Fine-scale Mapping Research Articles

Remote sensing of live and dead intertidal oyster reefs using aerial photo interpretation in Northeast Florida

The eastern oyster, Crassostrea virginica, has been classified as a keystone species as well as an ecosystem engineer because of the significant benefits that oysters and oyster reefs provide. Oyster reefs are being recognized as valuable for shoreline protection within coastal areas. The severe loss of oyster reef coverage has encouraged different types of conservation, mapping, monitoring and restoration efforts throughout its native range. Intertidal oyster reefs are an important habitat as well as an important industry in Florida, which makes accurate habitat monitoring a key element of resource management for this species. This project focused on creating a continuous intertidal oyster reef habitat map in northeast Florida. Existing aerial photography was used to identify oyster reef signatures, and map the distribution of intertidal oyster reefs throughout the study region using ArcGIS software. When accuracy assessment was completed, we found the number of observed agreements was 97% of the total observations. This mapping effort represents the first successful attempt at fine-scale oyster reef mapping across the entire northeast Florida region and resulted in a total of 17,953 individual reefs being mapped, with a total reef coverage of 651.86 ha. By using existing aerial photography, this methodology represents a low-cost method for reef mapping, compared to other methods such as drone imagery or field-based mapping. This baseline map of current oyster distribution will serve many functions for management this particular ecosystem. Future changes to reef distribution can be mapped and used to identify potential negative or positive impacts to the habitat.

Fine mapping and identification of the leaf shape gene BoFL in ornamental kale.

BoFL, a candidate gene conferring the feathered-leaved trait in ornamental kale, is located in a 374.532-kb region on chromosome C9. Leaf shape is an important economic trait in ornamental kale (Brassica oleracea var. acephala). Identifying the genes that influence leaf shape would provide insight into the mechanism underlying leaf development. In this study, we constructed F1, F2, BC1P1, BC1P2, and F2:3 populations from a cross between a feathered-leaved inbred line, F0819, and a smooth-leaved inbred line, S0835, of ornamental kale. Genetic analysis showed that the feathered-leaved trait was controlled by a semi-dominant gene, BoFL. Using bulked segregant analysis sequencing, we mapped the BoFL gene to a 4.17-Mb interval on chromosome C9. Next, we narrowed down the candidate region to 374.532-kb by fine-scale mapping between the two flanking markers INDEL940 and INDEL5443. We identified 38 genes in the candidate region, among which only Bo9g184610 showed significant variation in expression level between the two parental lines. Sequencing of the gene in the parental lines identified three single-nucleotide polymorphism (SNP) differences, containing two nonsynonymous and one synonymous SNPs, which resulted in coding variations of an aspartate and a phenylalanine residue in F0819, but an alanine and a leucine residue in S0835. A cleaved amplified polymorphic sequence (CAPS) marker, CAPS4610, corresponding to the first nonsynonymous SNP co-segregated with the leaf shape trait. We thus speculated that the gene conferring the feathered-leaved trait is BoALG10, a homolog of ALG10, which encodes an alpha-1,2-glucosyltransferase in Arabidopsis thaliana. This work will be useful for understanding the mechanism of leaf development and provides important information for the breeding of kale with novel leaf shapes.

Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard

Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field-scale using a data assimilation strategy.

Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk

Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.

A Novel Water Change Tracking Algorithm for Dynamic Mapping of Inland Water Using Time-Series Remote Sensing Imagery

Fine-scale mapping of inland water is important for regional or global ecosystem monitoring; however, due to the spectral heterogeneity of inland water, large-scale dynamic mapping of these water bodies is usually challenging. In this article, we propose a water change tracking (WCT) algorithm, that uses only optical data, for accurate inland water mapping. Simultaneously, time-series of Landsat imagery are used as an example for illustrating the effectiveness of the method. First, time-series of Minimum Normalized Water Score (MNWS) images and corresponding ‘bad-pixel’ (cloud, ice and snow) masks were developed using time-series of Landsat imagery. Secondly, based on the MNWS time-series stack and ‘bad-pixel’ masks, time-series trajectories were formed to track changes in water bodies. A 30-m Dynamic Inland Water Body Map (DIWBM) of China was then produced to test the robustness of the WCT algorithm. Eight test sites were selected, and the water detection algorithms used in the Global 1 arc-second Water Body Map (G1WBM) and the function of mask (FMASK) were applied to our data to provide a comparison with the DIWBM results. The results indicated that the DIWBM gave a better performance of 96.20% against 85.91% for the G1WBM results and against 76.36% for the FMASK results. The DIWBM achieved user's accuracies of 98.36% and 92.53%, and producer's accuracies of 97.45% and 95.39% for permanent and temporary water, respectively. The proposed method was also better at identifying small water bodies. It is concluded, therefore, that the WCT algorithm is a promising method for large-scale dynamic inland water mapping.

