Spatial Information Techniques Research Articles

A novel adaptive contextual information-based 2D-histogram for image thresholding

This paper proposes a new two-dimensional (2D) histogram containing an image's adaptive local spatial contextual information. The correlation information between pixels is essential to achieve the optimal thresholds for image segmentation purposes. But it is an arduous task to find adaptive spatial information efficiently. So, an innovative adaptive local spatial information technique is proposed here to compute a novel 2D histogram. An adaptive contraharmonic mean (ACM) filter-based 2D-histogram is designed that contains contextual information by a unique proposed logical pseudo-energy (LPE) function. The order of contraharmonic filter Q is adaptively obtained using LPE, and the 2D Tsallis-Havrda-Charvat entropy is utilized to obtain the segmented results. Setting the Q value of the contraharmonic mean filter is tedious, and the proposed LPE function solves this problem. For the result assessments, the presented framework is compared with other maximum sum entropy-based methods, which are 2D-Kapur, 2D-Renyi, 2D-Tsallis, 2D-Masi and Parzen-window-based Kapur entropies-based image segmentation approaches. The proposed ACM-based Tsallis-Havrda-Charvat (Tsallis) entropy (ACM-Tsallis) shows excellent segmented results above local mean-based 2D-entropies methods and the latest Parzen-based Kapur entropy approach.

Geospatial detection of Ommatissus lybicus de Bergevin using spatial and machine learning techniques

Dubas bug (Ommatissus lybicus de Bergevin) infestations are frequent in several Middle Eastern and North African countries, causing havoc to date palms. The key objectives of this research were (1) to examine the prevalence of the Dubas bug geographically and (2) to identify hotspots and clusters at the local levels. Normalised difference vegetation indices, kernel density estimations, spatial autocorrelations, Getis-Ord G*, Anselin spatial indicators, ordinary regression and forest-based classification and regression algorithms were used to detect Dubas bug infestations in the Al Batinah North and Al Batinah South governorates in Oman from 2017 to 2021. Images of the region, including several sub-locations with diverse levels of infestation, were collected throughout the Dubas bug spring and autumn generations during the study period. The spring generation's kappa coefficient for identifying infestation levels was 0.97, whereas the autumn generation's kappa coefficient was 0.98. Varying infestation levels were observed over the study period, with some areas experiencing fewer or more cold spots or hotspots than others. The highest Moran's I value of 0.48, along with the highest z-score of 12.9, characterised the highly clustered pattern of infestation during the 2017 spring generation, while the lowest infestation clustering occurred during the 2019 spring generation, with the lowest Moran's I of 0.23 and the highest z-score of 7.2. Despite seasonal fluctuations, most Dubas bug hotspots were located in the southern parts of the study area. Thus, the infestations caused the greatest damage to palm tree farms in interior areas between mountains, away from the coastal plains. Dubas bugs were found in high-density clusters in the south of Sohar and Al Khaburah and in the southwest of Al Rustaq during the 2017 and 2018 spring generations. By observing statistically significant changes in infection clustering patterns, entomologists can assess the prevalence of infestation using spatial information techniques.

Development of livelihood vulnerability index for the coastal fishermen communities of Bangladesh using spatial information technique

The livelihood of the people in the dynamic coastal regions of Bangladesh is highly dependent on nature and thus is vulnerable to climatic events. The research study developed the Fishermen Livelihood Vulnerability Index (LVI) to measure the vulnerability of the fishing communities living in the coastal regions of Bangladesh. The weighted average method was used to measure the FLVI and the binary logistic regression was used to find out the determinants of vulnerability. The variables to measure vulnerability such as exposure, sensitivity, and adaptive capacity were taken from the IPCC climate vulnerability framework. The results, based on the findings of a survey of 164 households from 3 coastal districts (Patuakhali, Khulna & Cox's Bazar) indicated that the coastal fishing communities are more vulnerable (FLVI = 0.48) compare to the people living in other parts of Bangladesh. The study also revealed that the livelihood activities of the coastal fishermen are mainly affected by the impacts of natural disasters. Spatial maps were prepared to show the distribution of the vulnerable areas with indicating the houses according to their level of vulnerability. The study also identified the most significant factors of vulnerability. As part of the analysis, it was found that the variables that had the most significant impact on fishermen livelihood vulnerability were natural disasters, diseases due to climate change, NGO and microcredit, income, and piracy problem. The study also identified that vulnerability factors are highly influenced by spatial characteristics and thereby identification of location specific factors is crucial in assessing and calculating vulnerability score. The study concluded that if location specific fishermen vulnerability factors are identified after certain interval with the active engagement of local communities can generate valuable information for developing appropriate strategy to secure their livelihood in a sustainable way.

