Evaluation of erosion risk map based on hierarchical decision tree method, a case study: Semnan drainage basin

Introduction: Today, Soil erosion has become one of the biggest problems in the country, especially in arid and semi-arid including Semnan. Effective and long term water and soil conservation programs require the concentration of resources on limited areas. For that purpose, regional-scale assessments of erosion risk are required. There are various methods to studying, evaluating, calculate and prevention with soil erosion. In addition, a number of parameters such as lithology, slope, aspect, land cover, elevation, and distance to stream, drainage density, vegetable cover, land use, river banks, and human activities are recommended to analyze the mechanism of soil erosion. So a rapid and cost effective methodological erosion assessment for these regions is required to describe and monitor the processes that control erosion. This study uses one of the remote sensing analyses to describe the contribution of several factors that control erosion in Semnan drainage basin where erosion is the major environmental problem. Remote sensing monitoring has been carried out by using aero photos, or multispectral images, DTM (Digital Terrain Model) or ALS (Airborne LASER Scanning) data. Semnan basin, study area, is located in north of Kavir plain and south of Alborz mountain range. Methodology: This study was conducted to evaluate the potential of analyzing regional erosion risk Topography, land use; vegetation density, soil properties and climatic proxies are used to determine erosion risk and to provide basic maps of water and soil conservation practices. A hierarchical decision tree is used to sum and combine the weight of parameters controlling the erosion. The assigned weights of each spatial unit express the susceptibility to erosion. The most important attributes in the definition of erosion landforms like gullies were selected using decision tree induction algorithms, being these attributes spectral, altimetry and texture. Classifications hierarchical and by decision trees were carried out. Using decision tree the classification is performed only by a factor of scale, not allowing the identification of all the constituent features of the erosion landforms system. One of the advantages of this method is that it can be used if there are insufficient experimental data. The lack of experimental data can be compensated for through the use of expert evaluations. Results and discussion: Three different combinations of the three dominant controlling factors are yielded in this study. In order to optimize the qualitative erosion risk assessment, each combination is discussed and evaluated depending on the contribution of parameters involved in the erosion process. As different erosion landforms erosion is similar when presents the same evolution stage and soil type, it is not possible to select attributes to classify all erosion landform systems, being necessary to investigate attributes for each erosion landform erosion, based on available data and existing land use classes in the area. The erosion landforms are the biggest erosive processes and, consequently, responsible for ambient, social and financial damages. Corrective and preventive measures need mapping and monitoring, which can be made by local measurements or by remote sensing. In relation to the remote sensing, the erosion landform erosion presents spectral heterogeneity (soil, vegetation, shade and water mix), spatial heterogeneity (existence of features as head, canals and digits with irregular forms and variable dimensions) and altimetry variation (with high declivity on the edges). Due to spectral heterogeneity, it is not enough use only spectral data, being necessary auxiliary data, as altimetry and texture data. This clearly shows that the study area is generally exposed to a high hazard of soil erosion. Nevertheless, there is a probability that the rate of erosion will increase in the future, as hazard is the probability of occurrence of a potential damaging phenomenon, within a period of time and a given area. As known, there is always an interest to depend on latest developments when making subjective judgments. In spite of the results obtained in this study, the development of a susceptibility map is usually determined by the needs and available resources, and AHP method can be equally important for all sorts of susceptibility zoning practices. Conclusion: The purpose of this study was to assess the soil erosion hazard in the Semnan province for planning appropriate conservation measures. The integrated GIS-AHP model was used to define spatial distribution of soil erosion hazard. In this area, erosion risk mainly was related with vegetation and also, it anticipated that the southern and south-eastern region due to the poverty of vegetation associated with increased levels of erosion. In each of the three mapped models, the area of the class with high erosion sensitivity was more than 75% and for observational data, the area in all three maps is above 71%. Also, the results of the assessment show that in all three maps there are over 99% correlation between the data obtained from the modeling and the test data. The erosion landforms present spectral and spatial heterogeneity and altimetry variation. This research demonstrates that the model developed was an effective tool for fast assessment of soil erosion hazard by the integration of remote sensed data, AHP, and GIS techniques. Nevertheless, the results obtained in this study are valid only for generalized planning and assessment purposes. They may be less useful at the site-specific scale, where local geological and geographic heterogeneities may prevail. Finally, any proposed decision-making tool in erosion control studies should also include local experimentation data to better simulate the erosion hazard, resulting thereby in the most appropriate and efficient choice of soil conservation works

