Resolution Multispectral Data Research Articles

Advances in Classification Techniques for Semi Urban Land Features using High Resolution Satellite Data

Classification of satellite Images is one of the major research areas in remote sensing fields. Classification of remote sensed data is required for accurate classification of semi urban land features. Satellite image classification plays an essential role in proper monitoring and management of natural and manmade resources on the earth surface. However a good data set is required for the accurate classification of remotely sensed data. In this paper, to classify the data set, various image fusion techniques are used for fusing high resolution Panchromatic data with low resolution Multi-spectral data which gives better quality and more informative image data set. The performances of different fusion techniques are then evaluated to identify the best possible technique which gives better result for image classification.

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum.

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

Efficient crop protection management requires timely detection of diseases. The rapid development of remote sensing technology provides a possibility of spatial continuous monitoring of crop diseases over a large area. In this study, to monitor powdery mildew in winter wheat in an area where a severe disease infection occurred, the capability of high resolution (6 m) multi-spectral satellite imagery, SPOT-6, in disease mapping was assessed and validated using field survey data. Based on a rigorous feature selection process, five disease sensitive spectral features: green band, red band, normalized difference vegetation index, triangular vegetation index, and atmospherically-resistant vegetation index were selected from a group of candidate spectral features/variables. A spectral correction was processed on the selected features to eliminate possible baseline effect across different regions. Then, the disease mapping method was developed based on a spectral angle mapping technique. By validating against a set of field survey data, an overall mapping accuracy of 78 % and kappa coefficient of 0.55 were achieved. Such a moderate but practically acceptable accuracy suggests that the high resolution multi-spectral satellite image data would be of great potential in crop disease monitoring.

Constructing Hierarchical Segmentation Tree for Feature Extraction and Land Cover Classification of High Resolution MS Imagery

Accurate interpretation of high spatial resolution multispectral (MS) imagery relies on the extraction and fusion of information obtained from both spectral and spatial domains. Feature extraction from one or several fixed windows uses inaccurate description of pixel contexts and produces blurred object boundaries and low classification accuracy. In order to accurately characterize the spatial context properties of pixels, this paper presents a hierarchical-segmentation-based classification system. The system consists of two main modules: 1) hierarchical segmentation and 2) context-based classification. The segmentation module involves an optimization procedure to prevent undersegmentation of the land objects of interest and a scale selection procedure to find the most representative segmentation layers for modeling pixel contexts. The classification module couples a context-driven multilevel feature extraction methodology with a support vector machine classifier to get classification result. The proposed system is validated on three high spatial resolution MS data sets. Compared with state-of-the-art classification methods based on the similar concept, the proposed method demonstrates superior performance on both the classification accuracy and the quality of classification maps.

Wavelet-Based Texture Features for the Classification of Age Classes in a Maritime Pine Forest

This letter evaluates the potential of wavelet-based texture modeling for the classification of stand age in a managed maritime pine forest using very high resolution panchromatic and multispectral PLEIADES data. A cross-validation approach based on stand age reference data is used to compare classification performances obtained from different multivariate models (multivariate Gaussian, spherically invariant random vector (SIRV)-based models, and Gaussian copulas) and from co-occurrence matrices. Results show that the multivariate modeling of the spatial dependence of wavelet coefficients (particularly when using the Gaussian SIRV model) outperforms the use of features derived from co-occurrence matrices. Simultaneously adding features representing the color dependence and leveling the dominant orientation in anisotropic forest stands enhances the classification performances. These results confirm the ability of such wavelet-based multivariate models to efficiently capture the textural properties of very high resolution forest data and open up perspectives for their use in the mapping of monospecific forest structure variables.

Assessment of urbanization encroachment over Al-Monib island using fuzzy post classification comparison and urbanization metrics

Nile River has about 144 islands from Aswan to the Mediterranean Sea. In this research remotely sensed images have been used for the assessment of land cover changes in the Al-Monib island as part of an ongoing sustainable development of this island. The island has witnessed high rates of change in land use in the past few years. An urbanization process continues and it causes serious increases in urban areas while decreasing the amount of green areas.The most common use of many of the change detection algorithms has been to identify the change in coarse to medium spatial resolution satellite imagery. Now there is great interest in identifying the change in high spatial resolution multispectral data such as SPOT5 and QuickBird. In order to improve the quality and accuracy, different cues have been extracted such as IHS or PCA and texture derived from color image. Fuzzy classification has been performed several times utilizing from Multi-Cue integration (resulted into six classifications) for each date. Assessment of different approaches of classification (six classifications) has been performed for each date. After that fuzzy post classification comparison has been made for the best case. Value of the urban expansions for the period of 2002–2009 was calculated as 0.11km2. The urban expansion rate had been realized as 3.04%. Another significant change was the decline in agricultural lands result was estimated to be 8.29%.The changes of landscape pattern were then analyzed using a series of spatial metrics (class level) which were derived from FRAGSTATS software.

