scholarly journals Geostatistical modelling of the malaria risk in Mozambique: effect of the spatial resolution when using remotely-sensed imagery

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Federica Giardina ◽  
Jonas Franke ◽  
Penelope Vounatsou
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Population Data ◽  
Earth Observation ◽  
Malaria Risk ◽  
Geostatistical Model ◽  
Geostatistical Modelling ◽  
Elevation Data ◽  
Moderate Resolution

<p>The study of malaria spatial epidemiology has benefited from recent advances in geographic information system and geostatistical modelling. Significant progress in earth observation technologies has led to the development of moderate, high and very high resolution imagery. Extensive literature exists on the relationship between malaria and environmental/climatic factors in different geographical areas, but few studies have linked human malaria parasitemia survey data with remote sensing-derived land cover/land use variables and very few have used Earth Observation products. Comparison among the different resolution products to model parasitemia has not yet been investigated. In this study, we probe a proximity measure to incorporate different land cover classes and assess the effect of the spatial resolution of remotely sensed land cover and elevation on malaria risk estimation in Mozambique after adjusting for other environmental factors at a fixed spatial resolution. We used data from the Demographic and Health survey carried out in 2011, which collected malaria parasitemia data on children from 0 to 5 years old, analysing them with a Bayesian geostatistical model. We compared the risk predicted using land cover and elevation at moderate resolution with the risk obtained employing the same variables at high resolution. We used elevation data at moderate and high resolution and the land cover layer from the Moderate Resolution Imaging Spectroradiometer as well as the one produced by MALAREO, a project covering part of Mozambique during 2010-2012 that was funded by the European Union’s 7<sup>th</sup> Framework Program. Moreover, the number of infected children was predicted at different spatial resolutions using AFRIPOP population data and the <em>enhanced</em> population data generated by the MALAREO project for comparison of estimates. The Bayesian geostatistical model showed that the main determinants of malaria presence are precipitation and day temperature. However, the presence of wetlands and bare soil are also very important factors. The model validation performed on a subset of locations revealed that the use of high-resolution covariates (MALAREO land cover and elevation data) improved prediction performance.</p>

scholarly journals Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany

2020 ◽  
Vol 12 (10) ◽  
pp. 3976 ◽  
Author(s):  
Sebastian Eichhorn
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Population Distribution ◽  
Population Data ◽  
Added Value ◽  
Moran’S I ◽  
Dispersion Measure ◽  
Test Statistics ◽  
Moran's I ◽  
Urban Density

High-resolution population data are a necessary basis for identifying affected regions (e.g., natural disasters, accessibility of social infrastructures) and deriving recommendations for policy and planning, but municipalities are, as in Germany, regularly the smallest available reference unit for data. The article presents a dasymetric-based approach for modeling high-resolution population data based on urban density, dispersion, and land cover/use. In addition to common test statistics like MAE or MAPE, the Gini-coefficient and the local Moran’s I are applied and their added value for accuracy assessment is tested. With data on urban density, a relative deviation between the modeled and actual population of 14.1% is achieved. Data on land cover/use reduces the deviation to 12.4%. With 23.6%, the dispersion measure cannot improve distribution accuracy. Overall, the algorithms perform better for urban than for rural areas. Gini-coefficients show that same spatial concentration patterns are achieved as in the actual population distribution. According to local Moran’s I, there are statistically significant underestimations, especially in the highly-dense inner-urban areas. Overestimates are found in the transition to less urbanized areas and the core areas of peripheral cities. Overall, the additional test statistics can provide important insights into the data, which go beyond common methods for evaluation.

scholarly journals High-resolution inventory to capture glacier disintegration in the Austrian Silvretta

2021 ◽  
Author(s):  
Andrea Fischer ◽  
Bernd Seiser ◽  
Kay Helfricht ◽  
Martin Stocker-Waldhuber
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Ice Age ◽  
Surface Elevation ◽  
Glacier Changes ◽  
Wide Range ◽  
Elevation Data ◽  
Glacier Ice ◽  
Geodetic Mass Balance