Combining Multiband Remote Sensing and Hierarchical Distance Sampling to Establish Drivers of Bird Abundance

Information on habitat preferences is critical for the successful conservation of endangered species. For many species, especially those living in remote areas, we currently lack this information. Time and financial resources to analyze habitat use are limited. We aimed to develop a method to describe habitat preferences based on a combination of bird surveys with remotely sensed fine-scale land cover maps. We created a blended multiband remote sensing product from SPOT 6 and Landsat 8 data with a high spatial resolution. We surveyed populations of three bird species (Yellow-breasted Bunting Emberiza aureola, Ochre-rumped Bunting Emberiza yessoensis, and Black-faced Bunting Emberiza spodocephala) at a study site in the Russian Far East using hierarchical distance sampling, a survey method that allows to correct for varying detection probability. Combining the bird survey data and land cover variables from the remote sensing product allowed us to model population density as a function of environmental variables. We found that even small-scale land cover characteristics were predictable using remote sensing data with sufficient accuracy. The overall classification accuracy with pansharpened SPOT 6 data alone amounted to 71.3%. Higher accuracies were reached via the additional integration of SWIR bands (overall accuracy = 73.21%), especially for complex small-scale land cover types such as shrubby areas. This helped to reach a high accuracy in the habitat models. Abundances of the three studied bird species were closely linked to the proportion of wetland, willow shrubs, and habitat heterogeneity. Habitat requirements and population sizes of species of interest are valuable information for stakeholders and decision-makers to maximize the potential success of habitat management measures.

Risk assessment for wildland fire aerial detection patrol route planning in Ontario, Canada

This study presents a model developed using a risk-based framework that is calibrated by experts, and provides a spatially explicit measure of need for aerial detection daily in Ontario, Canada. This framework accounts for potential fire occurrence, behaviour and impact as well as the likelihood of detection by the public. A three-step assessment process of risk, opportunity and tolerance is employed, and the results represent the risk of not searching a specified area for the detection of wildland fires. Subjective assessment of the relative importance of these factors was elicited from Ontario Ministry of Natural Resources and Forestry experts to develop an index that captures their behaviour when they plan aerial detection patrol routes. The model is implemented to automatically produce a province-wide, fine-scale risk index map each day. A retrospective analysis found a statistically significant association between points that aerial detection patrols passed over and their aerial detection demand index values: detection patrols were more likely to pass over areas where the index was higher.

Learning mean progressive scattering using binomial truncated loss for image dehazing

In this study, the authors propose a novel progressive dehazing network to address the single image haze removal problem based on a new mean progressive scattering model. Different from methods that learn atmosphere light and transmission maps with different networks, these two variables are optimised in a unified network. Following the methodology of traditional prior-based methods that estimate a coarse transmission map first, a progressive refinement branch in the decoder has been designed to restore the fine-scale transmission map. To improve the prediction accuracy of the transmission map, a novel binomial truncated loss that assigns weights to error values according to the probabilities of error occurrences has been proposed. An ablation study is conducted to verify the effectiveness of the components in the proposed method. Experiments in the synthetic datasets and real images demonstrate that the proposed method outperforms other state-of-the-art methods.

Improving Impervious Surface Estimation by Using Remote Sensed Imagery Combined With Open Street Map Points-of-Interest (POI) Data

As a key indicator of urban environments, the accurate mapping of impervious surface is essential. With the availability of high spatial resolution remote sensing imagery, such as Sentinel-2, it is feasible to produce fine-scale impervious surface maps. For mapping high-resolution impervious surfaces, object-based image analysis (OBIA) classification method demonstrated its efficiency and accuracy by combining spectral information and spatial information. Although with some success, impervious surface estimation remains challenging because different land cover types share similar spectral information. With the emergence of affordable GPS-enabled devices (such as a smartphone) and web 2.0 development, more and more people are getting involved in sharing their locations with others or posting on the Internet. These volunteered geographical information (VGI) data provide a brand-new prospect for mapping urban impervious surfaces. This research proposes an optimized method for impervious surface mapping based on Sentinel-2 multispectral imagery and open street map (OSM) points-of-interest (POI) data. The proposed method was tested in Milwaukee county, US, and the results show that the overall accuracy of the proposed OBIA increases from 82.57% to 87.02% compared with the conventional OBIA. Thus, this study provides an effective means of combining OBIA and the relatively new VGI POIs data to extract impervious surface with higher spatial resolution.

