Search Results (111)

Dengue disease incidence is related with the construction of a house roof, which is an Aedes mosquito habitat. This study was conducted to classify pitch roof (PR) and flat roof (FR) surfaces using pan-sharpened Worldview 2 to identify dengue disease patterns (DDPs) and their association with DDP. A Supervised Minimum Distance classifier was applied to 653 training data from image object segmentations: PR (81 polygons), FR (50), and non-roof (NR) class (522). Ground validation of 272 pixels (52 for PR, 51 for FR, and 169 for NR) was done using a global positioning system (GPS) tool. Getis-Ord score pattern analysis was applied to 1154 dengue disease incidence with address-approach-based data with weighted temporal value of 28 days within a 1194 m spatial radius. We used ordinary least squares (OLS) and geographically weighted regression (GWR) to assess spatial association. Our findings showed 70.59% overall accuracy with a 0.51 Kappa coefficient of the roof classification images. Results show that DDPs were found in hotspot, random, and dispersed patterns. Smaller PR size and larger FR size showed some association with increasing DDP into more clusters (OLS: PR value = −0.27; FR = 0.04; R² = 0.076; GWR: R² = 0.76). The associations in hotspot patterns are stronger than in other patterns (GWR: R² in hotspot = 0.39, random = 0.37, dispersed = 0.23). Full article

In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medium spatial resolution. Due to the limited ground sampling distance (GSD), hyperspectral satellite images are often insufficient for precision agricultural applications. In the presented study, simulated hyperspectral data of the upcoming Environmental Mapping and Analysis Program (EnMAP) mission (30 m GSD) covering an agricultural region were pan-sharpened with higher resolution panchromatic aisaEAGLE (airborne imaging spectrometer for applications EAGLE) (3 m GSD) and simulated Sentinel-2 images (10 m GSD) using the spectral preserving Ehlers Fusion. As fusion evaluation criteria, the spectral angle (α_spec) and the correlation coefficient (R) were calculated to determine the spectral preservation capability of the fusion results. Additionally, partial least squares regression (PLSR) models were built based on the EnMAP images, the fused datasets and the original aisaEAGLE hyperspectral data to spatially predict the LAI of two wheat fields. The aisaEAGLE model provided the best results (R²_cv = 0.87) followed by the models built with the fused datasets (EnMAP–aisaEAGLE and EnMAP–Sentinel-2 fusion each with a R²_cv of 0.75) and the simulated EnMAP data (R²_cv = 0.68). The results showed the suitability of pan-sharpened EnMAP data for a reliable spatial prediction of LAI and underlined the potential of pan-sharpening to enhance spatial resolution as required for precision agriculture applications. Full article

Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden Markov Models in crop classification, based on the time-series analysis of phenological states, inferred by a sequence of remote sensing observations. More specifically, we model the dynamics of vegetation over an agricultural area of Greece, characterized by spatio-temporal heterogeneity and small-sized fields, using RapidEye and Landsat ETM+ imagery. In addition, the classification performance of image sequences with variable spatial and temporal characteristics is evaluated and compared. The classification model considering one RapidEye and four pan-sharpened Landsat ETM+ images was found superior, resulting in a conditional kappa from 0.77 to 0.94 per class and an overall accuracy of 89.7%. The results highlight the potential of the method for operational crop mapping in Euro-Mediterranean areas and provide some hints for optimal image acquisition windows regarding major crop types in Greece. Full article

Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions. Full article

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages. Full article

Habitat mapping can be accomplished using many techniques and types of data. There are pros and cons for each technique and dataset, therefore, the goal of this project was to investigate the capabilities of new satellite sensor technology and to assess map accuracy for a variety of image classification techniques based on hundreds of field-work sites. The study area was Masonboro Island, an undeveloped area in coastal North Carolina, USA. Using the best map results, a habitat change assessment was conducted between 2002 and 2010. WorldView-2, QuickBird, and IKONOS satellite sensors were tested using unsupervised and supervised methods using a variety of spectral band combinations. Light Detection and Ranging (LiDAR) elevation and texture data pan-sharpening, and spatial filtering were also tested. In total, 200 maps were generated and results indicated that WorldView-2 was consistently more accurate than QuickBird and IKONOS. Supervised maps were more accurate than unsupervised in 80% of the maps. Pan-sharpening the images did not consistently improve map accuracy but using a majority filter generally increased map accuracy. During the relatively short eight-year period, 20% of the coastal study area changed with intertidal marsh experiencing the most change. Smaller habitat classes changed substantially as well. For example, 84% of upland scrub-shrub experienced change. These results document the dynamic nature of coastal habitats, validate the use of the relatively new Worldview-2 sensor, and may be used to guide future coastal habitat mapping. Full article

