Digital Land Cover Research Articles

Forecasting urban futures: Evaluating global land use data sensitivity for regional growth simulation in the Ruhr Metropolitan Area

In recent years, numerous multitemporal global land use and land cover products have been published acting as valuable source for training spatially explicit geosimulation models forecasting urban growth. However, there is a notable gap in research that specifically addresses the sensitivity of models traing with those data sets when it comes to regional modeling purposes. Accordingly, the objectives of this study were to calibrate, validate, and employ global urban input datasets for the regional simulation of urban growth by the year 2030. The SLEUTH urban growth model, focused on the metropolitan area of the Ruhr, Germany, was calibrated using the Global Human Settlement Layer, World Settlement Footprint Evolution, historical OpenStreetMap data, and a Digital Land Cover Model for Germany. The goal was to compare the results in terms of accuracy, certainty, quantity, and allocation, particularly in urban areas susceptible to floods and heat. While all models achieved high accuracy levels concerning quantity and allocation, the extent of new settlements varied from 40.77 km2 to 477.91 km2. The models based on World Settlement Footprint and OpenStreetMap exhibited higher certainty and lower stochasticity. As the simulated urban growth increased, there was a corresponding rise in the likelihood of allocating new settlements in areas affected by natural hazards. While all models presented a similar relative portion of new settlement areas impacted by floods, variations emerged in terms of areas affected by unfavorable thermal conditions. This study underscored the potential use of historical OpenStreetMap data in training cellular automation for geosimulating future settlement growth. Furthermore, it highlighted the applicability of global Earth observation-based urban datasets for regional geosimulation and explored the impacts of diverse input data on the accuracy, certainty, quantity, and allocation performances in simulating future conditions.

Unleashing the power of old maps: Extracting symbology from nineteenth century maps using convolutional neural networks to quantify modern land use on historic wetlands

Topographical maps from the nineteenth century hold significant historical and environmental value, providing insights into landscape changes over the past two centuries. These maps feature distinct symbols representing various land cover types, such as forests and wetlands, offering a unique historical perspective on land-use changes. For example, there has been a significant reduction in wetlands because of agricultural expansion and intensification which lead to biodiversity loss and increased greenhouse gas emissions globally. Our study uses U-net CNN to automatically segment wetland symbols from nineteenth century maps from hundreds of map sheets for an area of interest a large river catchment in Ireland. Extracted wetland extents were intersected with digital land cover datasets to estimate current land cover on former wetland (on both organic and mineral soils). Utilizing U-Net, we successfully automated the segmentation of wetland symbols from hundreds of nineteenth-century map sheets, focusing on a large river catchment area in Ireland. Our analysis achieved a very high F1 score of 98.2% and a Kappa of 89%. While it is challenging to verify the veracity of historical map content, the largely untapped information contained within these maps are important for understanding landscape change over time, and especially before the era of Earth observation & remote sensing. The data extracted from these sources can inform modern environmental management strategies, for example, in targeted rewetting of peatlands, or in habitat restoration.

Assessing ecosystem services in Russia: Case studies from four municipal districts

The task of organizing ecosystem accounting and integrating the value of ecosystems and ecosystem services into the management process has been set in Russia. The municipal level is the key one in the system of ecological spatial planning. However, the analysis of the official documents on spatial planning in Russia revealed that ecosystem services are still to be integrated into them. This paper aims to develop guidelines for the formation and implementation of integrated plans for the development of rural areas in the aspect of ecosystem development. The suggested methods of ES evaluation and monetization are based on the use of open statistical data and available digital land cover maps. This way, ecosystem services were calculated in biophysical indicators by converting the area of different types of ecosystems into the demanded and supplied volumes of ecosystem services. The described approaches were approbated on four municipal districts in different federal subjects of Russia (Kiznerskiy district in the Udmurt Republic; Elizovskiy district in Kamchatskiy krai; Maikopskiy district in the Adygeya Republic; and Bobrovskiy district in Voronezh oblast). It reveals that carbon regulation makes up the major part of a total ecosystem services value in the studied districts, thus making regulating services constitute the largest share of the total supplied ecosystem services volume. Recreational and other cultural ecosystem services do not make up a considerable part of the total ES value in any district. These evaluations, on the one hand, form an incomplete and exaggerated idea of ecosystems’ value as a source of natural resources; and on the other, detract their regulating and cultural services, which are also extremely significant for the sustainable development on different spatial levels.

