Integrated remote sensing and GIS techniques for landslide hazard zonation: a case study Wadi Watier area,South Sinai,Egypt

Natural hazard analysis involves mapping and identifying future hazardous zones through the analysis of the controls influencing hazard initiation and occurrence. One of such natural hazard is the landslide. Landslides are amongst the most costly and damaging natural hazards especially in mountain regions and are triggered mainly by seismic activity and/or rainfall. The aim of the present study is to integrate Remote Sensing (RS) and Geographic Information System (GIS) tools to create thematic layers for assessment and the estimation of landslide hazard zones in and surrounding the Wadi Watier area, South Sinai, Egypt. Various factors, variables and/or parameters can be derived from thematic layers such as lithology, structural lineaments, land-cover/land-use, terrain analysis and earthquakes. Intensity risk layers were created by using ERDAS Imagine 9.2, ARC GIS 9.2 and ARC INFO 7.2.1 software. Enhanced Thematic Mapper (ETM+7) Landsat satellite images were used to discriminate and extract structural lineaments, lithology and land-use/land-cover variables for the study area. The Digital Elevation Model (DEM) was generated from digitized topographic maps to produce terrain analysis maps such as; slope, aspect, height elevation, and 3D. The weighting score rating system based on the relative importance of various causal factors derived from RS data and other thematic layers was used for landslide hazard zonation (LHZ). Based on these data, a simple algorithm was created to classify the area into different risk zones. By overlaying all hazard layers a final landslide hazard map was produced. Using trial and error and statistical methods the weight score rating values have been readjusted. GIS integration with RS data can greatly facilitate classifying landslide hazard zones into low risk, moderate risk and high risk by using a slicing operation. Seismic data are integrated with final the LHZ to generate a LHZ scenario map for the future and to draw up an action plan of mitigation measures to avoid the damage, loss of life and socio-economic impacts in the study area.     

A new approach to the analysis of adjacencies: Potentials for landscape insights

Spatial adjacencies are a key-issue in environmental studies. Adjacency effects have been amply observed for biotic (plants and animals) and abiotic components of ecosystems. Particularly well-documented are the effects from human manufactures onto the contiguous vegetation mosaics of natural and semi-natural areas.In this work we first propose and reformulate association rules analysis (ARA), a relatively new data mining algorithm with very limited scientific applications so far, in the form of an in-depth investigation method of the spatial pattern of landcover and vegetation maps. We applied ARA to two very different study areas in Northern Italy, the first (Ceno valley) having a substantial human footprint and mapped at 1:25,000 scale, the second (Foses valley) being almost natural and mapped at 1:5000 scale.We were able to: (a) detect the entire network of spatial adjacencies among landcover types and (b) quantify the frequency and strength of detected adjacencies. Based on our spatial analysis, we also advanced hypotheses on both natural and man-driven vegetation dynamics. In addition, ARA allowed us to propose an index of naturality based on the discovered contiguities.Results show the skill of the proposed approach to characterize landcover spatial patterns for both mid-resolution and high-resolution maps. Furthermore the proposed approach bears a general interest, since it can be applied to the analysis of any landcover map.     

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.     

Species diversity variation of Cupressus funebris secondary forest in landslide site after 5.12 Wenchuan Earthquake北大核心CSCD

The characteristics of species diversity in Cupressus funebris secondary forest under the effect of geological hazard were explored by selecting the typical landslide surface of Fenghuang Mountain, Leigu Town, Beichuan County, which was derived from the 5.12 Wenchuan earthquake. The results showed that 93 species belonged to 42 families, and 78 genera were found in the landslide area, where the main families were Compositae, Leguminosae, and Gramineae-based. Further, 97 species in the transition area belonged to 39 families and 80 genera, and the main families were Compositae, Gramineae, and Rosaceae-based. In all, 108 species were recorded in the non-landslide area, which belonged to 59 families and 92 genera, the main families of which were Compositae, Rosaceae, and Gramineae-based. Compositae and Gramineae played important roles in the landslide recovery process after earthquake. The Pielou index (JSW) was the highest in the non-landslide area of the herb layer, whereas the richness index (D) was the lowest; the Shannon-Wiener index (H) and Simpson index (H') showed medium values. In the non-landslide area, the D, H, and H' were the highest in the shrub layer, whereas the JSW was the lowest. Further, the species diversity index of the transitional area was higher than that of the landslide area. In the tree layer, the D and H were both the highest and lowest in the landslide area and transition area, respectively. In contrast, the H' and JSW were the highest in the transition area and the lowest in the non-landslide area. The distribution of vegetation was generally consistent with the distribution of soil nutrients in the spatial distribution of surface soil nutrients. The succession of trees in the landslide area was relatively slow and at the initial stage, and the pioneer species were Coriaria nepalensis, Leptopus chinensis, and Arthraxon lanceolatus in this area. Taken together, the findings suggested that the stability of a plant community can be increased by improving the soil and stabilizing the slope. © 2018 Science Press. All rights reserved.     