Fine-Scale Plant Richness Mapping of the Andean Páramo According to Macroclimate

How the macroclimate conditions spatial patterns of plant diversity has been extensively studied in many systems and usually proven a strong relationship, however tropical mountains remain understudied. Because understanding primary drivers of diversity patterns in the Andean paramo is still in its infancy, we focused on evaluating the role of the current macroclimate in form of three complementary hypotheses, energy, seasonality and harshness, in explaining local variation of plant species richness. We relied on 1559 vegetation plots that offered a fine-scale outlook on real species assemblages due to community rules and species’ interactions with their surrounding environment, including climate. Generalized Least Squares (GLS) regression models provided insight on the significance of the different hypotheses in explaining local plant richness, but only the energy and seasonality hypotheses received partial support. The best model was then combined with spatial interpolation Kriging modelling techniques to project species richness for a standardized 25m2 plot throughout the entire paramo biogeographic province. We highlighted a North-South increase in richness with several species-rich areas, potential local biodiversity hotspots, independent of the general gradient: the Amotape-Huancabamba zone, Sangay and Cotacachi areas, and eastern Venezuelan Andes. Our endeavor to finely map local richness is the first effort predicting macroecological patterns in the emblematic Andean paramo and contributes novel biogeographic knowledge useful to further support in-depth research and conservation focus in the northern Andes.

A comprehensive index for threatened biodiversity valuation

We present a new comprehensive index for mapping the relative conservation value of threatened biodiversity. The index is based on explicit criteria to (1) select threatened species according to regional government responsibility for species’ conservation; (2) combine species’ presence by means of weighting factors based on differences in threat status, sensitivity to disturbance, functional role, and amount of knowledge; and (3) map species distributions at the scale of 1 km × 1 km UTM squares or lower from the information available. We tested the performance of the index in the forest of Andalusia (southern Spain), an extensive European region of 87,268 km2, with 43,864 km2 (ca. 50%) classified as forest habitats. All species included in the Annexes of the European Birds and Habitats Directives inhabiting the target area, plus the regionally endemic species either ‘Critically Endangered’ or ‘Endangered’ not (yet) covered by the Directives were selected. The final list included 224 species: 81 plants, 76 birds, 31 mammals, 22 arthropods, six reptiles, five amphibians, and three mollusks. Fine-scale distribution maps were available for 108 species (48%). The remaining maps were downscaled from published 10 km × 10 km UTM maps using knowledge on species’ habitat requirements and land-use maps. We overlapped the fine-scale maps with the 86,546 forest tiles of the Andalusian Forest Map and weighted presences by a factor of relative conservation estimated for each species to obtain an index of the relative conservation of each forest tile. Index values were higher in protected areas, and in different forest habitats according to expected numbers of endangered species. Index values were correlated with the existence value of threatened species, but the index underestimate existence values especially for the most valuable tiles. The proposed index allows mapping the relative conservation value of threatened biodiversity by integrating knowledge on all threatened species into a single variable. Integration is based on explicit methodological criteria for species selection, combination of relative conservation values, and estimation of downscaled maps. Accurate mapping and synthetic summary of compulsory data on the conservation status of all species present in a territory advise its extended use for threatened biodiversity valuation in both Europe and elsewhere.