Https://journals.scholarpublishing.org/index.php/AIVP/article/view/13922

Land use system is the interface between society and the biophysical environment; it is concern how humans use ecosystems services provide by natural resources.  Land-use and land-cover changes have great effects for the environmental and socioeconomic sustainability of rural communities. A capacity for detecting and reporting land use change is critical to evaluating and monitoring trends in natural resource conditions and the effectiveness of public investment in their management. In Mexico, despite ongoing mapping land use - land cover efforts; there remains a need for development of basic datasets providing quantitative and spatial land use/land cover information, mainly a detailed scale. Recent availability of high-resolution satellite remote-sensing images coupling with advances digital image processing techniques offers an improved opportunity to map in a more detailed scale the land use  and land cover, which will be an important tool to monitor the environment at local scales. It is the objective of this study, conduct a detailed mapping of land use-land cover in  Usumacinta watershed, using high-resolution satellite images and analyze  changes occurred when  is compared with  National Institute of Statistic and geography map - Series VII,2016. The basis for the land use and vegetation cover presented in this study is the digital land cover and land use classification elaborated with SPOT imagery, spatial resolution 1.5 meters, using  an object-based classification method  in combination with an image classification based on expert knowledge. The resulting map was  updated using Sentinel images  2021 and 2022 , by means of on screen digital interpretation. The result is a detail map of land use land cover, where new classes were generated as association and consociation units. As a conclusion is established that the resulting map is more stable in time, and it can be used as indicative to implement restoration measures on natural vegetation and as input to calculate a more precise estimations of agriculture statistic’s. All of this aspects can guide the design and implementation of public policies about land use.

Assessment and Mapping of Land Cover/Use Aided by Remote Sensing and GIS Techniques in Elnour Natural Forest, Blue Nile State, Sudan

The main aim of this study was to assess and mapping of land cover (LC) / land use (LU), using Remote Sensing (RS) and Geographical Information System (GIS) techniques in Elnor natural forest reserve, Blue Nile state, Sudan. The study also attempted to update some information in the study area such as LC and contour map by using different methods of data transformation and analysis such as Normalized Different Vegetation Index (NDVI), interpolation and supervised classification. Thematic Mapper (TM), Landsat Enhanced Thematic Mapper (ETM+) and Landsat8 images were used in LCLU changes and supported by field observation. The study area was affected by cutting the mother trees and overgrazing. Closed forests during 2000, 2010 and 2019 decreased from 36.9%, 28.5% and 18.7% respectively. Moreover, the total area of bare soil increased by about 14.4%, 47.2% and 39.7% in 2000, 2010 and 2019 respectively. The open forest (grassland) decreased from 48.7% in (2000) to 41.7% in (2019). A Digital land cover map was produced using LCCS software; depending on the fieldwork data, they were found in four Classes. Mixed Woodland Single Layer, Semi-Deciduous Shrub land with Open Herbaceous, Open Grassland, Single XII Layer and Hardpans. The study revealed that there are some indicators of land degradation during the last 20 years (2000, 2010 and 2019) and is expected to continue increasing unless serious measures are carried out to reduce it.

MAPPING MULTI-SCALED LANDSCAPE TYPOLOGY OF LANG SON PROVINCE

The landscape of a territory is generated by natural components, and it is also influenced by human activities. The complexity of factors making up landscape and the relationship between them with humans has created a diverse and complex differentiation of landscape in Lang Son province. Three factors reflecting typical characteristics of Lang Son landscape: Digital Elevation Model (DEM), soil, and land cover have been selected in this study. The concept of multiscaled landscape classification based on the European Landscape Convention (ELC) is applied for combining the holistic with parametric approaches and combining typological with multi-scaled landscape classifications. A number of possible combinations between 9 land cover variables, 9 soil combination variables, and 5 topographic variables have yielded 40 landscape units for Lang Son province. The obtained results are basic documents for contributing to modern and practical landscape research on the one hand, and to territory planning for sustainable development of Lang Son on the other hand.