Crop type identification by integration of high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data

Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (MLC) was used to classify the TM and merged images. Independent validation results indicated that the average overall classification accuracy was improved from 92.38% using TM to 94.67% using the merged images. Based on the classification results using the proposed method, acreages of two major summer crops were estimated and compared to statistical data provided by the United States Department of Agriculture (USDA). The proposed method was able to achieve highly satisfactory crop type identification results.

Scale Object Selection (SOS) through a hierarchical segmentation by a multi-spectral per-pixel classification

In high resolution multispectral optical data, the spatial detail of the images are generally smaller than the dimensions of objects, and often the spectral signature of pixels is not directly representative of classes we are interested in. Thus, taking into account the relations between groups of pixels becomes increasingly important, making object-oriented approaches preferable. In this work several scales of detail within an image are considered through a hierarchical segmentation approach, while the spectral information content of each pixel is accounted by a per-pixel classification. The selection of the most suitable spatial scale for each class is obtained by merging the hierarchical segmentation and the per-pixel classification through the Scale Object Selection (SOS) algorithm. The SOS algorithm starts processing data from the highest level of the hierarchical segmentation, which has the least amount of spatial detail, down to the last segmentation map. At each segmentation level, objects are assigned to a specific class whenever the percentage of pixels belonging to the latter, according to a pixel-based procedure, exceeds a predefined threshold, thereby automatically selecting the most appropriate spatial scale for the classification of each object. We apply our method to multispectral, panchromatic and pan-sharpened QuickBird images, considering two different test cases: a region on the Etna volcano (Italy), imaged at a 2.4m resolution, and an area close to the town of Balakot (Pakistan), imaged at a 0.6m resolution. On both test-cases the proposed approach enhanced the classification accuracy with respect to the single-segmentation per-pixel classification approach. A detailed analysis of the benefits achieved using the hierarchical segmentation with respect to a single segmentation is reported.

Accuracy Assessment of Land Cover Maps of Forests within an Urban and Rural Environment

Land cover maps of forests within an urban and rural environment derived from high spatial resolution multispectral data (QuickBird) and medium spatial resolution multispectral data (Landsat ETM+ and SPOT 4) were compared to ascertain whether increased spatial resolution increases map accuracy of forests and whether map accuracy varies across land cover classification schemes. It is commonly assumed that increased spatial resolution would probably increase land cover map accuracy regardless of land cover classification methodology. This study assessed whether that assumption is correct within a rural and an urban environment. Map accuracy for modified National Land Cover Data (NLCD) 2001 Level II, Level I, and Unique (a modified NLCD 2001 Level II and Level I combination) shows that 30-m Landsat ETM+ data had the highest overall map accuracy for rural, urban, and combined rural/urban land cover maps. Analysis of user's and producer's accuracies shows that Landsat ETM+ data had higher levels of producer's accuracy of >90.0% for the coniferous cover type for modified NLCD 2001 Level II and Unique, excluding one instance for which SPOT 4 had a user's accuracy of 98.5% for the rural coniferous cover type. Modified NLCD 2001 Level I Landsat ETM+ data had user's and producer's accuracies for a homogeneous forest cover type of 98.4 and 90.6%, respectively. Landsat ETM+ data also outperformed SPOT 4 and QuickBird within an urban environment, creating the only map products with forest cover type user's and producer's accuracies of >90.0%.

Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

Accidental release of crude oil into the sea due to human activity causes water pollution and heavy damages to natural ecosystems killing birds, fish, mammals and other organisms. A number of monitoring systems are used for tracking the spills and their effects on the marine environment, as well as for collecting data for feeding models. Among them, Earth observation technologies play a crucial role and moderate spatial resolution satellite systems are able to collect images with a very short revisit time or even daily. This paper describes the use of Moderate-Resolution Imaging Spectroradiometer data for monitoring large oil slicks with the fluorescence/emissivity index and object-based image analysis. Two case studies are presented: the Deepwater Horizon (2010) and the Campos Basin (2011) oil spill accidents. Results show that it is possible to track the dynamics of the slick both for massive and long-lasting accidents and for smaller and very quick accidents. The main advantages of the method proposed are a straightforward implementation, a fast and semi-automated data processing and the capability of integration of daytime and nighttime acquisitions, as well as its adaptability to different sensors.