Mapping and Characterising HIV Transmission Hotspots in Sub-Saharan Africa: A Geospatial Analysis of National Survey Data

Background: In the generalised epidemics of sub-Saharan Africa (SSA), HIV prevalence shows patterns of clustered micro-epidemics. We mapped and characterised these so-called 'hotspots' for young adults (15-29 years of age), as a proxy for transmission hotspots, for seven countries in Eastern and Southern Africa: Kenya, Malawi, Mozambique, Tanzania, Uganda, Zambia, and Zimbabwe. Methods: We used geolocated survey data and applied kriging to predict HIV prevalence at unmeasured locations. We explored to what extent behavioural, socioeconomic and environmental factors explain HIV prevalence at the individual- and sample location-level, by developing a series of multilevel multivariable logistic regression models. We then compared and geospatially visualised how heterogeneity and hotspots can be explained by the models. Findings: We found clear HIV hotspots among young adults, characterised by areas with prevalences of over 11% or 15% alternating with areas of prevalences between 0% and 2%. Heterogeneity could be explained for 15·6% by an interplay of known behavioural, socioeconomic and environmental factors. Maps of the interpolated random effect estimates show that environmental variables, representing indicators of economic activity, were most powerful in explaining HIV hotspot locations. Interpretation: In young adults, micro-epidemics of relatively high HIV prevalence alternate with areas of very low prevalence, clearly illustrating the existence of transmission hotspots. These hotspots are partially characterised by high economic activity, relatively high socioeconomic status, and risky sexual behaviour. Localised HIV prevention interventions specifically tailored to the populations at risk will be essential to curb transmission. More fine-scale geospatial mapping of key populations, such as sex workers, and migrant populations, could help to further understand the drivers of these transmission hotspots, and to determine to what extent they fuel the generalised epidemics in SSA. Funding Statement: This study was funded by the Dutch AIDS Foundation (P-29702). Furthermore, Jan Hontelez was supported by the NWO Talent Scheme. Till Barnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the Federal Ministry of Education and Research; the Wellcome Trust; and from NICHD of NIH (R01-HD084233), NIA of NIH (P01-AG041710), NIAID of NIH (R01-AI124389 and R01-AI112339) as well as FIC of NIH (D43-TW009775). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: Not required.

Cold spot microrefugia hold the key to survival for Brazil's Critically Endangered Araucaria tree.

Brazil's Araucaria tree (Araucaria angustifolia) is an iconic living fossil and a defining element of the Atlantic Forest global biodiversity hotspot. But despite more than two millennia as a cultural icon in southern Brazil, Araucaria is on the brink of extinction, having lost 97% of its extent to 20th-century logging. Although logging is now illegal, 21st-century climate change constitutes a new-but so far unevaluated-threat to Araucaria's future survival. We use a robust ensemble modelling approach, using recently developed climate data, high-resolution topography and fine-scale vegetation maps, to predict the species' response to climate change and its implications for conservation on meso- and microclimate scales. We show that climate-only models predict the total disappearance of Araucaria's most suitable habitat by 2070, but incorporating topographic effects allows potential highland microrefugia to be identified. The legacy of 20th-century destruction is evident-more than a third of these likely holdouts have already lost their natural vegetation-and 21st-century climate change will leave just 3.5% of remnant forest and 28.4% of highland grasslands suitable for Araucaria. Existing protected areas cover only 2.5% of the surviving microrefugia for this culturally important species, and none occur in any designated indigenous territory. Our results suggest that anthropogenic climate change is likely to commit Araucaria to a second consecutive century of significant losses, but targeted interventions could help ensure its survival in the wild.

Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN ( SRM CNN ) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed SRM CNN method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed SRM CNN method was validated by visualizing output features and analyzing the performance of different geographic objects.

Mapping black ash dominated stands using geospatial and forest inventory data in northern Minnesota, USA

Emerald ash borer (EAB; Agrilus planipennis Fairmaire, 1888) has been a persistent disturbance for ash forests in the United States since 2002. Of particular concern is the impact that EAB will have on the ecosystem functioning of wetlands dominated by black ash (Fraxinus nigra Marsh.). In preparation, forest managers need reliable and complete maps of black ash dominated stands. Traditionally, forest survey data from the United States Forest Inventory and Analysis (FIA) Program have provided rigorous measures of tree species at large spatial extents but are limited when providing estimates for smaller management units (e.g., stands). Fortunately, geospatial data can extend forest survey information by generating predictions of forest attributes at scales finer than those of the FIA sampling grid. In this study, geospatial data were integrated with FIA data in a randomForest model to estimate and map black ash dominated stands in northern Minnesota in the United States. The model produced low error rates (overall error = 14.5%; area under the curve (AUC) = 0.92) and was strongly informed by predictors from soil saturation and phenology. These results improve upon FIA-based spatial estimates at national extents by providing forest managers with accurate, fine-scale maps (30 m spatial resolution) of black ash stand dominance that could ultimately support landscape-level EAB risk and vulnerability assessments.