Most of the existing pan-sharpening quality assessment methods consider only the spectral quality and there are just few investigations, which concentrate on spatial characteristics. Spatial quality of pan-sharpened images is vital in elaborating the capability of object extraction, identification, or reconstruction, especially regarding man-made objects and their application for large scale mapping in urban areas. This paper presents an Edge based image Fusion Metric (EFM) for spatial quality evaluation of pan-sharpening in high resolution satellite imagery. Considering Modulation Transfer Function (MTF) as a precise measurement of edge response, MTFs of pan-sharpened images are assessed and compared to those obtained from the original multispectral or panchromatic images. Spatial quality assessment of pan-sharpening is done by comparison of MTF curves of the pan-sharpened and reference images. The capability of the proposed method is evaluated by quality assessment of two different residential and industrial urban areas of WorldView-2 pan-sharpened images. Obtained results clearly show the wide spatial discrepancy in quality of Pan-sharpened images, resulting from different fusion methods, and confirm the need for spatial quality assessment of fused products. The results also prove the capability of the proposed EFM as a powerful tool for evaluation and comparison of different image fusion techniques and products. Full article

The structure and functioning of coral reef coastal zones are currently coping with an increasing variety of threats, thereby altering the coastal spatial patterns at an accelerated pace. Understanding and predicting the evolution of these highly valuable coastal ecosystems require reliable and frequent mapping and monitoring of both inhabited terrestrial and marine areas at the individual tree and coral colony spatial scale. The very high spatial resolution (VHR) mapping that was recently spearheaded by WorldView-2 (WV2) sensor with 2 m and 0.5 m multispectral (MS) and panchromatic (Pan) bands has the potential to address this burning issue. The objective of this study was to classify nine terrestrial and twelve marine patch classes with respect to spatial resolution enhancement and coast integrity using eight bands of the WV2 sensor on a coastal zone of Moorea Island, French Polynesia. The contribution of the novel WV2 spectral bands towards classification accuracy at 2 m and 0.5 m were tested using traditional and innovative Pan-sharpening techniques. The land and water classes were examined both separately and combinedly. All spectral combinations that were built only with the novel WV2 bands systematically increased the overall classification accuracy of the standard four band classification. The overall best contribution was attributed to the coastal-red edge-near infrared (NIR) 2 combination (Kappa_gain = 0.0287), which significantly increased the fleshy and encrusting algae (User’s Accuracy_gain = 18.18%) class. However, the addition of the yellow-NIR2 combination dramatically impacted the hard coral/algae patches class (Producer’s Accuracy_loss = −20.88%). Enhancement of the spatial resolution reduced the standard classification accuracy, depending on the Pan-sharpening technique. The proposed composite method (local maximum) provided better overall results than the commonly used sensor method (systematic). However, the sensor technique produced the highest contribution to the hard coral thicket (PA_gain = 30.36%) class with the coastal-red edge-NIR2 combination. Partitioning the coast into its terrestrial and aquatic components lowered the overall standard classification accuracy, while strongly enhancing the hard coral bommie class with the coastal-NIR2 combination (UA_gain = 40%) and the green-coastal Normalized Difference Ratio (UA_gain = 11.06%). VHR spaceborne remote sensing has the potential to gain substantial innovative insights into the evolution of tropical coastal ecosystems from local to regional scales, to predict the influence of anthropogenic and climate changes and to help design optimized management and conservation frameworks. Full article

The La Grande River watershed, located in the James Bay region (54°N, Quebec, Canada), is a major contributor to the production of hydroelectricity in the province. Peatlands cover up to 20% of the terrestrial environment in this region. Their hydrological behavior is not well understood. The present study is part of a multidisciplinary project which is aimed at analyzing the hydrological processes in these minerotrophic peatlands (fens) in order to provide effective monitoring tools to water managers. The objective of this study was to use VHR remote sensing data to understand the seasonal dynamics of the hydrology in fens. A series of 10 multispectral pan-sharpened GeoEye-1 images (with a spatial resolution of 40 cm) were acquired during the snow-free season (May to October) in 2009 and 2010, centered on two study sites in the Laforge sector (54°06'N; 72°30'W). These are two fens instrumented for continuous hydrometeorological monitoring (water level, discharge, precipitation, air temperature). An object-based classification procedure was set up and applied. It consisted of segmenting the imagery into objects using the multiresolution segmentation algorithm (MRIS) to delineate internal structures in the peatlands (aquatic, semi-aquatic, and terrestrial). Then, the objects were labeled using a fuzzy logic based algorithm. The overall classification accuracy of the 10 images was assessed to be 82%. The time series of the peatland mapping demonstrated the existence of important intra-seasonal spatial dynamics in the aquatic and semi-aquatic compartments. It was revealed that the dynamics amplitude depended on the morphological features of the fens. The observed spatial dynamics was also closely related to the evolution of the measured water levels. Full article

The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images), we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France) and in Southeast Asia (Da Nang, Vietnam). The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts. Full article

The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm. Full article