A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water

The Universal Soil Loss Equation (USLE) model is the most frequently used model for soil erosion risk estimation. Among the six input layers, the combined slope length and slope angle (LS-factor) has the greatest influence on soil loss at the European scale. The S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length. The combined LS-factor describes the effect of topography on soil erosion. The European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil erosion assessment to achieve a better understanding of the spatial and temporal patterns of soil erosion in Europe. The LS-calculation was performed using the original equation proposed by Desmet and Govers (1996) and implemented using the System for Automated Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and contributes to a precise estimation of flow accumulation. The LS-factor dataset was calculated using a high-resolution (25 m) Digital Elevation Model (DEM) for the whole European Union, resulting in an improved delineation of areas at risk of soil erosion as compared to lower-resolution datasets. This combined approach of using GIS software tools with high-resolution DEMs has been successfully applied in regional assessments in the past, and is now being applied for first time at the European scale.JRC.H.5-Land Resources Managemen

Modelling Soil Degradation in Libya

Soil degradation is considered one of the most important factors limiting agricultural development in Libya, however little effort has been taken to identify the distribution of soil degradation occurrence and type for the country. While the soil degradation for the primary agriculture regions (PAR) has been previously determined as thirty-three percent (33%), the degradation for the rest of the country was still unknown. For this reason, polygons representing soil and climate characteristics, landscape feature and soil degradation from the PAR were converted to raster using ArcGIS (at a resolution of 1000 m2) resulting in 850 points which were then exported as a table for modelling purposes. The data set was subjected to logistic regression to model the binomial outcome of soil degradation occurrence (occurrence, no occurrence). A multinomial logistic regression was used to relate predictor variables to the type of soil degradation since there was more than two outcome options (salinization, water erosion, and wind erosion). Finally, the prediction models were used to determine the remainder of the country’s degradation occurrence and type. Results indicated that slope, texture and wind speed are the most important variables for soil degradation occurrence and type in PAR. When these models are applied to the reminder of the country, they show that salinization was the primary type of soil degradation (30 %), with water erosion and wind erosion causing 10 % and 15 % of soil degradation, respectively. The intention is for these models to assist stakeholders in identifying areas where agriculture is most likely to be successful, while also applicable to countries with similar climate and soils in North Africa. Keywords: Agriculture, GIS, Libya, Logistic regression, Soil degradation

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

APLICAÇÃO DO MÉTODO DE CLASSIFICAÇÃO CONTÍNUA FUZZY PARA O MAPEAMENTO DA FRAGILIDADE DO TERRENO EM RELAÇÃO À OCORRÊNCIA DE RAVINAS NO PARQUE NACIONAL DA SERRA DA CANASTRA - APPLICATION OF CONTINUOUS FUZZY CLASSIFICATION METHOD FOR MAPPING TERRAIN FRAGILITY REGARDING THE OCCURRENCE OF GULLIES IN THE SERRA DA CANASTRA NATIONAL PARK