Fine mapping a quantitative trait locus, qSER-7, that controls stigma exsertion rate in rice (Oryza sativa L.)

BackgroundStigma exsertion rate (SER) is a key determinant of outcrossing in hybrid rice seed production. A quantitative trait locus (QTL) for stigma exsertion rate in rice, qSER-7, has previously been detected on chromosome 7 by using a F2 population derived from two indica cytoplasmic male sterility (CMS) maintainers, Huhan 1B and II-32B.ResultsThe chromosomal location of qSER-7 was precisely delimited by fine-scale mapping. Near-isogenic lines (NILs) were established, one of which isolated the locus in the qSER-7II-32B line, which contains an introgressed segment of II-32B in the Huhan 1B genetic background, and exhibits a significantly higher stigma exsertion rate than that of the recurrent parent. Using 3192 individuals from the BC4F2 segregation population, the QTL qSER-7 was narrowed down to a 28.4-kb region between the markers RM3859 and Indel4373 on chromosome 7. According to the rice genome annotation database, three genes were predicted within the target region. Real-time PCR analysis showed significantly higher expression levels of LOC_Os07g15370 and LOC_Os07g15390 in II-32B than in Huhan 1B. LOC_Os07g15370(OsNRAMP5) was a previously reported gene for Mn and Cd transporter. The stigma exertion rates of OsNRAMP5-overexpressing plants were significantly higher than that of wild type plants, in contrast, a T-DNA insertion mutant osnramp5 showed a lower stigma exertion rate.ConclusionsIn the present study, the QTL qSER-7 was isolated to a region between the markers RM3859 and Indel4373. Two candidate genes were selected based on the expression difference between the two parents, which can facilitate the further cloning of the gene underlying the quantitative trait associated with qSER-7 as well as the marker-assisted transfer of desirable genes for stigma exsertion rate improvement in rice.

Advancing land-sea integration for ecologically meaningful coastal conservation and management

Coasts are among our most valuable natural assets but are under intense pressure from human use and climate change. Despite this, coasts – as a coherent ecological unit – have been poorly included in conservation plans, largely because they are inadequately delineated. There are usually gaps and overlaps at the edges of the separate terrestrial-, estuarine- and marine-realm maps, and often no clarity on which specific coastal boundary (e.g., high-water mark) was used, other than vaguely, ‘the coastline’. This particularly compromises conservation and management of ecotonal, intertidal ecosystems along realm-map seams because they are poorly defined and mapped. Therefore, a key step in advancing coastal conservation, assessment, planning and management is to generate a fine-scale ecosystem-type map that is seamless across realms. We undertook this for South Africa, aiming to delineate the ecotone into ecologically meaningful zones comprising structurally and functionally appropriate ecosystem types. We defined and mapped (at <1:3000) the ‘seashore’ as the land-sea interface between the dune scrub-thicket break and the back of the surf zone. The seashore is divided at the dune base into a landward ‘backshore’ and seaward ‘shore’, with the inherent dynamic variability included in the boundary delineation and constituent ecosystem types. Estuaries were also embedded into the map. Finally, we created rules for including adjacent terrestrial and marine ecosystem types in an ecologically determined coastal zone. We describe what tools this seashore integration and coastal delineation has unlocked, and how this places South Africa in a strong position to manage and conserve its coast.

Landsat-MODIS image fusion and object-based image analysis for observing flood inundation in a heterogeneous vegetated scene

Typhoon flooding normally occurs suddenly with short duration, and the thick cloud cover limits the ability of one single satellite to timely capture the inundation extent. Landsat satellite data with a spatial resolution of 30 m is spatially applicable for flooding research; however, its 16-day observation frequency is typically insufficient to observe short-term typhoon inundation. Alternatively, despite the coarse spatial resolutions, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor provide daily data, which is well suited for flood-related investigations. Accordingly, the idea of combining these two sources of data to generate a high spatial and temporal image would be useful. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was applied to generate cloud-free Landsat/MODIS synthesized data with a spatial resolution of 30 m for the delineation of the inundated areas during a flood event. This approach produces a Landsat-scale image for fine-scale flood mapping of areas where there are no observed cloud-free Landsat or similar resolution satellite images. The fusion model was implemented on atmospherically corrected surface reflectance, and the resultant reflectance values were validated by comparing with observed Landsat reflectance before further data interpretation. The blending results indicate that the synthetic Landsat-scaled image is highly correlated with Landsat surface reflectance, captured a day after the synthetic image acquisition date, over cloud-free areas. For image interpretation, an object-based image analysis (OBIA) approach with an optimal-scale segmentation and the support vector machine (SVM) classifier was applied for flood classification. The flood mapping result was validated by comparing with a reference flood map derived from an observed Landsat image. This study demonstrates that the techniques of image fusion and object-based image analysis are useful for observing flood inundation in the heterogeneous vegetated area.

