Assessment of soil erosion based on clustered geoinformatics approaches: a case study of Tyume River Catchment, Eastern Cape, South Africa

This research centres on the holistic assessments of spatial and temporal dimensions of soil erosion zones based on the parameters of geomorphometry, hydro-statistics, and land use/cover dynamics. The study used a case study approach based on a clustered framework model of soil erosion parameters in the Tyume River basin in Eastern Cape, South Africa. The methods adopted for the investigation are, namely; non-parametric time-series assessment of streamflow dataset, semidecadal assessment of land use/cover (LU/C) dynamics, geospatial analysis of geomorphometric variables, vulnerability analysis of soil erosion zones, regression analysis of determination coefficient, and Receiver Operating Characteristic Curve (ROC). The delineation of soil erosion zones was based on the integrated analysis of the parameters of geomorphometry, geology, hydrology, and land use/ cover. The result of the hydro-statistical analysis of the Tyume River reports a major decline in the inter-annual regime frequency of storm flow based on the Mann- Kendall (MK) test and Sen’s slope assessment in 1992 (p-value = 0.031), 1997 (p-value = 0.045), 2003 (p-value = 0.021), 2008 (p-value = 0.003), and 2016 (p-value = 0.002). The MK test depicted a recurrence of peak streamflow acceleration in every three years based on low-flow and highflow transition. The sensitivity of LU/C to temporal dynamics of streamflow trends shown by the coefficient of correlation of trends of the LU/C water bodies with streamflow semi-decadal acceleration indicates a moderately relevant relationship, R = 0.76. The temporal analysis of LU/C and hydro-statistical analysis shows that the Tyume basin was highly vulnerable to soil erosion by water in 1999, 2009, and 2019. The vulnerability of the Tyume River basin in 2019 is exceptional and this is due to the conversion of forested area (woodland) into a built-up environment and farmland, with a high vulnerability in 2019 due to the slump in the rate of change of woodland and precipitation, and the increase in the rate of built-up and agricultural activities. The soil erosion vulnerability mapping divides the river basin into the critical high, high, moderate, low, nonvulnerable zones that cover 40 km2, 135 km2, 209 km2, and 186 km2 respectively. Regression analysis shows that the areas of soil erosion in the Tyume basin are moderately represented by the model (R2 = 56) while the model performance assessment based on success rate and prediction rate estimation from the area under the ROC curve shows that the model is good, Area Under Curve of the ROC = 0.899, and 0.897. The analysis suggests that soil erosion is driven by the impact of land use/land cover change, particularly in areas of high drainage density. Significantly, high vegetation density played a vital role in lowering high-flow on the hill-slope and low topographic wetness area as well as in areas with erodible geologic properties. The study, therefore, recommends the advocacy of crop rotation method of agricultural practice in the highly critical areas of soil erosion and recommends the development of riparian forests around the Tyume River. The study provides important information for environmental stakeholders on degradable areas which may require the urgent implementation of sustainable development measures.Thesis (MPhil) -- Faculty of Science and Agriculture, 202

Assessment of soil erosion based on clustered geoinformatics approaches: a case study of Tyume River Catchment, Eastern Cape, South Africa