A Batch-Mode Active Learning Algorithm Using Region-Partitioning Diversity for SVM Classifier

In this paper, a region-partitioning active learning (AL) technique is proposed for classification of remote sensing (RS) images based on the support vector machines (SVM) classifier. In the batch-mode AL process, diversity information is required to select a batch of informative samples. A new AL technique that aims to introduce diversity information is proposed based on relative positions of candidate samples in the feature space. The proposed technique selects informative samples according to an uncertainty criterion at each iteration. These samples are selected with an extra constraint to guarantee that they are not located in the same region of the feature space. The proposed technique is compared with state-of-the-art methods adopted in the RS community. Experimental tests were performed on three data sets, including one very high spatial resolution multispectral data set and two hyperspectral data sets. The proposed algorithm displays a classification performance that is similar to or even better than the state-of-the-art methods. In addition, the proposed algorithm performs efficiently in terms of computational time.

Validating the geometric accuracy of high spatial resolution multispectral satellite data

Uses of high spatial resolution data obtained from satellite-based sensors include creating land cover maps, deriving large-scale quantitative assessments such as vegetation indices, and visually assessing an area for qualitative information only assessable from large-scale digital data. One of the more popular uses of high spatial resolution data is to use the image as a base map for on-screen digitizing spatially dependent vector products. Since most geographic information system (GIS) databases store a variety of current and historical data, the accuracy of any on-screen digitized product is dependent on the spatial accuracy of the reference data. Therefore, it is important to understand and validate the accuracy of data used to create spatially referenced product, even though the data come with high spatial resolution. One of the more popular and historical high spatial resolution data within most GIS labs is QuickBird's multispectral data at 2.44 × 2.44 m2. Although there are current sensors available with a higher spatial resolution, the sometimes prohibitive expense of obtaining high spatial resolution data necessitates the need to utilize and assess historic data. Since the QuickBird has been the mainstay of high spatial resolution data since 2001, understanding the geometric accuracy of the DigitalGlobe's QuickBird user-defined panchromatic and multispectral image bundle product remains relevant. In this study, we assessed the positional accuracy of this product for its utility as an “off the shelf” base map for creating other spatially referenced products. The average Euclidean distance, RMSE (root mean square error), and RSME (root square mean error) between QuickBird-identified Universal Transverse Mercator (UTM) coordinates and coincident in situ GPS-collected UTM coordinates were calculated at 33 systematically selected locations throughout the city of Nacogdoches, Texas, USA. The average Euclidean distance, RMSE, and RSME between QuickBird's projected UTM coordinates and its corresponding GPS-collected UTM coordinates measured at 5.34 meters, 5.79 meters, and 4.05 meters, respectively. They were well within DigitalGlobe's stated RMSE positional accuracy of 14.0 meters for a panchromatic and multispectral QuickBird image bundle.

A method for the integration of satellite vegetation activities observations and magnetic susceptibility measurements for monitoring heavy metals in soil

We present a procedure for monitoring heavy metals in soil based on the integration of satellite and ground-based techniques, tested in an area affected by high anthropogenic pressure. High resolution multispectral satellite data were elaborated to obtain information on vegetation status. Magnetic susceptibility measurements of soils were collected as proxy variable for monitoring heavy metal presence. Chemical analyses of heavy metals were used for supporting and validating the integrated monitoring procedure. Magnetic and chemical measurements were organized in a GIS environment to be overlapped to satellite-based elaborations and to analyze the pattern distribution. Results show the presence of correlation between anomalies in vegetation activity and soil characteristics. The relationship between the distribution of normalized difference vegetation index anomalies and magnetic susceptibility values provides hints for adopting the integrated procedure as preliminary screening to minimize monitoring efforts and costs by supporting the planning activities of field campaigns.

Coal fires revisited: The Wuda coal field in the aftermath of extensive coal fire research and accelerating extinguishing activities