Abstract. Eastern Alpine glaciers have been receding since the LIA maximum, but the majority of glacier margins could be delineated unambiguously for the last Austrian glacier inventories. Even debris-covered termini, changes in slope, colour or the position of englacial streams enabled at least an in situ survey of glacier outlines. Today the outlines of totally debris-covered glacier ice are fuzzy and raise the theoretical discussion if these glaciogenic features are still glaciers and should be part of the respective inventory – or part of an inventory of transient cryogenic landforms. A new high-resolution glacier inventory (area and surface elevation) was compiled for the years 2017 and 2018 to quantify glacier changes for the Austrian Silvretta region in full. Glacier outlines were mapped manually, based on orthophotos and elevation models and patterns of volume change of 1 to 0.5 m spatial resolution. The vertical accuracy of the DEMs generated from 6 to 8 LiDAR points per m2 is in the order of centimetres. calculated in relation to the previous inventories dating from 2004/2006 (LiDAR), 2002, 1969 (photogrammetry) and to the Little Ice Age maximum extent (moraines). Between 2004/06 and 2017/2018, the 46 glaciers of the Austrian Silvretta lost −29 ± 4 % of their area and now cover 13.1 ± 0.4 km2. This is only 32 ± 2 % of their LIA extent of 40.9 ± 4.1 km2. The area change rate increased from −0.6 %/year (1969–2002) to −2.4 %/year (2004/06–2017/18). The annual geodetic mass balance showed a loss increasing from −0.2 ± 0.1 m w.e./year (1969–2002) to –0.8 m ±0.1 w.e./year (2004/06–2017/18) with an interim peak in 2002–2004/06 at −1.5 ± 0.7 m w.e./year. Identifying the glacier outlines offers a wide range of possible interpretations of former glaciers that have evolved into small and now totally debris-covered cryogenic geomorphological structures. Only the patterns and amounts of volume changes allow us to estimate the area of the buried glacier remnants. To keep track of the buried ice and its fate, and to distinguish increasing debris cover from ice loss, we recommend inventory repeat frequencies of three to five years and surface elevation data with a spatial resolution of one metre.

scholarly journals REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 175-182
Author(s):  
G. Bratic ◽  
A. Vavassori ◽  
M. A. Brovelli
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Accuracy Assessment ◽  
General Information ◽  
Tree Canopy ◽  
Global Land Cover ◽  
Tree Canopy Cover ◽  
Global Land ◽  
Land Cover Maps

Abstract. The land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions.

scholarly journals Evaluating the Spatial Representativeness of the MODerate Resolution Image Spectroradiometer Albedo Product (MCD43) at AmeriFlux Sites

2019 ◽  
Vol 11 (5) ◽  
pp. 547 ◽  
Author(s):  
Hongmin Zhou ◽  
Shunlin Liang ◽  
Tao He ◽  
Jindi Wang ◽  
Yanchen Bo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Spatial Scale ◽  
Surface Heterogeneity ◽  
Mixed Forests ◽  
Land Cover Types ◽  
Resolution Image ◽  
Moderate Resolution ◽  
Moderate Resolution Image Spectroradiometer

Land surface albedo is a key parameter in regulating surface radiation budgets. The gridded remote sensing albedo product often represents information concerning an area larger than the nominal spatial resolution because of the large viewing angles of the observations. It is essential to quantify the spatial representativeness of remote sensing products to better guide the sampling strategy in field experiments and match products from different sources. This study quantifies the spatial representativeness of the MODerate Resolution Image Spectroradiometer (MODIS) (collection V006) 500 m daily albedo product (MCD43A3) using the high-resolution product as intermediate data for different land cover types. A total of 1820 paired high-resolution Landsat Thematic Mapper (TM) and coarse-resolution (MODIS) albedo data from five land cover types were used. The TM albedo data was used as the spatial-complete high resolution data to evaluate the spatial representativeness of the MODIS albedo product. Semivarioagrams were estimated from 30 m Landsat data at different spatial scales. Surface heterogeneity was evaluated with sill value and relative coefficient of variation. The 30 m Landsat albedo data was aggregated to 450 m–1800 m using two different methods and compared with MODIS albedo product. The spatial representativeness of MODIS albedo product was determined according to the surface heterogeneity and the consistency of MODIS data and the aggregated TM value. Results indicated that for evergreen broadleaf forests, deciduous broadleaf forests, open shrub lands, woody savannas and grasslands, the MODIS 500 m daily albedo product represents a spatial scale of approximately 630 m. For mixed forests and croplands, the representative spatial scale was approximately 690 m. The difference obtained was primarily because of the complexity of the landscape structure. For mixed forests and croplands, the structure of the landscape was relatively complex due to the presence of different forest and plant types in the pixel area, whereas the other landscape structures were considerably simpler.

scholarly journals Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 230
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Multiple Scales ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Hybrid Network ◽  
High Resolution Satellite Images

Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin.

scholarly journals LUCAS Copernicus 2018: Earth-observation-relevant in situ data on land cover and use throughout the European Union

2021 ◽  
Vol 13 (3) ◽  
pp. 1119-1133
Author(s):  
Raphaël d'Andrimont ◽  
Astrid Verhegghen ◽  
Michele Meroni ◽  
Guido Lemoine ◽  
Peter Strobl ◽  
...  
Keyword(s):  
Land Use ◽  
European Union ◽  
High Resolution ◽  
Land Cover ◽  
Earth Observation ◽  
The European Union ◽  
In Situ Data ◽  
Cardinal Directions ◽  
High Resolution Satellite Imagery