Soil erosion hazard evaluation—An integrated use of remote sensing,GIS and statistical approaches with biophysical parameters towards management strategies

Soil erosion hazard maps can be an essential tool in erosion prone areas as they explain and display the distribution of hazards and areas likely to be affected to different magnitudes. Therefore, it is very useful to planners and policy makers initiating remedial measures and for prioritizing areas. In this study, a numerical model was developed for soil erosion hazard assessment, in which Z-score analysis was combined with a geographical information system (GIS) to compute a synthetic soil erosion hazard index (SEHI). For this model, nine factors which have notable impact on soil erosion were selected. To generate the selected factors remote sensing, analytical hierarchy process (AHP) and GIS techniques along with spatial models were applied. To standardize all of the factors and establish the factor weights, the AHP method was adopted. For Z-score analysis with selected standardized factors, the Integrated Land and Water Information System (ILWIS) software was used and nine individual layers with Z-scores were prepared. Afterwards, the layers were integrated with their factor weights by means of a weighted linear combination to derive a SEHI value for each pixel. To classify the discrete SEHI map to represent a meaningful regionalization of soil erosion hazard, the equal distance cluster principle was used and graded into four levels of hazard; very high, high, moderate and low. The results depicted that in general, a moderate hazardous condition of soil erosion was found in the study area and the proposed approach was also able to identify the areas under high and very high hazards that require urgent intervention on a priority basis. Based on this study, comprehensive erosion hazard management strategies were anticipated for the efficient management of present and future erosion disaster in the area.     

Modeling and interpreting the accuracy assessment error matrix for a doubly classified map

This paper considers two maps having the same spatial extent and the same mapping categories but where each map is subject to classification error. An overlay of the maps yields a (dis)similarity matrix whose (i, j)-entry is the areal proportion placed into category i by the first map and into category j by the second map. A parametric model, called the latent truth model, is proposed which specifies the dissimilarity matrix in terms of the true (but unknown) proportions for the mapping categories as well as the unknown error rates for the two maps. The number of parameters in the model exceeds the degrees of freedom in the dissimilarity matrix. However, a method of regularization is applied to effectively reduce the dimension of the parameter space and to permit model fitting. From the fitted model, one obtains estimates for the true mapping proportions as well as estimated error matrices for each of the maps. Accuracy assessment characteristics for each map (such as user's accuracy, producer's accuracy, overall accuracy, and the kappa coefficient) can be computed from the estimated error matrices. Methods are illustrated with two landcover maps of Wicomico County, Maryland.     

Quantifying forest fragmentation spatial process in the developing State of Selangor,peninsular Malaysia

Forest fragmentation has several phases; thus, the ecological significance of each phase during a particular period of time must be interpreted. To interpret, this study quantifies the magnitude of forest loss and the changes in the temporal pattern of fragmentation in the State of Selangor, peninsular Malaysia. Using the decision tree model of land transformation, five phases of forest fragmentation were identified: perforation, dissection, dissipation, shrinkage and attrition. This analysis showed that the magnitude of forest loss was the highest during the dissipation phase. The patchiness analysis showed that dissipation contributes to the highest environmental uncertainty found for the forest patches. This study can be considered a first step in the exploration of the properties and the behavioural pattern shown by the spatial process of forest fragmentation.     

Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India

Fire is the most spectacular natural disturbance that affects the forest ecosystem composition and diversity. Fire has a devastating effect on the landscape and its impact is felt at every level of the ecosystem and it is possible to map forest fire risk zone and thereby minimize the frequency of fire. There is a need for supranational approaches that analyze wide scenarios of factors involved and global fire effects. Fires can be monitored and analyzed over large areas in a timely and cost effective manner by using satellite imagery. Also Geographical Information System (GIS) can be used effectively to demarcate the fire risk zone map. Bhadra wildlife Sanctuary located in Kamataka, India was selected for this study. Vegetation, slope, distance from roads, settlements parameters were derived for a study area using topographic maps and field information. The Remote Sensing (RS) and Geographical Information System (GIS)-based forest fire risk model of the study area appeared to be highly compatible with the actual fire-affected sites. The temporal satellite data from 1989 to2006 have been analyzed to map the burnt areas. These classes were weighted according to their influence on forest fire. Four categories of fire risk regions such as Low, Moderate, High and Very high fire intensity zones were identified. It is predicted that around 10.31% of the area falls undermoderate risk zone.     

Application of geographic information system technique and analytical hierarchy process model for land-use suitability analysis on coastal area

Land-use suitability is the ability of a given type of land to support a defined use. GIS is known as a powerful tool for handling spatial data in land-use analysis. Application of this tool alone cannot overcome the lack of consistency in opinions given by experts when trying to assign relative importance to each of the several criteria considered in a suitability analysis. The combination of GIS and Multi-Criteria Decision Analysis (MCDA) is a powerful approach used to assess land suitability. To address this issue, the Analytical Hierarchy Process method is used in combination with the GIS tool. The aim of this study is to demonstrate how GIS tools and AHP model can be used for integrated coastal resource planning and management. Based on the information from final map/suitability map, we can define the best area. The findings indicate that the area 1 (2111 m) from class 3 is the most appropriate one because it has good facilities and wide open areas. This study indicates how the integrated tool is handled effectively in a land use suitability analysis for building hotels in the coastal areas of Terengganu in Malaysia. This research develops a framework for integrating GIS and AHP to incorporate the decision maker’s preferences on a range of factors in finding land areas suitable for coastal development.     

基于GIS的崂山区地下空间利用适宜性评价

为了更好地利用青岛崂山区地下空间资源,在全面分析该区地形、地质、水文等条件的基础上,结合岩土工程勘察原始数据和现场水样调查分析报告,根据模糊数学理论和专家经验,遴选出地形地貌、地质构造、工程地质、水文地质、洪水淹没、人口密集程度为评价因子并确定各因子的权重,研究构建了该区地下空间利用评价模型。将评价模型与GIS相结合,利用ArcGIS强大的空间分析功能,采用定量分析的方法得到地下空间利用适宜性分区。研究表明青岛崂山区地下空间建设适宜区面积为106．1km^2,占评价区域面积的73．8％;一般区域面积为26．5km^2,占评价区域面积的18．4％;不适宜区域面积为11．14km^2,占评价区域面积的7．8％。     

河南省大气污染的气候分类

分析确定了影响大气污染的主要气候因子。根据省内各气象站的常规测资料，采用模糊聚类和纳污指数的方法将该省分成５个气候类型区。结果表明，纳污能力以中部地区为最强，占全省面积３／５的南、东、北邻近省界地区较强，豫西山区最弱。     

GIS and geostatistics: Essential partners for spatial analysis

Initially, geographical information systems (GIS) concentrated on two issues: automated map making, and facilitating the comparison of data on thematic maps. The first required high quality graphics, vector data models and powerful data bases, the second is based on grid cells that can be manipulated by suites of mathematical operators collectively termed map algebra. Both kinds of GIS are widely available and are taught in many universities and technical colleges. After more than 20 years of development, most standard GIS provide both kinds of functionality and good quality graphic display, but until recently they have not included the methods of statistics and geostatistics as tools for spatial analysis. Recently, standard statistical packages have been linked to GIS for both exploratory data analysis and statistical analysis and hypothesis testing. Standard statistical packages include methods for the analysis of random samples of cases or objects that are not necessarily co-located in space—if the results of statistical analysis display a spatial pattern then that is because the underlying data also share that pattern. Geostatistics addresses the need to make predictions of sampled attributes (i.e., maps) at unsampled locations from sparse, often expensive data. To make up for lack of hard data geostatistics has concentrated on the development of powerful methods based on stochastic theory. Though there have been recent moves to incorporate ancillary data in geostatistical analyses, insufficient attention has been paid to using modern methods of data display for the visualization of results. GIS can serve geostatistics by aiding geo-registration of data, facilitating spatial exploratory data analysis, providing a spatial context for interpolation and conditional simulation, as well as providing easy-to-use and effective tools for data display and visualization. The value of geostatistics for GIS lies in the provision of reliable interpolation methods with known errors, methods of upscaling and generalization, and for supplying multiple realizations of spatial patterns that can be used in environmental modeling. These stochastic methods are improving understanding of how errors in models of spatial processes accrue from errors in data or incompleteness in the structure of the models. New developments in GIS, based on ideas taken from map algebra, cellular automata and image analysis are providing high level programming languages for modeling dynamic processes such as erosion or the development of alluvial fans and deltas. Research has demonstrated that these models need stochastic inputs to yield realistic results. Non-stochastic tools such as fuzzy subsets have been shown to be useful for spatial analysis when probabilistic approaches are inappropriate or impossible. The conclusion is that in spite of differences in history and approach, the linkage of GIS, statistics and geostatistics provides a powerful, and complementary suite of tools for spatial analysis in the agricultural, earth and environmental sciences.     