This research centres on the holistic assessments of spatial and temporal dimensions of soil erosion zones based on the parameters of geomorphometry, hydro-statistics, and land use/cover dynamics. The study used a case study approach based on a clustered framework model of soil erosion parameters in the Tyume River basin in Eastern Cape, South Africa. The methods adopted for the investigation are, namely; non-parametric time-series assessment of streamflow dataset, semidecadal assessment of land use/cover (LU/C) dynamics, geospatial analysis of geomorphometric variables, vulnerability analysis of soil erosion zones, regression analysis of determination coefficient, and Receiver Operating Characteristic Curve (ROC). The delineation of soil erosion zones was based on the integrated analysis of the parameters of geomorphometry, geology, hydrology, and land use/ cover. The result of the hydro-statistical analysis of the Tyume River reports a major decline in the inter-annual regime frequency of storm flow based on the Mann- Kendall (MK) test and Sen’s slope assessment in 1992 (p-value = 0.031), 1997 (p-value = 0.045), 2003 (p-value = 0.021), 2008 (p-value = 0.003), and 2016 (p-value = 0.002). The MK test depicted a recurrence of peak streamflow acceleration in every three years based on low-flow and highflow transition. The sensitivity of LU/C to temporal dynamics of streamflow trends shown by the coefficient of correlation of trends of the LU/C water bodies with streamflow semi-decadal acceleration indicates a moderately relevant relationship, R = 0.76. The temporal analysis of LU/C and hydro-statistical analysis shows that the Tyume basin was highly vulnerable to soil erosion by water in 1999, 2009, and 2019. The vulnerability of the Tyume River basin in 2019 is exceptional and this is due to the conversion of forested area (woodland) into a built-up environment and farmland, with a high vulnerability in 2019 due to the slump in the rate of change of woodland and precipitation, and the increase in the rate of built-up and agricultural activities. The soil erosion vulnerability mapping divides the river basin into the critical high, high, moderate, low, nonvulnerable zones that cover 40 km2, 135 km2, 209 km2, and 186 km2 respectively. Regression analysis shows that the areas of soil erosion in the Tyume basin are moderately represented by the model (R2 = 56) while the model performance assessment based on success rate and prediction rate estimation from the area under the ROC curve shows that the model is good, Area Under Curve of the ROC = 0.899, and 0.897. The analysis suggests that soil erosion is driven by the impact of land use/land cover change, particularly in areas of high drainage density. Significantly, high vegetation density played a vital role in lowering high-flow on the hill-slope and low topographic wetness area as well as in areas with erodible geologic properties. The study, therefore, recommends the advocacy of crop rotation method of agricultural practice in the highly critical areas of soil erosion and recommends the development of riparian forests around the Tyume River. The study provides important information for environmental stakeholders on degradable areas which may require the urgent implementation of sustainable development measures.Thesis (MPhil) -- Faculty of Science and Agriculture, 202

Efficacy of machine learning, earth observation and geomorphometry for mapping salt-affected soils in irrigation fields