The Wuda coal mining area in Inner Mongolia Autonomous Region, China, is one of the largest coal fire areas world-wide. At the same time it is also one of the most extensively researched ones. The sub-surface coal fires raging in the underground coal seams of the syncline are located directly west of the town of Wuda. The fires have been mapped in situ, based on remote sensing data, as well as based on geo-physical survey all throughout the years 2001 to 2005. Numerous publications depict the situation during that time. In the present study we assess the current situation in Wuda coal field five years after the last field campaigns, under nowadays on-going and accelerating extinguishing activities. We present the results of coal fire dynamics assessment based on in situ mappings between 2004 and 2010, the analyses of changes in mining infrastructure between 2005 and 2012, as well as land cover change in the area from 2005 to 2012. Newest high resolution multispectral and panchromatic Quickbird data, as well as time series of Quickbird data supported this endeavor. We furthermore present the result of semi-structured interviews undertaken with members of the local mine directorate of the Wuda syncline. All jointly analyzed data indicate that the Wuda coal fires tend to move eastwards within the syncline, meaning that they move along the dip of the coal seams to greater depths. Furthermore, mining related infrastructure is clearly moving from the West to the East as well. Despite large funds spent on fire extinguishing, only few fires remain permanently extinct, while other fire areas increase in size. The former land cover structure in the arid syncline does not exist anymore — extinguishing activities have led to a complete ‘turn-over’ of all material in the region. The coal fire problem in Wuda area remains a pressing one. ► Re-assessment of coal fires in Wuda coal mining syncline after extinguishing started. ► From 2005 to 2012 coal fires decreased in number, but overall coal fire area doubled. ► Coal fires ‘move’ to the East, along the dipping direction of the seams. ► Coal fire related infrastructure has decreased due to closure of private mines. ► Mass movements for extinction / exploitation lead to a complete change of land cover.

Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.

THE APPLICATION OF HIGH RESOLUTION SATELLITE REMOTELY SENSED DATA TO LANDUSE DYNAMIC MONITORING

In this study, the authors chose Daxing County of Beijing as the experimental field in view of its varied landuse styles. Based on analyzing the data-acquisition ways, covering periods, operation conditions, costs, techniques and equipment conditions of some high resolution satellites such as Indian IRS, Korean KOMPSAT-1 and American IKONOS, the authors conducted researches on the feasibility of performing dynamic monitoring of landuse with remotely-sensed data, determined some important technical parameters in high resolution data, analyzed the range of gray level and characteristics of histogram for principal landuse styles, and summarized the spectral characteristics of different types of landuse and land cover. In addition, the spectral characteristics of merged images were analysed, the largest scale of mapping was determined, the monitoring results of different data sources such as IRS and TM, KOMPSAT-1 and TM as well as SPOT and TM were compared, the recognition capacities of different combinations of high resolution data and multi-spectral data were differentiated, the interpretation signatures of land usage dynamic monitoring were established, and some crucial technical problems were solved. A set of operational and practical technical methods and producing procedures have been set up, which serve as great technical supports to national land resources investigation.

Hybrid Bayesian Classifier for Improved Classification Accuracy

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.

Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment

This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.

Mangrove Classification in Sundarban using High Resolution Multi-Spectral Remote Sensing Data and GIS

Mangrove is one of the most magnificent ecosystems of the coastal regions from ecological as well as biodiversity point of view. Mangrove pose real challenge to mapping due to their inaccessibility for field survey in most of the places. Multispectral optical remote sensing offers a tangible solution to this problem. In this study, the remote sensing and GIS were used to delineate and map the mangrove distribution at species level. As distribution of different mangrove species depends on the physico chemical conditions of the substrate, especially the soil salinity, the distribution of threeAvicennis species could be possible using soil salinity gradient. From the present observations, it is revealed that most of the species occupy a zone to which it is best adapted; however, there are overlapping occurrences of different species with varying ecological optima along salinity gradients.

Spatial distribution of soil hydro-physical properties and morphometric analysis of a rainfed watershed as a tool for sustainable land use planning

In this study, high resolution (5.8 m) multi-spectral satellite data (IRS-P6, LISS IV) along with extensive ground information were used for sustainable land use planning of a rainfed watershed in eastern India (Bahasuni watershed, Dhenkanal, Orissa). Pedo-transfer functions were developed to predict soil water constants using easily measured parameters such as cation exchange capacity, organic carbon, oven-dry bulk density, soil texture, calcium carbonate etc. Different morphometric parameters like drainage density, stream frequency, form factor, circulatory ratio, elongation ratio, bifurcation ratio, relief ratio, relative relief, etc., were analyzed in relation to the development of water resources in the watershed. A circulatory ratio of 0.56 indicated that the shape of the basin was fairly elongated and suggested that the area was not prone to flood. The elongated shape, together with a moderate bifurcation ratio (3.01), indicates moderate flow, moderate runoff and moderate sediment yield. A low drainage density (2.7 km/km 2) furthermore implies the presence of permeable strata with high groundwater prospects and supports the assumption of moderate runoff and sediment yield. After studying potentials and prospects of soil hydro-physical properties and morphometric parameters, a suggested land use alternative was implemented in representative, rainfed rice areas of the watershed and was found to be more productive and more profitable.