Bioactivity,adsorption and persistence of pretilachlor in paddy field soils

The effects of environmental factors on bioactivity, adsorption and persistence of pretilachlor were studied in the laboratory and greenhouse using cucumber (Cucumis sativus L.) as the bioassay species. The three soils studied viz. Bernam, Selangor and Sabrang series were chosen for their different characteristics. The half‐life of pretilachlor decreased from 10.24 to 4.90 days as temperature increased from 25°C to 35°C in the Selangor Series soil and from 10.86 to 7.63 days in the Bernam Series soil at 60% field capacity. At the same moisture level, an increase of temperature from 25°C to 35°C also reduced the half‐life of pretilachlor in Sabrang soil from 8.87 to 2.59 days. The half‐life of pretilachlor also decreased with increasing moisture levels in Selangor and Sabrang series but not in Bernam series soils. The greatest adsorption of pretilachlor was observed in Bernam series, followed by Selangor and Sabrang series. No phytotoxic residue of pretilachlor was detected in the supernatant after 10 h incubation. Since the residue was strongly adsorbed in Bernam series, its biological activity was less than in the other soils studied.     

厦门环同安湾海岸带景观空间格局动态变化分析

利用遥感和地理信息技术,运用平均斑块面积与分离度指数、多样性指数、破碎度指数、优势度指数和分维数等景观格局指数,以厦门环同安湾区1989年、1997年和2007年3个时期遥感影像解译数据作为基本信息源,对过去18年间环同安湾海岸带的景观格局的时空变化过程和演变趋势进行了研究。结果表明：1989—2007年,农田面积大量...     

A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

Contrasting approved uses against actual uses at La Restinga Lagoon National Park, Margarita Island, Venezuela. A GPS and GIS method to improve management plans and rangers coverage

Protected areas are the core of efforts to conserve biological diversity and zoning uses, and they are used as a tool for their management. Global Positioning Systems (GPS) and Geographic Information System (GIS) have been used to evaluate conflicts between approved uses and actual uses in La Restinga Lagoon National Park in Venezuela (LRLNP). The park (188.6 km²), covers various ecosystems such as coastal lagoons, marine waters and xerophytic vegetation, it is visited by up to 260,000 people visit per year. A GIS, using a base map compiled from 1:25,000 maps was developed. Natural cover was mapped from LANDSAT VII TM images, orthophotomaps and aerial photographs. Spatial use data was collected by field GPS location of any use inside the park during 2 years. Thematic vector maps for each land cover, zoning area, and observed use were created. Maps of zoning and uses were overlapped and new maps for each use-zoning crossing were created. The park contain nine different zoning areas wherein 28 different uses were identified, 18 of them were direct consumptive and non-consumptive uses, occurring on 54% of the park. Tourism transit areas were the most used zones. More than 5,000 people use the park during a high season day. Superimposition of zoning maps with actual use data produced 13 uses taking place in not allowed areas (46% of park area). Most common prohibited use was commercial net fishing, occurring in nearly 40% of the park area. Therefore, identifying human use conflicts and its geographical distribution is a key issue to improve Management Plans as well as identifying hot sites. The GPS-GIS methodology presented here allows Park Rangers to select those areas which could benefit from enhanced supervision with the limited budget available.     