GERMANYS FIRST CLOUD-BASED WEB SERVICE FOR LAND MONITORING USING COPERNICUS SENTINEL-2 DATA

Abstract. The research and development project named Landscape Change Detection Service (German abbreviation: LaVerDi) was initiated by the German Federal Agency for Cartography and Geodesy (BKG). Within the scope of the project a monitoring service for landscape changes was developed and implemented using free Copernicus satellite data for an automated derivation of potential land cover change. This change indication is meant to be used to update or continue BKG in-house products, such as the Digital Land Cover Model Germany (LBM-DE), in a comprehensive and uniform quality. The results can be further used for numerous applications or as change information for administration and planning, and for the compilation of spatial statistics. It satisfies the users' need for a national service for open data on land cover changes and thus represents the first automatic and verified national satellite product for land cover changes in Germany. As input data the service uses pre-processed Sentinel-2 data from the European Copernicus satellite program, as well as an image segmentation approach to extract change objects. Using an improved cloud mask algorithm, Sentinel-2 tiles with up to maximum cloud coverage of 60% can be used for analysis. The service (data processing, change detection, visualisation) runs on the German “Copernicus Data and Utilization Platform” (CODE-DE). As of December 2020, the INSPIRE-compliant LaVerDi web service is operational. The thematic accuracy of the generated change layers is above the given requirements (minimum of 80%), considering the 95% confidence interval for all relevant land cover classes in certain test areas. The transferability of the methodology has been successfully shown by a prototypic nationwide demonstrator in early 2020 and is therefore expected to reliably detect both long-term and seasonal changes.

Dengue Risk Mapping from Geospatial Data Using GIS and Data Mining Techniques

Dengue fever is a major public health problem and has been an epidemic in Thailand for a long time. Therefore, there is a need to find a way to prevent the disease. This research aimed to explore the important factors of dengue fever, to study the factors affecting dengue hemorrhagic fever in Surat Thani Province, and to map the potential outbreak of dengue fever. Collecting patient information was done including, Rainfall, Digital Elevation Model (DEM), Land Use and Land Cover (LULC), Population Density, and Patients in Surat Thani Province, which was analyzed using data mining techniques involving analysis using 3 algorithms comprising Random Forest, J48, and Random Tree. The correct result is Random Forest since the accuracy of the data is 96.7 percent followed by J48 with accuracy of 95.9 percent. The final sequence is Random Tree with accuracy of 93.5 percent. Then, using the information can be displayed through ArcGIS program to see the risk points that are compared to the risk areas that have been previously done. The results can be very risky in Mueang District, Kanchanadit District, and Don Sak District, corresponding to the information obtained from the Public Health Office and the risk map created from the patient information.

Mapping and Assessing Soil Sealing in Padua Municipality through Biotope Area Factor Index

Soil sealing is a worldwide phenomenon of covering of natural or seminatural soil with impervious surfaces, such as built-up or paved surfaces. It is widely recognized as a major environmental issue which drives landscape fragmentation and ecosystem services degradation and loss. Italy is one of European countries with the highest extent of soil sealing. The most affected area is northern Italy, especially the Po Valley with more than 12% of sealed surfaces. According to official data, Veneto Region and the city of Padua are seriously affected by this phenomenon. The Biotope Area Factor is a consolidated ecological urban index for mapping soil sealing, adopted in different European cities to support urban planning; it expresses the ratio of the ecologically effective surface area in relation to the total land area according to land cover classes. The general aim of this study is to map and to assess soil sealing in the whole municipal territory of Padua using the Biotope Area Factor (BAF) index. We tested and adopted a digital land cover map together with aerial images to perform a BAF analysis on the whole municipal territory of Padua. By using sample areas previously analyzed, we validated our source data by a double spatial validation process; therefore, soil sealing analysis was scaled-up to the municipality territory. Results show that in the city of Padua, the average BAF index value is 0.6; totally permeable surfaces (BAF = 1) cover 59.5%, whereas totally “sealed” surfaces (BAF = 0) are 40.3% of the municipal territory (93 km2). Most of the sealed soil is located in the east sector and in the historical core of the city, with BAF values ranging from 0 to 0.2. A particularly critical area is identified within the new industrial area of the city, which is strongly affected by soil sealing. BAF maps are useful tools to identify critical areas by geovisualizing surface permeability at a very detailed scale and by enabling further analyses for hydrogeological risk assessment and urban climate regulation. Moreover, the use of BAF maps at urban scale today represent an important tool for urban management, especially for policy makers who are planning mitigation and compensation measures to control soil sealing.

