursuing legal compliance it is necessary to ensure that erosion control strategies are developed so as to ensure the positive effects of any interventions are optimised. The identification of areas that are particularly at risk to erosion or contribute to sediment delivery is an essential component in prioritising areas for management interventions. Establishing the erosion potential for commercial forestry areas is readily accomplished through the application of existing models. Process based erosion models generally have greater data requirements than the empirically derived USLE-based models. Given the paucity of data available, the latter approach was adopted. Two methods of topographic sub-factor derivation were investigated, those associated with the RUSLE (Renard, Foster, Weesies & McCool1991) and the Unit Stream Power method presented by Moore and Burch (1986). Since no existing methods identifying delivery risk areas existed, a method was developed based on principles and factors identified in the literature. Additionally, methods for identifying topographic assets, in terms of sediment attenuation, were developed. From these models three indices were derived; sediment supply, delivery risk and sediment attenuation. Thereafter, the mean Sediment Supply Index was divided by stream length for small catchments defined within the landscape to derive an index of sediment loading to streams. This index is used to identify priorities for management intervention across the landscape. The mean slope and sediment supply is used to develop buffer width recommendations for the streams draining the catchments, using a method developed by Karssies and Prosser (2001). Using the three indices in conjunction it is possible to make on-site and off-site erosion control recommendations as well as identify and exploit any natural features that can be utilized in erosion control

Desertification indicators for the European Mediterranean region: state of the art and possible methodological approaches [= Indicatori di desertificazione per il Mediterraneo europeo: stato dell'arte e proposte di metodo]

The Italian Environment Protection Agency (ANPA), and the Desertification Research Centre at the University of Sassary have worked jointly to provide decision-makers with an in-depth analysis of the state of the art and methodologies applicable to the evaluation of the desertification phenomenon. ANPA has promoted this important research activity, within the wider and more dynamic framework of actions it conducts in the Italian National Committee, providing its support to the definition and start up of the National Plan to Combat Desertification and Drought. The complexity of the phenomena and their causes leads to the individuation of a plurality of “actors” who might take the responsibility to carry out actions aimed at combating Desertification and Drought. Indicators represent a crucial link in the chain that, from knowledge, leads to taking decisions and promoting responsible behaviours: starting from an evaluation of the various, physical, biologic, socio-economic processes that contribute to land degradation and desertification, the goal is to individuate indicators that might prove useful in territorial planning and public information activities, and that might be a suitable answer to the request for direct knowledge of the status and evolution of the phenomenon, as well as the opportunity to take actions aimed at mitigating and, above all, preventing the occurrence of the phenomenon

A Platform for Proactive, Risk-Based Slope Asset Management, Phase II

INE/AUTC 15.0

Integrating Sentinel-2 Data and PAPCAR Model to Map Water Erosion: Case of Beni Boufrah Watershed

Water erosion causes significant economic losses linked mainly to the silting up of dams and losses in soil productivity, these consequences will increase if soil and water conservation actions associated with development actions are not undertaken. The present work aims to evaluate the water erosion in the basin of the Beni Boufrah located in the Northern part of Morocco. The hierarchy of this basin in plot according to the degrees and the tendencies of the erosion was made using the qualitative PAP/CAR approach (Programme d’Actions Prioritaires/Centre d’Activités Regionales) which is based on the integration of the factors influencing the water erosion, such as the slope, lithology and/or pedology, vegetation cover and land use. This work was conducted in three stages, the first one being predictive based on the analysis of the natural factors influencing water erosion and the processing of databases of developed maps. The second so-called descriptive stage is based on the mapping of different forms and processes of soil loss that occur in the study area. The last step, it allows the integration and the combination of the results of the two previous steps. Its purpose is to provide a precise cartographic product that reflects the reality of the state of soil degradation and the future evolution of erosion. The consolidated erosion map shows that more than half of the basin area (53%) is affected by medium-level erosion, 13% is affected by high erosion level, and 15 % is affected by low-level water erosion. Low-intensity erosion occurs along the river in areas where the slope and lithology favour runoff. The trend map is the final result of the integration phase, it describes erosion trends in the different parts of the basin and is, therefore, a tool to guide decisions on land use planning and tillage methods to limit the risk of water erosion in the basin. Keywords: Oued Beni Boufrah, water erosion, PAP/CAR, erosive states, erosion trend

Exploration of hydro-geomorphological indices for coastal floodplain characterization in Rivers State, Nigeria

Flood is a reoccurring natural hazard in many parts of Nigeria, and is likely to increase in severity and frequency. Characterization of recently flooded areas was carried out using hydro-morphological indices to identify flood prone areas. In flood risk quantification and identification, hydrodynamic models require vast amounts of data, while contour delineation fails to account for the upstream contribution and accumulation at downstream locations. Data on recently flooded areas and elevation data were collated. Hydro-geomorphometric indices were computed and compared using the Mann-Whitney U test. Across the indices, the terrain roughness indices – vertical roughness measure (VRM) and topographic roughness index (TRI) were found to be significant but weakly correlated (r = 0.455, P<0.05). There was a significantly positive but moderate correlation between topographic wetness index (TWI) and VRM (r=-0.673) and TWI vs TRI (r=0.572). Topographic position index (TPI) displayed a weak but significant relation to VRM, TWI and TRI. Of these four indices, TWI and TRI have standardized test statistics of -6.11 and 10.00 respectively and a significant test value < 0.05. Results show that flooded and non-flooded areas can be distinguished for the study area using these indices. It is recommended that hydro-geomorphometric indices should be used, adding another layer of confidence in the identification of flood prone areas for disaster risk management in data poor environments

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

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

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