Abstract. The Land Use/Cover Area frame Survey (LUCAS) is an evenly spaced in situ land cover and land use ground survey exercise that extends over the whole of the European Union. LUCAS was carried out in 2006, 2009, 2012, 2015, and 2018. A new LUCAS module specifically tailored to Earth observation (EO) was introduced in 2018: the LUCAS Copernicus module. The module surveys the land cover extent up to 51 m in four cardinal directions around a point of observation, offering in situ data compatible with the spatial resolution of high-resolution sensors. However, the use of the Copernicus module being marginal, the goal of the paper is to facilitate its uptake by the EO community. First, the paper summarizes the LUCAS Copernicus protocol to collect homogeneous land cover on a surface area of up to 0.52 ha. Secondly, it proposes a methodology to create a ready-to-use dataset for Earth observation land cover and land use applications with high-resolution satellite imagery. As a result, a total of 63 364 LUCAS points distributed over 26 level-2 land cover classes were surveyed on the ground. Using homogeneous extent information in the four cardinal directions, a polygon was delineated for each of these points. Through geospatial analysis and by semantically linking the LUCAS core and Copernicus module land cover observations, 58 426 polygons are provided with level-3 land cover (66 specific classes including crop type) and land use (38 classes) information as inherited from the LUCAS core observation. The open-access dataset supplied with this paper (https://doi.org/10.6084/m9.figshare.12382667.v4 d'Andrimont, 2020) provides a unique opportunity to train and validate decametric sensor-based products such as those obtained from the Copernicus Sentinel-1 and Sentinel-2 satellites. A follow-up of the LUCAS Copernicus module is already planned for 2022. In 2022, a simplified version of the LUCAS Copernicus module will be carried out on 150 000 LUCAS points for which in situ surveying is planned. This guarantees a continuity in the effort to find synergies between statistical in situ surveying and the need to collect in situ data relevant for Earth observation in the European Union.

Delineating fragmented grassland patches in the tropical region using multi-seasonal SAR and Optical Satellite Images

2020 ◽  
Author(s):  
Abhishek Samrat ◽  
M. S. Devy ◽  
Ganesh
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
High Resolution ◽  
Land Cover ◽  
South Asia ◽  
Spatial Resolution ◽  
Post Monsoon ◽  
Habitat Matrix ◽  
Semi Arid ◽  
Sentinel 2

Globally grasslands are declining and are in highly degraded conditions. In south Asia grasslands are neglected and treated as wastelands. They remain unprotected, highly fragmented, and poorly understood which has led to a loss of unique biodiversity and livelihoods. Mapping grasslands accurately is a challenge and current maps based on optical remote sensing often over- or underestimate grasslands in south Asia due to a prevalant complex landscape matrix, small patch sizes, and obscuring monsoonal clouds. Synthetic Aperture Radar (SAR) fused with moderate spatial resolution has been used to delineate grasslands but, high-resolution, freely available ESA’s sentinel-1(SAR) and -2(optical) provides an opportunity to map small and fragmented patches that were not possible earlier with the publically available moderate or medium spatial resolution remote sensing dataset. Further, high resolution imageries require high computing power which is often limited with stand alone machines. Here we demonstrate that using cloud computing and optimal use of multi-seasonal imagery one can obtain a highly accurate land cover/use classification for a complex habitat matrix. We used freely accessible cloud computing platforms like Google Earth Engine (GEE) and land cover/use classification of sentinel-1 and -2. We compared the accuracy of grassland delineation between 1) seasonal (pre, during, and post-monsoon) sentinel-1, 2) post-monsoon sentinel-2, and 3) combined sentinel-1 and -2. We tested this method at two sites in a highly fragmented habitat matrix in semi-arid areas of Western and Southern India. The classification result has shown the overall accuracy of for the combined image was higher than only sentinel-2 and sentinel-1 alone for both sites. Grasslands habitat accuracy was also consistent with combined image classification across the sites. Our results identified newer grassland areas that coarse landuse management maps used by the government did not. The computation was done on a basic laptop and processing completed very quick. We, therefore, suggest that this novel approach of using cloud computing and optimal use of resource-hungry (computation and storage) high-resolution ESA’s sentinel-1 and -2 data, can be used to identify major land classes and small patchy grassland in the semi-arid regions of Asia and has the potential to map at continent level.

scholarly journals Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

2020 ◽  
Vol 12 (24) ◽  
pp. 4158
Author(s):  
Mengmeng Li ◽  
Alfred Stein
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Arrangement ◽  
Remote Sensing Images ◽  
Land Use Types ◽  
High Resolution Satellite Images

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.

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