Estimation of Mining and Landfilling Activities with Associated Overburden through Satellite Data: Germany 2000–2010

Despite ever-increasing material extraction on the global scale, very few studies have focused on the relationship between mining activities, overburden, and landfilling. This is mainly due to the lack of statistical data. Yet, large mining activities cause environmental strain to the natural environment, and are often cause of irreversible alterations to the natural landscape. To circumvent this problem, we develop a methodology that employs the digital elevation model and land cover to detect and analyze mining and landfilling site over time. We test our methodology with the case of Germany for the years 2000–2010. We then confront our results with statistically available data, to verify whether this methodology can be applied to other countries. Results from the analysis of satellite data give 15.3 Pg of extracted materials and 7.8 Pg of landfilled materials, while statistics report 29.4 Pg and 1.8 Pg, respectively. This large difference was likely due to the different frequency of recording, where satellite data was updated after 10 years, while statistics were reported yearly. The analysis of the anthropogenic disturbance with spatial information can effectively contribute to observe, analyze, and quantify mining activities, overburden, and landfills, and can thus provide policy makers with useful and practical information regarding resource usage and waste management.

Evaluating Nature Tourism Destination Potentiality in Samosir Regency using Remote Sensing and GIS

Samosir regency is one of the national tourism destinations that has many tourist attractions for both domestic and foreign tourists. The management of sustainable tourism destination requires inventory of tourism assets. Inventory of tourism assets can be determined through natural resources condition in tourism area. Land cover monitoring become useful information to determine natural resources condition in tourism area. Remote sensing technology can be used to monitor land cover changes quickly, accurately and effectively. This research uses a method of digital land cover classification with object-based image analysis (OBIA) approach, because this method effectively used in high resolution image with true colour (RGB). The overall accuracy value obtained from land cover classification using the OBIA approach is 75,4 %. This study aims to analyze the potential of tourist attractions using land cover data as the determinants of physical characteristics besides tourist attractions in addition to attractions accessibility and facilities. Determination of tourism potential is obtained by looking at the relationship between the tourism site physical characteristics with the attraction of a tourist object then categorized using the scoring method. the result of multiple regression analysis obtained by correlation coefficient is R = 0,832, which means there is relationship between facility variable, land cover, and accessibility to the number of visitors in Samosir regency. Tourist attraction potential of Samosir regency is divided into three that is high, middle and low potential tourist attraction. The level of potential tourism object categories is expected to be used in the development of tourism planning in Samosir District to become a sustainable tourism destination.

MAP OF PLOWED SOILS OF RUSSIA

The paper presents the results of the of the country's plowed area assessment on the basis of the Unified State Register of Soil Resources of Russia, a digital land cover map created on the basis of MODIS satellite data, and study of plowed land areas according to the data of the Internet service Google Earth. Information on the plowing of soils is tied to the soil-geographical units. The map shows the actually plowed soils and soils under the layland. Independent verification of the plowland soil digital map of Russia showed its high enough accuracy (an error of about 8–9%). The share of all plowed soils of the Unified State Register of Soil Resources in Russia has been determined. It is found that chernozem soils predominate on the arable land of Russia, from the northernmost subtypes to the most southern ones. Somewhat unexpected is the presence in this list of meadow chernozem solonetzic and solonchak soils. The map is intended for applied analysis and accounting of soil resources at the national level.

Interpretability Evaluation of Annual Mosaic Image of MTB Model for Land Cover Changes Analysis

To verify whether the annual mosaic image of MTB model is acceptable for further digital analysis, it is necessary to evaluate the visual interpretability. The MTB model is an effort to integrate multi-scene and multi-temporal data, to obtain a minimum cloud cover mosaic image in locations that are often covered by clouds and haze. This study is to evaluate the interpretability of the annual mosaic image for analysis of the land cover changes. The data used are the images of 2015, 2016, and 2017 covers a part of central Sumatra. Visual interpretations with a series of steps are used, starting with identification of the objects using interpretation keys, followed by spectral band correlations, scattergram analysis, and ended by consistency assessment. The consistency assessment step is performed to determine the level of clearness and easiness of the object recognition in the annual mosaic images. The results showed that the most optimal spectral bands used for RGB combinations for visual interpretation were Band SWIR-1, Band NIR, and Band Red. Based on the evaluation results, the annual mosaic image of MTB model performed the consistent results of the clearness objects and the easiness of the object recognition. Thus the annual mosaic image of MTB model of 0.02x0.02 degree tile is acceptable for further digital processing as well as digital land cover analysis.