Este trabalho tem como objetivo realizar o mapeamento da fragilidade ambiental à ocorrência de ravinas no Parque Nacional da Serra da Canastra, por meio de um modelo espacial baseado em lógica fuzzy, desenvolvido para SIG, que combina mapas das seguintes variáveis geoambientais: índice de vegetação, densidade de lineamentos estruturais, densidade de vias de circulação e declividade do terreno. Este modelo utiliza parâmetros do teste estatístico Kolmogorov-Smirnov (KS), extraídos a partir da avaliação do grau de aderência entre a distribuição dos valores das variáveis ambientais e a distribuição espacial real das ravinas observadas no parque. Os pesos são utilizados em um processo de álgebra de mapas, baseado na soma ponderada das variáveis geoambientais convertidas em escala fuzzy. O mapa de fragilidade ambiental produzido é representado segundo o método de classificação contínua fuzzy, indicando o grau de afinidade de cada pixel à ocorrência de ravinas. Os resultados mostraram que a ordem de importância das variáveis na ocorrência de ravinas, nesta Unidade de Conservação, é a seguinte: (1) densidade de lineamentos estruturais; (2) índice de vegetação; (3) densidade de estradas; (4) declividades. As áreas de maior fragilidade concentram-se em bacias localizadas a noroeste e na porção central do Chapadão da Babilônia. Os graus menores de fragilidade são observados, sobretudo, no Chapadão da Canastra e em pontos a sudeste do Chapadão da Babilônia, em bacias que apresentam densidades de ravinas abaixo da média do parque.ABSTRACTThe aim of this work was to map environmental fragility of Serra da Canastra National Park, located in Southwest region of Minas Gerais state, Brazil, using ravine sites identified on high spatial resolution images, as terrain true controls points. The methodology used for mapping was based on fuzzy logic model and GIS techniques. The model combines four environmental variables maps, as follows: vegetation index, structural lineaments density, roads density and terrain slope. Kolmogorov-Smirnov (KS) statistical test was used to evaluate adherence between spatial distribution of environmental variables values and spatial distribution of ravines sites. Environmental fragility was mapped using linear membership fuzzy function, and evaluated upon third and fourth orders hydrographic basins. The results showed that ravine sites are mainly associated to the structural lineaments density, followed by vegetation index, road density and terrain slope. The most of higher environmental fragility hydrographic basins are located in the Chapadão da Babilônia landscape unit

CARACTERIZAÇÃO DA FRAGILIDADE AMBIENTAL DA BACIA HIDROGRÁFICA DO CÓRREGO COME ONÇA, ÁGUA CLARA/MS

O presente trabalho objetivou analisar a fragilidade ambiental da bacia hidrográfica do córrego Come Onça, situado no município de Água Clara- MS. Os procedimentos técnicos científicos aplicados consistiram nas propostas que utilizam a teoria da fragilidade dos ambientes naturais. Os resultados mostraram o predomínio de níveis médios de fragilidade potencial (58,06%) e ambiental (77,46%) para área da bacia, característica explicada pela presença predominante de solos arenosos e áreas de pastagem que recentemente vem sendo substituída pela silvicultura. Conclui-se que a recente instalação de um complexo industrial de eucalipto-celulose-papel vem acelerando esse processo, exigindo estudos ambientais na região na busca de um planejamento, uso e manejo adequado do solo com utilização de práticas agrícolas conservacionistas, visando o desenvolvimento sustentável

CARACTERIZAÇÃO DA FRAGILIDADE AMBIENTAL DA BACIA HIDROGRÁFICA DO CÓRREGO COME ONÇA, ÁGUA CLARA/MS

O presente trabalho objetivou analisar a fragilidade ambiental da bacia hidrográfica do córrego Come Onça, situado no município de Água Clara- MS. Os procedimentos técnicos científicos aplicados consistiram nas propostas que utilizam a teoria da fragilidade dos ambientes naturais. Os resultados mostraram o predomínio de níveis médios de fragilidade potencial (58,06%) e ambiental (77,46%) para área da bacia, característica explicada pela presença predominante de solos arenosos e áreas de pastagem que recentemente vem sendo substituída pela silvicultura. Conclui-se que a recente instalação de um complexo industrial de eucalipto-celulose-papel vem acelerando esse processo, exigindo estudos ambientais na região na busca de um planejamento, uso e manejo adequado do solo com utilização de práticas agrícolas conservacionistas, visando o desenvolvimento sustentável

Water Resource Variability and Climate Change

Climate change affects global and regional water cycling, as well as surficial and subsurface water availability. These changes have increased the vulnerabilities of ecosystems and of human society. Understanding how climate change has affected water resource variability in the past and how climate change is leading to rapid changes in contemporary systems is of critical importance for sustainable development in different parts of the world. This Special Issue focuses on “Water Resource Variability and Climate Change” and aims to present a collection of articles addressing various aspects of water resource variability as well as how such variabilities are affected by changing climates. Potential topics include the reconstruction of historic moisture fluctuations, based on various proxies (such as tree rings, sediment cores, and landform features), the empirical monitoring of water variability based on field survey and remote sensing techniques, and the projection of future water cycling using numerical model simulations

Sustainable intensification of arable agriculture:The role of Earth Observation in quantifying the agricultural landscape

By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an important first step in the creation of an operational EO-based monitoring system to assess sustainable intensification, ensuring we are able to meet future food demands in an environmentally sustainable way