Mapping Tillage Practices Using Spatial Information Techniques.

Monitoring tillage practices is important for explaining soil quality and yield trends, and their impact on environmental quality. However, a common problem in sustainable residue management is scarcity of accurate residue maps. Because predictive insights on soil quality dynamics across a spatial domain are vital, this entry explicates on a new remote sensing-based technique for assessing surface residue cover. Here, an empirical model for mapping surface residue cover was created by integrating line-transect % residue cover field measurements with information gleaned from ground spectroradiometers and Advanced Wide-Field Sensor (AWiFS) satellite imagery. This map was validated using non-photosynthetic vegetation (NPV) fractional component extracted by spectral mixture analysis (SMA). SMA extracts fractional components of sensed signals in imagery, which within agricultural fields are NPV, green vegetation, bare soil, and shade. A stepwise linear regression between residue estimates by line transect and map generated using satellite imagery had R2 = 87%. Upon map categorization according to surface residue for a single AWiFS imagery encompassing an area of 836,868 ha, but focused on corn (Zea mays) fields within South Dakota, revealed that <4% of these corn fields had >15% surface residue cover left in the field by November 2009. Findings such as these may guide policy on soil quality, which is directly correlated with residue management. In the future, the spatial distribution of surface residues remaining after harvest in field planted with other crops and other seasons will be mapped. Besides, the efficacy of integrating hyperspectral sensor data to enhance accuracy will be investigated.

The spatial relationship between human activities and C, N, P, S in soil based on landscape geochemical interpretation.

The development and formation of chemical elements in soil are affected not only by parent material, climate, biology, and topology factors, but also by human activities. As the main elements supporting life on earth system, the C, N, P, S cycles in soil have been altered by human activity through land-use change, agricultural intensification, and use of fossil fuels. The present study attempts to analyze whether and how a connection can be made between macroscopical control and microcosmic analysis, to estimate the impacts of human activities on C, N, P, S elements in soil, and to determine a way to describe the spatial relationship between C, N, P, S in soil and human activities, by means of landscape geochemical theories and methods. In addition, the disturbances of human activities on C, N, P, S are explored through the analysis of the spatial relationship between human disturbed landscapes and element anomalies, thereby determining the diversified rules of the effects. The study results show that the rules of different landscapes influencing C, N, P, S elements are diversified, and that the C element is closely related to city landscapes; furthermore, the elements N, P, and S are shown to be closely related to river landscapes; the relationships between mine landscapes and the elements C, N, P, S are apparent; the relationships between the elements C, N, P, S and road landscapes are quite close, which shows that road landscapes have significant effects on these elements. Therefore, the conclusion is drawn that the response mechanism analysis of human disturbance and soil chemical element aggregation is feasible, based on the landscape geochemical theories and methods. The spatial information techniques, such as remote sensing and geographic information systems, are effective for research on soil element migration.

Analysis of the spatial relationship between heavy metals in soil and human activities based on landscape geochemical interpretation

The development and formation of chemical elements in soil are affected not only by parent material, climate, biology and topology factors, but also by human activities. The pollution sources of heavy metals in the environment are mainly derived from anthropogenic sources, and heavy metal elements in soil have been considered to be powerful tracers for monitoring the impacts of human activities. The present study attempts to analyze whether and how a connection can be made between macroscopical control and microcosmic analysis, to estimate the impacts of human activities on heavy metals in soil, and to determine a way to describe the spatial relationship between heavy metals in soil and human activities, by means of landscape geochemical theories and methods. In addition, the disturbances of human activities on Zn, Cr, Cu, Pb, Hg, Cd, Ni and Ag are explored through the analysis of the spatial relationship between human disturbed landscapes and element anomalies, thereby determining the diversified rules of the effects. The study results show that the rules of different landscapes influencing heavy metal elements are diversified, and that the Zn, Ni, Pb, Hg and Ag elements are closely related with city landscapes, but Cu and Cd are not significantly affected by city landscapes; furthermore, the elements Ni, Pb, Hg, Ag and Cd are shown to be closely related with river landscapes, while evidently Zn is not affected by river landscapes; the relationships between mine landscapes and the elements Cr, Cu, Pb, Ni and Hg are apparent, among which Zn is not included; the relationships between the elements Zn, Cr, Cu, Pb, Hg, Cd, Ni and Ag and road landscapes are quite close, which shows that road landscapes have significant effects on these elements. Therefore, the conclusion is drawn that the response mechanism analysis of human disturbance and soil chemical element aggregation is feasible, based on the landscape geochemical theories and methods. The results of the study provide the possibility for applying spatial information techniques, such as remote sensing and geographic information systems, to study chemical elements in soil, thereby realizing the effective combination of macroscopic spatial information and microscopic mechanism of soil element migration research.