1702-P: Fine-Scale Haplotype Mapping of PPM1K Gene Reveals New Genetic Markers for the Study of Insulin Resistance at Clinical Scale

Branched chain amino acids (BCAA) plasma levels represent potential biomarkers for development of type 2 diabetes (T2D). Mendelian randomization and prospective studies indicated causal relationship between SNPs in PPM1K (protein phosphatase Mg2+/Mn2+ dependent 1K) gene (Chr 4q22.1) and increased risk of incident T2D while other studies, including ours suggested the contribution of MUT (metylmalonyl-CoA mutase) gene (Chr 6p12.3) in insulin resistance (IR). To define genetic markers we performed fine-scale haplotype mapping of PPM1K in two populations (French and Romanians) with metabolic syndrome (n = 465). SNPs were genotyped with Affymetrix and enriched by imputation at 1000 genome density (BEAGLE 4.1). Haplotype association was performed by sliding window (4 SNPs windowing) and analyzed in HAPLOVIEW and PHASE 2.1. Previous leader SNPs in PPM1K (rs7678928 and rs1440581) were not associated with IR in our population but haplotype mapping revealed two major signals downstream (GGGC) and upstream (ACAG), centered by rs71609524 and rs4423843 with P &amp;lt; 3.76 x 10-3 (OR 1.59, 95% CI [1.13 - 2.23]) and &amp;lt; 4.72x10-3 (OR 1.50, 95% CI [1.12 - 2.01]), respectively. Values were weaker that those from MUT gene (P-value of the most significant haplotype, centered by rs2167284 was &amp;lt; 10-5). Total BCAA plasma levels were associated with PPM1K by several SNPs, including rs7656367 and rs4693955 with P &amp;lt; 5 x 10-3, OR of 0.41 and 3.22, respectively. Interestingly, correlation trend test with leucine, isoleucine and valine were better correlated with SNPs in the MUT gene: rs10948396 was associated with P &amp;lt; 7.91 x 10-5 (Bonferroni 6 x 10-2) and confirmed by correlation with total BCAA levels (P &amp;lt; 1.60 x 10-6, Bonferroni 1.22 x 10-3). These data suggest synergistic effects and indicate that new valuable genetic markers can be defined for clinical trials using fine-scale haplotype mapping, thus revealing complex relationship between genes in the determinism of IR and BCAA metabolism. Disclosure S. Haydar: None. C. Lautier: None. F. Grigorescu: None. Funding MEDIGENE (FP7-279171)

Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach

Spatial distribution information on population density is essential for understanding urban dynamics. In recent decades, remote sensing techniques have often been applied to assess population density, particularly night-time light data (NTL). However, such attempts have resulted in mapped population density at coarse/medium resolution, which often limits the applicability of such data for fine-scale territorial planning. The improved quality and availability of multi-source remote sensing imagery and location-based service data (LBS) (from mobile networks or social media) offers new potential for providing more accurate population information at the micro-scale level. In this paper, we developed a fine-scale population distribution mapping approach by combining the functional zones (FZ) mapped with high-resolution satellite images, NTL data, and LBS data. Considering the possible variations in the relationship between population distribution and nightlight brightness in functional zones, we tested and found spatial heterogeneity of the relationship between NTL and the population density of LBS samples. Geographically weighted regression (GWR) was thus implemented to test potential improvements to the mapping accuracy. The performance of the following four models was evaluated: only ordinary least squares regression (OLS), only GWR, OLS with functional zones (OLS&FZ) and GWR with functional zones (GWR&FZ). The results showed that NTL-based GWR&FZ was the most accurate and robust approach, with an accuracy of 0.71, while the mapped population density was at a unit of 30 m spatial resolution. The detailed population density maps developed in our approach can contribute to fine-scale urban planning, healthcare and emergency responses in many parts of the world.