Thesis (MSc)--Stellenbosch University, 2018.ENGLISH ABSTRACT: There is a need to monitor salt accumulation throughout agricultural irrigation schemes as it can have a major negative impact on crop yields and subsequently result in a lower food production. Salt accumulation can result from natural processes, human interference or prolonged waterlogging. Most irrigation schemes are large and therefore difficult to monitor via conventional methods (e.g. regular field visits). More cost-effective, less time-consuming approaches in identifying salt-affected and salt-prone areas in large irrigation schemes are therefore needed. Remote sensing has been proposed as an alternative approach due to its ability to cover a large region on a timely basis. The approach is also more cost-effective because less field visits are required. A literature review on salt accumulation and remote sensing identified several direct and indirect methods for identifying salt-affected or salt-prone areas. Direct methods focus on the delineation of salt crusts visible on the bare soil in multispectral satellite imagery, whereas indirect methods, which include vegetation stress monitoring and geomorphometry (terrain analysis), attempt to take subsurface conditions into account. A disadvantage of the direct approach is that it does not take subsurface conditions into account, while vegetation stress monitoring (an indirect method) can produce inaccurate results because the vegetation stress can be a result of other factors (e.g. poor farming practices). Geomorphometry offers an alternative (modelling) approach that can either replace or augment direct and other indirect methods. Two experiments were carried out in this study, both of which focussed on machine learning (ML) algorithms (namely k-nearest neighbour (kNN), support vector machine (SVM), decision tree (DT) and random forest (RF)) and statistical analyses (regression or geostatistics) to identify salt-affected soils. The first experiment made use of very high resolution WorldView-2 (WV2) imagery. A number of texture measures and salinity indices were derived from the WV2 bands and considered as predictor variables. In addition to the ML and statistical analyses, a classification and regression tree (CART) model and Jeffries-Matusita (JM) distance thresholds were also produced from the predictors. The CART model was the most accurate in differentiating salt-affected and unaffected soils, but the accuracy of kNN and RF classifications were only marginally lower. The normalized difference salinity index showed the most promise among the predictors as it featured in the best JM, regression and CART models. The second experiment applied geomorphometry approaches to two South African irrigation schemes. Elevation sources include the Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) and a digital surface model (DSM) produced from stereoscopic aerial photography. A number of morphological (e.g. slope gradient) and hydrological (e.g. flow direction) terrain parameters were derived from the SRTM DEM and the DSM and used as predictors. In addition to the algorithms used for the first experiment, the geostatistical method Kriging with external drift (KED) was also evaluated in this experiment. The source of elevation had an insignificant impact on the accuracies, although the DSM did show promise when combined with ML. KED outperformed regression modelling and ML in most cases, but ML produced similar results for one of the study areas. The experiments showed that direct and geomorphometry approaches hold much potential for mapping salt-affected soil. ML also proved to be a viable option for identifying salt-affected or salt-prone soil. It is recommended that a combination of direct and indirect (e.g. vegetation stress monitoring) approaches are considered in future research. Making use of alternative data sources such as hyperspectral imagery or higher spatial resolution DEMs may also prove useful. Clearly, more research is needed before such approaches can be operationalized for detecting, monitoring and mapping salt accumulation in irrigated areas.AFRIKAANSE OPSOMMING: Daar is 'n behoefte om soutophoping deur middel van landboubesproeiingskemas te monitor aangesien dit 'n beduidende negatiewe uitwerking op oesopbrengste kan hê en gevolglik tot laer voedselproduksie kan lei. Soutophoping kan voortspruit uit natuurlike prosesse, menslike inmenging of langdurige deurdrenking. Die meeste besproeiingskemas is groot en daarom moeilik om te monitor via konvensionele metodes (bv. gereelde veldbesoeke). Meer koste-effektiewe, minder tydrowende benaderings is dus nodig om soutgeaffekteerde areas en areas wat geneig is tot soutophoping in groot besproeiingskemas te identifiseer. Afstandswaarneming is voorgestel as 'n alternatiewe benadering weens sy vermoë om 'n groot streek op 'n tydige basis te dek. Die benadering is ook meer koste-effektief omdat minder veldbesoeke vereis word. 'n Literatuuroorsig oor soutophoping en afstandswaarneming het verskeie direkte en indirekte metodes geïdentifiseer om soutgeaffekteerde areas of areas geneig tot soutophoping te identifiseer. Direkte metodes fokus op die afbakening van soutkorste wat in multispektrale satellietbeelde op die kaal grond sigbaar is. Indirekte metodes, insluitende plantstresmonitering en geomorfometrie (terreinanalise), aan die ander kant, poog om die ondergrondse toestande in ag te neem. 'n Nadeel van die direkte benadering is dat dit nie ondergrondse toestande in ag neem nie, terwyl plantstresmonitering ('n indirekte metode) onakkurate resultate kan veroorsaak, aangesien die plantstres die gevolg kan wees van ander faktore (bv. swak boerderypraktyke). Geomorfometrie bied 'n alternatiewe (modellering) benadering wat direkte of ander indirekte metodes kan vervang of uitbrei. In hierdie studie is twee eksperimente uitgevoer. Albei het gefokus op masjienleer (ML) algoritmes, naamlik k-nearest neighour (kNN), ondersteunende vektormasjien, besluitboom en ewekansige woud (EW), en statistiese ontledings (regressie of geostatistiek) om soutgeaffekteerde gronde te identifiseer. Die eerste eksperiment het gebruik gemaak van baie hoë resolusie WorldView-2 (WV2) beelde. 'n Aantal tekstuurmaatreëls en soutindekse is afgelei van die WV2-bande en is beskou as voorspeller-veranderlikes. Benewens die ML en statistiese ontledings, is 'n klassifikasie- en regressieboom (KARB) model en Jeffries-Matusita (JM) afstandsdrempels ook van die voorspellers vervaardig. Die KARB-model het die mees akkuraatste differensiasie tussen sout-geaffekteerde en ongeaffekteerde grond gemaak, maar die akkuraatheid van kNN- en EW-klassifikasies was slegs marginaal laer. Van al die voorspellers het die genormaliseerde-verskil-saliniteit-indeks die meeste belofte getoon aangesien dit in die beste JM-, regressie- en KARB-modelle presteer het. Stellenbosch University https://scholar.sun.ac.za vi Die tweede eksperiment het geomorfometriese benaderings toegepas op twee Suid-Afrikaanse besproeiingskemas. Elevasiebronne sluit in die Shuttle Radar Topographic Mission (SRTM) digitale elevasie-model (DEM) en 'n digitale oppervlakmodel (DOM) wat uit stereoskopiese lugfotografie vervaardig word. 'n Aantal morfologiese (bv. hellingsgradiënt) en hidrologiese (bv. vloeirigting) terreinparameters is afgelei van die SRTM DEM en die DOM en is gebruik as voorspellers. Benewens die algoritmes wat vir die eerste eksperiment gebruik is, is die geostatistiese metode Kriging met eksterne dryf (KED) ook in hierdie eksperiment geëvalueer. Die bron van elevasie het 'n onbeduidende impak op die akkuraatheid gehad, hoewel die DOM belofte getoon het wanneer dit met ML gekombineer is. KED het in meeste gevalle beter presteer as regressie modellering en ML, maar ML het soortgelyke resultate vir een van die studiegebiede opgelewer. Die eksperimente het getoon dat direkte en geomorfometriese benaderings baie potensiaal het vir die kartering van soutgeaffekteerde grond. ML het ook bewys dat dit 'n lewensvatbare opsie is om soutgeaffekteerde grond of grond wat geneig is tot soutophoping, te identifiseer. Daar word aanbeveel dat 'n kombinasie van direkte en indirekte (bv. plantegroei-stresmonitering) benaderings in toekomstige navorsing oorweeg word. Die gebruik van alternatiewe databronne soos hiperspektrale beelde of hoër ruimtelike resolusie-DOM's kan ook nuttig wees. Dit is duidelik dat meer navorsing nodig is voordat sulke benaderings geoperasionaliseer kan word vir die opsporing, monitering en kartering van soutophoping in besproeide gebiede