The application of a Multicriteria Spatial Decision Support System (MCSDSS) for the assessment of biodiversity conservation in the Province of Varese (Italy)

Spatial Decision Support Systems (SDSSs) are rapidly gaining traction for planning and policy-making, thanks to their significant new capabilities in the use of spatial or geospatial information.The present paper illustrates the development of a basic MCSDSS (Multicriteria Spatial Decision Support System) that integrates spatial information techniques such as GIS (Geographic Information System) and Multicriteria Analysis (MCA). Many spatial problems are complex and require the use of integrated analysis and models. In the work a methodological framework for such integration is provided, highlighting the advantages and characteristics of GIS and MCA coupling and paying attention to the issues of transparency and applicability. The method is illustrated with reference to a case study in the Province of Varese (Italy). The purpose of the research is to generate a suitability map of the study region, concerning the characterization of biodiversity conservation, to be used as a decision variable in spatial planning. Different criteria are considered in the analysis and the results are obtained in the form of maps and analyzed through IDRISI Andes software. In particular, by using the resulting suitability map as a means of analysis, it is possible to identify, for the sake of biodiversity conservation, some critical areas needing mitigation measures. In addition, areas with high biodiversity conservation values can be highlighted and monitoring procedures can therefore be planned. The study concludes with some lessons learned during the development of the MCSDSS and highlights that the applied methodology is an effective tool in providing decision support for spatial planning.

Evaluation of reclamation land productivity in mining districts

In order to evaluate the productivity of reclamation lands in mining districts, field investigation and spatial information technique were adopted to investigate influence factors of land. Based on the collected data of each land, membership grade of each factor was obtained based on Fuzzy logic, and combined weight of each factor was got by analytica1 hierarch process and Delphi method. According to membership grades and combined weights of all factors, integrated fertility index (IFI) of every land was calculated to reflect levels of the productivity of reclamation lands in mining districts. Experimental results show that spatial information technique is an efficient method in field data collection, while lack of evaluation data. By means of quantificational analysis of this method, IFI is appropriate to describe the productive capacity level of reclamation lands.

A rough set approach to analyze factors affecting landslide incidence

Landslide incidence can be affected by a variety of environmental factors. Past studies have focused on the identification of these environmental factors, but most are based on statistical analysis. In this paper, spatial information techniques were applied to a case study of landslide occurrence in China by combining remote sensing and geographical information systems with an innovative data mining approach (rough set theory) and statistical analyses. Core and reducts of data attributes were obtained by data mining based on rough set theory. Rules for the impact factors, which can contribute to landslide occurrence, were generated from the landslide knowledge database. It was found that all 11 rules can be classified as both exact and approximate rules. In terms of importance, three main rules were then extracted as the key decision-making rules for landslide predictions. Meanwhile, the relationship between landslide occurrence and environmental factors was statistically analyzed to validate the accuracy of rules extracted by the rough set-based method. It was shown that the rough set-based approach is of use in analyzing environmental factors affecting landslide occurrence, and thus facilitates the decision-making process for landslide prediction.

Utilizing 3-D topographical information for the quality assessment of a satellite surveying

Fostered by the rapid development of spatial information techniques, three-dimensional topographical information (e.g., digital terrain models and digital surface models) of a higher quality is becoming accessible to the public. The main emphasis of this study is on developing a satellite visibility and quality assessment technique for a global navigation and satellite system utilizing high-resolution 3-D topographical information. An adaptive sampling and analysis procedure is proposed to increase the computational efficiency while processing high-resolution topographical data. Additionally, a unified least-squares approach is introduced to realistically model the uncertainties in satellite orbits. Finally, simulation tests and global positioning system (GPS) field work have been performed to demonstrate the performance and capability of the proposed approach. The results reveal a significant improvement in efficiency and reliability for the quality estimation of a satellite surveying. Consequently, the proposed approach will benefit applications in which a pre-evaluation of the positioning quality is of a major concern for a satellite surveying (e.g., GPS field planning or network design).