Levels of heavy metals (Zn,Cu, Cd,and Pb) in mudskippers (Periophthalmodon schlosseri) and sediments collected from intertidal areas at Morib and Remis,Peninsular Malaysia

The concentrations of Zn, Cu, Cd, and Pb were determined in different tissues of mudskipper fishes Periophthalmodon schlosseri caught at two selected locations (Morib and Remis) of the intertidal mudflat area of Selangor state, West Coast of Peninsular Malaysia and in surface sediment samples. Metal concentrations in the mudskipper tissue and in the sediment samples tended to vary significantly (p < 0.05) between the two locations. Higher metal concentrations (except for Zn) were mostly found in the tissues of mudskippers from Remis. For sediment samples, significantly (p < 0.05) higher metal concentrations of all metals were also found at Remis. Generally, metal distribution between different tissues of mudskipper varied with scales being highly accumulative of Zn, Cd, and Pb, while for Cu, the highest mean concentrations were found in the liver. The lowest mean concentrations of Zn, Cu, and Cd were found in the muscles except for Pb, which was lowest in the liver. This study suggests that mudskippers can be potential biomonitoring organisms for heavy metal bioavailability and contamination of intertidal coastal mudflats. The concentrations of Cd and Pb were slightly above the acceptable limits of Malaysian and European food safety guidelines.     

Assessment of the state of agroecosystem sustainability using landscape indicators: a comparative study of three rural areas in Greece

The research was based on a comparative study of three representative rural areas (Dovras, Larissa, and Messapia) in Greece. Remote sensing data were collected (maps, aerial photographs) for the landscape analysis and elaborated using GIS linked with economic and social parameters regarding land use. By using a selected core set of landscape indicators, this research aims at providing a useful tool for assessing agroecosystem management at territorial level and hopefully assist decision-making for the promotion of sustainability. The selected tool showed that the study area of Messapia presented the highest level of environmental sustainability, while the area of Dovras showed the best combination of agricultural productivity and landscape management. Results showed that the ecoregions of Dovras, Larissa, and Messapia presented a landscape composed of important ecological function areas in the percentages of 40%, 15%, and 70%, respectively, and of cultivated areas in the percentages of 55%, 71.19%, and 19.75%, respectively.     

A novelty coastal susceptibility assessment method: application to Valdelagrana area (SW Spain)

The main aim of this paper is to present a new methodology to determine coastal susceptibility to erosion and flooding processes by means of an index-based method. The proposed indices take into account physical parameters, such as dune and beach geomorphologic characteristics, shoreline evolution, local significant wave height and relative run-up. The coastal susceptibility has been estimated by elaboration of spatial input data into a GIS environment. The method has been tested in Valdelagrana area, a sandy spit located in SW Spain. The spit includes several morpho-sedimentary environments: sandy beach, discontinuous embryo dunes and foredunes, mud flats and wide areas of vegetated salt marshes. The Northernmost sector is densely urbanized whilst the rest is part of a natural protected area belonging to the Bahía de Cádiz Natural Park. The results obtained showed how the Southern part of the spit presented a high susceptibility due to an elevated erosion rate and the presence of low and discontinuous dune ridges.     

Application of analytical hierarchy process and geographic information systems in land-use suitability evaluation: a case study of Dümrek village (Çanakkale,Turkey)

This study used both analytical hierarchy process (AHP) and geographic information systems (GIS) to make a land-use suitability analysis for the village of Dümrek, NW Turkey. Primarily, alternative land uses for agriculture, meadow–pasture and forest were determined along with criteria for these alternatives and a hierarchical structure was produced and used to determine the weights of the criteria. Spatial data were identified by means of GIS and calculations were made using the suitability values specified and weights obtained from AHP. Suitability maps were then produced for the above land use alternatives. Subsequently, a synthesized suitability map was formed from these maps. According to the weights specified by AHP, the order of land use preferences among the alternatives for rural development of Dümrek was agriculture, forest and meadow. The synthesized suitability map showed that the areas allocated for forest and agriculture were close to the present ratios of use; however, meadow land, which does not exist at present, should be allocated as a land use to constitute 12.5% of the study area. Achieving sustainability in land use is possible by planners and administrators considering results obtained from land suitability mapping studies at the stage of allocating land uses. The method applied in this research is considered useful when taking policy decisions covering the evaluation of rural land use, particularly for subunits of the state administration.