Modeling of Permafrost Distribution in the Semi-arid Chilean Andes

The distribution of mountain permafrost is generally modeled using a combination of statistical techniques and empirical variables. Such models, based on topographic, climatic and geomorphological predictors of permafrost, have been widely used to estimate the spatial distribution of mountain permafrost in North America and Europe. However at present, little knowledge about the distribution and characteristics of mountain permafrost is available for the Andes. In addition, the effects of climate change on slope stability and the hydrological system, and the pressure of mining activities have increased concerns about the knowledge of mountain permafrost in the Andes. In order to model permafrost distribution in the semi-arid Chilean Andes between ~29°S and 32°S, an inventory of rock glaciers is carried out to obtain a variable indicative of the presence and absence of permafrost conditions. Then a Linear Mixed-Effects Model (LMEM) is used to determine the spatial distribution of Mean Annual Air Temperature (MAATs), which is then used as one of the predictors of permafrost occurrence. Later, a Generalized Additive Model (GAM) with a logistic link function is used to predict permafrost occurrence in debris surfaces within the study area. Within the study area, 3575 rock glaciers were inventoried. Of these, 1075 were classified as active, 493 as inactive, 343 as intact and 1664 as relict forms, based on visual interpretation of satellite imagery. Many of the rock glaciers (~60-80%) are situated at positive MAAT, and the number of rock glaciers at negative MAAT greatly decreases from north to south. The results of spatial temperature distribution modeling indicated that the temperature changes by -0.71°C per each 100 m increase in altitude, and that there is a 4°C temperature difference between the northern and southern part of the study area. The altitudinal position of the 0°C MAAT isotherm is situated at ~4250 m a.s.l. in the northern (29°S) section and drops latitudinally to ~4000 m a.s.l. in the southern section (32°S) of the study area. For permafrost modeling purposes, 1911 rock glaciers (active, inactive and intact forms) were categorized into the class indicative of permafrost presence and 1664 (relict forms) as non-permafrost. The predictors MAAT and Potential Incoming Solar Radiation (PISR) and their nonlinear interaction were modeled by the GAM using LOESS smoothing function. A temperature offset term was applied to reduce the overestimation of permafrost occurrence in debris surface areas due to the use of rock glaciers as permafrost proxies. The dependency between the predictor variables shows that a high amount of PISR has a greater effect at positive MAAT levels than in negative ones. The GAM for permafrost distribution achieved an acceptable discrimination capability between permafrost classes (area under the ROC curve ~0.76). Considering a permafrost probability score (PPS) ≥ 0.5 and excluding steep bedrock and glacier surfaces, mountain permafrost can be potentially present in up to about 6.8% (2636 km2) of the study area, whereas with a PPS ≥ 0.75, the potential permafrost area decreases to 2.7% (1051 km2). Areas with the highest PPS are spatially concentrated in the north section of the study area where altitude rises considerably (the Huasco and Elqui watersheds), while permafrost is almost absent in the southern section where the topography is considerably lower (Limarí and Choapa watersheds). This research shows that the potential mountain permafrost distribution can be spatially modeled using topoclimatic information and rock glacier inventories. Furthermore, the results have provided the first local estimation of permafrost distribution in the semi-arid Chilean Andes. The results obtained can be used for local environmental planning and to aid future research in periglacial topics.4 month