Natural-color maps via coloring of bivariate grid data

Natural ground color is useful for maps where a representation of the Earth's surface matters. Natural color schemes are less likely to be misinterpreted, as opposed to hypsometric color schemes, and are generally preferred by map readers. The creation of natural-c\\olor maps was once limited to manual cartographic techniques, but they can now be created digitally with the aid of raster graphics editing software. However, the creation of natural-color maps still requires many steps, a significant time investment, and fairly detailed digital land cover information, which makes this technique impossible to apply to global web maps at medium and large scales. A particular challenge for natural-color map creation is adjusting colors with location to create smoothly blending transitions. Adjustments with location are required to show land cover transitions between climate zones with a natural appearance. This study takes the first step in automating the process in order to facilitate the creation of medium- and large-scale natural-color maps covering large areas. A coloring method based on two grid inputs is presented. Here, we introduce an algorithmic method and prototype software for creating maps with this technique. The prototype software allows the map author to interactively assign colors to design the appearance of the map. This software can generate web map tiles at a global level for medium and large scales. Example natural-color web maps created with this coloring technique are provided.

Volume and Carbon Estimates for the Forest Area of the Amhara Region in Northwestern Ethiopia

Sustainable forest management requires a continuous assessment of the forest conditions covering the species distribution, standing tree volume as well as volume increment rates. Forest inventories are designed to record this information. They, in combination with ecosystem models, are the conceptual framework for sustainable forest management. While such management systems are common in many countries, no forest inventory system and/or modeling tools for deriving forest growth information are available in Ethiopia. This study assesses, for the first time, timber volume, carbon, and Net Primary Production (NPP) of forested areas in the Amhara region of northwestern Ethiopia by combining (i) terrestrial inventory data, and (ii) land cover classification information. The inventory data were collected from five sites across the Amhara region (Ambober, Gelawdiwos, Katassi, Mahiberesilasse and Taragedam) covering three forest types: (i) forests, (ii) shrublands (exclosures) and (ii) woodlands. The data were recorded on 198 sample plots and cover diameter at breast height, tree height, and increment information. In order to extrapolate the local terrestrial inventory data to the whole Amhara region, a digital land cover map from the Amhara’s Bureau of Agriculture was simplified into (i) forest, (ii) shrubland, and (iii) woodland. In addition, the forest area is further stratified in five elevation classes. Our results suggest that the forest area in the Amhara region covers 2% of the total land area with an average volume stock of 65.7 m3·ha−1; the shrubland covers 27% and a volume stock of 3.7 m3·ha−1; and the woodland covers 6% and an average volume stock of 27.6 m3·ha−1. The corresponding annual volume increment rates are 3.0 m3·ha−1, for the forest area; 1.0 m3·ha−1, for the shrubland; and 1.2 m3·ha−1, for the woodland. The estimated current total volume stock in the Amhara region is 59 million m3.

Contribution of CRP to Illinois Bobwhite Habitat at the Landscape Level (Poster Abstract)

Northern bobwhite (Colinus virginianus) population declines in the midwest have been attributed to habitat degradation and loss due mainly to intensified agricultural land use and farming practices. Thus, there was initial optimism that the Conservation Reserve Program (CRP) would benefit bobwhites by converting cropland to semi-permanent grassland. However, CRP apparently has not positively impacted regional or statewide population trends in Illinois. Deficiencies at both site and landscape level may be involved. To address the latter issue, we mapped the location of each individual CRP field (> 8,800) in 11 representative counties within the bobwhite range in Illinois. We then analyzed their spatial relationship to other land cover and bobwhite habitat using Geographic Information Systems and a statewide digital land cover map based on classified satellite imagery. Existing bobwhite habitat and CRP fields tended to be spatially correlated because both are largely confined to moderately rolling terrain as opposed to very hilly or very flat areas. Nevertheless, almost 25% of all existing CRP acreage within our 11 study counties occurred in landscapes with insufficient woody edge to support high bobwhite populations. Furthermore, CRP did not always provide the habitat component most limiting for bobwhites.

Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas

Since the beginning of the 20th century, the overall occupied range of the lesser prairie-chicken (Tympanuchus pallidicinctus) has declined by 84% commensurate with population trends. Much of this decline has been attributed to the loss and fragmentation of native grasslands throughout the lesser prairie-chicken range. However, quantification of changes in land cover in the distribution of the lesser prairie-chicken is lacking. Our objectives were to (1) document changes in the areal extent and connectivity of grasslands in the identified lesser prairie-chicken range in Kansas, USA, (>60% of extant lesser prairie-chicken population) from the 1950s to 2013 using remotely sensed data and (2) assess the potential of the Conservation Reserve Program (U.S. Department of Agriculture Program converting cropland to permanent cover; CRP) to mitigate grassland loss. Digital land cover maps were generated on a decadal time step through spectral classification of LANDSAT images and visual analysis of aerial photographs (1950s and 1960s). Landscape composition and configuration were assessed using FRAGSTATS to compute a variety of landscape metrics measuring changes in the amount of grassland present as well as changes in the size and configuration of grassland patches. With the exception of a single regional portion of the range, nearly all of the grassland converted to cropland in the lesser prairie-chicken range of Kansas occurred prior to the 1950s. Prior to the implementation of CRP, the amount of grassland decreased 3.6% between the 1950s and 1985 from 18,455 km2 to 17,788 km2. Since 1985, the overall amount of grassland in the lesser prairie-chicken range has increased 11.9% to 19,898 km2 due to implementation of CRP, although the area of grassland decreased between 1994 and 2013 as CRP contracts were not renewed by landowners. Since 1986 grassland in Kansas became more connected and less fragmented in response to the CRP. While the CRP has been successful in increasing grassland quantity and connectivity throughout the lesser prairie-chicken range in Kansas, offsetting loss of grassland since the 1950s, abundance and occupied range of lesser prairie-chickens has declined since the 1980s, suggesting that habitat quality is the principal factor influencing population demography of the species. Although the CRP is contributing to conservation actions for lesser prairie-chickens, efforts to improve habitat quality throughout the range of the lesser prairie-chicken are likely necessary to meet management goals. Continuation of the CRP faces an uncertain future in the face of rising commodity prices, energy development, and reduction in program scope, leaving open the possibility that these areas that have created habitat for lesser prairie-chickens could be lost.

ESTIMATION OF INSULATOR CONTAMINATIONS BY MEANS OF REMOTE SENSING TECHNIQUE

The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data). Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM) and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remote sensing in power maintenance. To our knowledge, this is the first trial to introduce remote sensing and relevant data analysis technique into the estimation of electrical insulator contaminations.

Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka.

The aim of the present study was to identify geographical areas and time periods of potential clusters of dengue cases based on ecological, socio-economic and demographic factors in northern Sri Lanka from January 2010 to December 2013. Remote sensing (RS) was used to develop an index comprising rainfall, humidity and temperature data. Remote sensing data gathered by the AVNIR-2 instrument onboard the ALOS satellite were used to detect urbanisation, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analysed RS data and databases were integrated into a geographical information system (GIS) enabling space-time clustering analysis. Our results indicate that increases in the number of combinations of ecological, socio-economic and demographic factors that are present or above the average contribute to significantly high rates of space-time dengue clusters. The spatio-temporal association that consolidates the two kinds of associations into one can ensure a more stable model for forecasting. An integrated spatiotemporal prediction model at a smaller level using ecological, socioeconomic and demographic factors could lead to substantial improvements in dengue control and prevention by allocating the right resources to the appropriate places at the right time.

Characterization of the Temporal and Spatial Dynamics of the Dengue Epidemic in Northern Sri Lanka

Abstract. Dengue outbreaks are affected by biological, ecological, socio-economic and demographic factors that vary over time and space. These factors have been examined separately, with limited success, and still require clarification. The present study aimed to investigate the spatial and temporal relationships between these factors and dengue outbreaks in the northern region of Sri Lanka. Remote sensing (RS) data gathered from a plurality of satellites: TRMM TMI, Aqua AMSR-E, GCOM-W AMSR2, DMSP SSM/I, DMSP SSMIS, NOAA-19 AMSU, MetOp-A AMSU and GEO IR were used to develop an index comprising rainfall. Humidity (total precipitable water, or vertically integrated water vapor amount) and temperature (surface temperature) data were acquired from the JAXA Satellite Monitoring for Environmental Studies (JASMES) portal which were retrieved and processed from the Aqua/MODIS and Terra/MODIS data. RS data gathered by ALOS/AVNIR-2 were used to detect urbanization, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analyzed RS data and databases were integrated into geographic information systems, enabling both spatial association analysis and spatial statistical analysis. Our findings show that the combination of ecological factors derived from RS data and socio-economic and demographic factors is suitable for predicting spatial and temporal patterns of dengue outbreaks.