A novel data mining technique of analysis and classification for landslide problems

Landslides during earthquakes have led to severe casualties and have resulted in damaged structures and facilities. The goal of the present study is to analyze the landslide problems in a remote area—Shei-Pa National Park in Taiwan. Spatial information techniques (Remote Sensing and Geographic Information System) with an innovative data mining technique, Discrete Rough Set (DRS) method, are incorporated to our study for analyzing landslides, their distribution, and classification. The present study provides how to find (1) the most representative data of landslide samples from the existing database, (2) the core attributes of the target categories: Normalized Difference Vegetation Index (NDVI) and Vegetation Index (VI), and (3) the thresholds (segment points) of each attribute on the target categories. A conventional approach, C4.5 Decision Tree Analysis, is used as a comparison. The methodology discussed in this study is of help to the analysis of landslide problems and thus facilitates the informed decision-making process.

Spatial decision support system for reclamation in opencast coal mine dump

This paper describes the development and applications of a decision support system that uses spatial information techniques and field survey data. The SDSS can enable the various levels of the targ...

Spatial Information Techniques in Rapid Response to Wenchuan Earthquake

Spatial Information Techniques in Rapid Response to Wenchuan Earthquake

On island landscape pattern of forests in Helan Mountain and its cause of formation

Based on the spatial information techniques such as RS, GIS, and GPS, the forest landscape patterns in the Helan Mountain, western China, were studied. The Landsat 5 TM data were used to classify the forest landscapes through RS digital cartography, and then, the landscape characteristics and landscape pattern were analyzed quantificationally. Furthermore, through spatial data collection and spatial analysis of the main disturbances in this area, the cause of landscape formation was studied. The results showed that the total 1177 forest landscape patches could be classified into 21 landscape types, and the forest landscape in the Helan Mountain was island pattern, which was encircled by deserta as matrix. The values of landscape diversity index and landscape fragmentation index were 2.61 and 0.43, respectively. In this area, the landscape pattern was clearly formed and continuously changed in response to geological processes, climate, activities of organisms, forest fire, desertification, human activities and so on. This landscape pattern had an obviously negative effect on the stability and ecosystem services of forests. So, scientific landscape planning and protection should be adopted to improve the sustainability of forest management in this area.

Development of a spatial Decision Support System (DSS) for the Spencer Gulf penaeid prawn fishery, South Australia

The Spencer Gulf penaeid prawn fishery in South Australia has undergone a substantial increase in fishing efficiency (and profitability) mainly due to the implementation of adaptive harvest strategies requiring rapid response for change to harvesting plans. This paper describes the management background and the decision-making process leading to the development of a basic Decision Support System (DSS) that uses spatial information techniques and near real-time fishery-independent survey data. The system is implemented through linking an Oracle database to ArcGIS, Genstat and Splus. Two examples show the application of the DSS for optimal harvest timing and assessment of fishery sustainability. Fishery-independent survey data are used to assess stock and model population growth. The first example shows the information flow leading to a dynamic stock model and the estimate of value change as a function of harvest time. The second example shows how the DSS is used to validate and refine existing biological reference limits by evaluating long-term detailed data sets of the prawn population structure and catch dynamics. We conclude that it is important for the economic benefit and sustainability of the fishery to maintain and improve the collection of long-term data sets that are independent of commercial fishery statistics.

GIS-based integration of interdisciplinary ecological data to detect land-cover changes in creek mangroves at Gazi Bay, Kenya

5 Laboratory of Forest Management and Spatial Information Techniques, Universiteit Gent, Coupure 653, B-9000 Gent, Belgium /ha) and complexity indices (1.86 and 1.12) at Makongeni and Kinondo 1, respectively, reflected intense human pressure in these areas. Areas that were easily accessible or close to human settlements appeared more vulnerable. Accrued information from a socioeconomic survey carried out over the same period corroborates the hypothesis that human influence was a major contributor to these changes. Historic aerial photographs together with satellite imagery indicate less than 20% decrease in coverage of R. mucronata between 1965 and 1992, but an increase of almost 35% in sand cover over the same period. The approach that was used in this study, one largely unprecedented in the East African region, was useful in drawing the conclusion that human influence was the most probable trigger of the observed changes.