Landslide susceptibility mapping using remote sensing data and geographic information system-based algorithms

Whether they occur due to natural triggers or human activities, landslides lead to loss of life and damages to properties which impact infrastructures, road networks and buildings. Landslide Susceptibility Map (LSM) provides the policy and decision makers with some valuable information. This study aims to detect landslide locations by using Sentinel-1 data, the only freely available online Radar imagery, and to map areas prone to landslide using a novel algorithm of AB-ADTree in Cameron Highlands, Pahang, Malaysia. A total of 152 landslide locations were detected by using integration of Interferometry Synthetic Aperture RADAR (InSAR) technique, Google Earth (GE) images and extensive field survey. However, 80% of the data were employed for training the machine learning algorithms and the remaining 20% for validation purposes. Seventeen triggering and conditioning factors, namely slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, Normalized Difference Vegetation Index (NDVI), rainfall, land cover, lithology, soil types, curvature, profile curvature, Stream Power Index (SPI) and Topographic Wetness Index (TWI), were extracted from satellite imageries, digital elevation model (DEM), geological and soil maps. These factors were utilized to generate landslide susceptibility maps using Logistic Regression (LR) model, Logistic Model Tree (LMT), Random Forest (RF), Alternating Decision Tree (ADTree), Adaptive Boosting (AdaBoost) and a novel hybrid model from ADTree and AdaBoost models, namely AB-ADTree model. The validation was based on area under the ROC curve (AUC) and statistical measurements of Positive Predictive Value (PPV), Negative Predictive Value (NPV), sensitivity, specificity, accuracy and Root Mean Square Error (RMSE). The results showed that AUC was 90%, 92%, 88%, 59%, 96% and 94% for LR, LMT, RF, ADTree, AdaBoost and AB-ADTree algorithms, respectively. Non-parametric evaluations of the Friedman and Wilcoxon were also applied to assess the models’ performance: the findings revealed that ADTree is inferior to the other models used in this study. Using a handheld Global Positioning System (GPS), field study and validation were performed for almost 20% (30 locations) of the detected landslide locations and the results revealed that the landslide locations were correctly detected. In conclusion, this study can be applicable for hazard mitigation purposes and regional planning

Developing land management units using Geospatial technologies: An agricultural application

This research develops a methodology for determining farm scale land managementunits (LMUs) using soil sampling data, high resolution digital multi-spectral imagery (DMSI) and a digital elevation model (DEM). The LMUs are zones within a paddock suitable for precision agriculture which are managed according to their productive capabilities. Soil sampling and analysis are crucial in depicting landscape characteristics, but costly. Data based on DMSI and DEM is available cheaply and at high resolution.The design and implementation of a two-stage methodology using a spatiallyweighted multivariate classification, for delineating LMUs is described. Utilising data on physical and chemical soil properties collected at 250 sampling locations within a 1780ha farm in Western Australia, the methodology initially classifies sampling points into LMUs based on a spatially weighted similarity matrix. The second stage delineates higher resolution LMU boundaries using DMSI and topographic variables derived from a DEM on a 10m grid across the study area. The method groups sample points and pixels with respect to their characteristics and their spatial relationships, thus forming contiguous, homogenous LMUs that can be adopted in precision agricultural applications. The methodology combines readily available and relatively cheap high resolution data sets with soil properties sampled at low resolution. This minimises cost while still forming LMUs at high resolution.The allocation of pixels to LMUs based on their DMSI and topographic variables has been verified. Yield differences between the LMUs have also been analysed. The results indicate the potential of the approach for precision agriculture and the importance of continued research in this area

Rock Glaciers and Water Supplies in the Himalaya

The high-mountain cryosphere forms water towers that are important for ecosystem services provision, supplying large populations living in mountains and the surrounding lowlands and producing potable water resources, and water for agriculture, industry and hydropower generation. However, continued glacier recession and mass loss is projected throughout the twenty-first century, and this raises major concerns regarding the future sustainability of cryospheric water resources. While glacier meltwater represents an essential drought-resilient freshwater resource in vulnerable drought-prone regions, little research has focused on the contribution made by runoff from rock glaciers. These are located widely throughout the high-mountain cryosphere and estimates of rock glacier water volume equivalent (WVEQ) vs glaciers suggests that the former may constitute increasingly important long-term water stores. Owing to the insulating effects of thick supraglacial debris cover, rock glaciers are climatically more resilient than glaciers; therefore, their relative importance versus glaciers may increase under future climate warming. Yet, while the hydrological role of debris-free glaciers and debris-covered glaciers has been the subject of much research, that of rock glaciers has received comparatively little attention. Given the need for strong climate adaptation in many of the world’s mountain regions, it is clear that a more comprehensive understanding of all components of the hydrological cycle in the high-mountain cryosphere is required. In this thesis, I develop the scientific understanding of rock glacier significance in deglacierizing mountains across a range of spatial scales (local, national, regional and global), with a specific focus on High Mountain Asia (HMA). The review chapter critically assesses the state of current scientific knowledge regarding the hydrological role of rock glaciers in high mountain systems and serves to form the context for the empirical chapters. The thesis has three key themes to which the empirical chapters are aligned: (1) the distribution and hydrological significance of rock glaciers at global scales, (2) the distribution and hydrological significance of rock glaciers at regional and national spatial scales (Himalaya and Nepalese Himalaya), and (3) advancing rock glacier evolutionary theory. (1) the thesis created a meta-analysis of existing systematic rock glacier inventories and compiled the first near-global rock glacier database (RGDB). The RGDB presented here includes >73,000 rock glaciers (intact = ~39,500, relict = ~33,500), which contain a WVEQ of 83.7 ± 16.7 Gt [~69–102 trillion litres]. Furthermore, the estimated ratio of rock glacier: glacier WVEQ is 1:456 globally. (2) the results of the meta-analysis described in (1) show that only ~9% of studies included in the RGDB cover the Hindu Kush Himalaya (HKH); therefore, I produced the first systematic rock glacier inventory for the (i) Nepalese Himalaya (national-scale), and (ii) Himalaya (regional-scale). In the former (i) I inventoried >6,000 rock glaciers, and these are estimated to contain a WVEQ of 20.90 ± 4.18 km³ (19.16 ± 3.83 Gt). For the Nepalese Himalaya estimated rock glacier: glacier WVEQ ratio is 1:9. In the latter (ii) ~25,000 rock glaciers have been inventoried. The total WVEQ is 51.80 ± 10.36 km³ (47.48 ± 9.50 Gt) with an estimated rock glacier: glacier WVEQ ratio of 1:24. The results of Theme 1 and 2 indicate that rock glaciers form considerable long-term water stores, which may become increasingly important as climatically-driven glacier recession and mass loss continues throughout the twenty-first century and beyond. (3) in order to understand debris-free glacier transition to rock glaciers I use in situ sedimentological data and kite aerial photography (KAP) data and develop a conceptual hypothesis to explain the key drivers of this process. The thesis suggests that sediment connectivity (i.e. the strength of the link between sediment sources and downslope landforms) is one such driver of these transition processes. As a consequence, I hypothesise that the presence of well-developed lateral moraines along glacier margins serves to reduce this connectivity, and thus reduce the likelihood of glacier-to-rock glacier transition occurring. The corelationships between rock glaciers and glacial, periglacial and paraglacial processes are also evaluated in the context of rock glacier origin and the changing influence these processes have upon rock glacier evolution through their lifecycle. Collectively, this research has shaped the understanding of the current and potential future role of rock glaciers in mountain hydrology and is the first to comprehend the distribution and hydrological significance of rock glaciers globally and in the Himalaya.Natural Environment Research Council (NERC

Socio-Environmental Vulnerability Assessment for Sustainable Management

This Special Issue explores the cross-disciplinary approaches, methodologies, and applications of socio-environmental vulnerability assessment that can be incorporated into sustainable management. The volume comprises 20 different points of view, which cover environmental protection and development, urban planning, geography, public policymaking, participation processes, and other cross-disciplinary fields. The articles collected in this volume come from all over the world and present the current state of the world’s environmental and social systems at a local, regional, and national level. New approaches and analytical tools for the assessment of environmental and social systems are studied. The practical implementation of sustainable development as well as progressive environmental and development policymaking are discussed. Finally, the authors deliberate about the perspectives of social–environmental systems in a rapidly changing world

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies