High Resolution Multispectral Satellite Images Research Articles

Ground Reflectance Retrieval on Horizontal and Inclined Terrains Using the Software Package REFLECT

This paper presents the software package REFLECT for the retrieval of ground reflectance from high and very-high resolution multispectral satellite images. The computation of atmospheric parameters is based on the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) routines. Aerosol optical properties are calculated using the OPAC (Optical Properties of Aerosols and Clouds) model, while aerosol optical depth is estimated using the dark target method. A new approach is proposed for adjacency effect correction. Topographic effects were also taken into account, and a new model was developed for forest canopies. Validation has shown that ground reflectance estimation with REFLECT is performed with an accuracy of approximately ±0.01 in reflectance units (for the visible, near-infrared, and mid-infrared spectral bands), even for surfaces with varying topography. The validation of the software was performed through many tests. These tests involve the correction of the effects that are associated with sensor calibration, irradiance, and viewing conditions, atmospheric conditions (aerosol optical depth AOD and water vapour), adjacency, and topographic conditions.

Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty

AbstractAimsThe mapping and monitoring of natural vegetation is a challenging but important objective for environmental management. Although remote sensing has been used to map plant communities for several years, the maps produced are not sufficiently accurate to meet management requirements. This can be explained by the cumulative effects of floristic and spectral uncertainty. The objective of this study was to accurately map grassland plant communities using a comprehensive fuzzy approach in order to address floristic and spectral uncertainty.LocationSub‐brackish wet grasslands, Marais Poitevin, France.MethodsWe first created a compromise typology—floristically and spectrally consistent—to perform fuzzy noise clustering on a joint PCA matrix derived from vegetation relevés and remote sensing data. This typology had two levels, which corresponded to spectral signatures and plant communities, respectively. Second, we mapped grassland plant communities to predict the fuzzy model from the remote sensing data. We applied this approach using (1) a very high spatial resolution multispectral satellite image and a LiDAR‐derived Digital Terrain Model acquired on a 73 km2 wet grassland site and (2) more than 200 relevés collected in the field.ResultsThe results show that (1) the compromise typology yields significantly higher mapping accuracy than classic phytosociological typology (62% and 26%, respectively); (2) compared to a crisp approach, the fuzzy approach improves mapping accuracy by 17 percentage points and (3) a single plant community can be defined by several (1–4) distinct spectral signatures.ConclusionsThe comprehensive fuzzy procedure successfully mapped herbaceous plant communities at the ecosystem scale using inexpensive remote sensing data. Floristic and spectral uncertainty was considered in a fuzzy approach, resulting in the mapping of nine herbaceous plant communities with acceptable accuracy. As the natural habitats were characterized at the plant community level, correspondence with functional properties of the species or with ecosystem services can be easily inferred. These encouraging results open up new ways to meet the requirements for monitoring the conservation status of natural habitats in the EU Habitats Directive.

Mapping wheat rust based on high spatial resolution satellite imagery

Timely and accurate disease incidence monitoring and estimation using satellite imagery is critical for the effective control of wheat rust disease. Though studies have been conducted using different spectral and sensing technologies to detect and monitor the wheat rust disease, few studies have been conducted in the application of high resolution multi-spectral satellite imagery in the monitoring of wheat rust to facilitate an operational monitoring over large areas. Since there are not many options for the multispectral data than the hyperspectral data, it is more important to select appropriate vegetation indices for the classification model. However, there have been few literatures about the comparison of these feature selection methods on the application of wheat disease. We proposed a wheat rust disease mapping protocol including removing the non-vegetated and lodged area, calculating and filtering the vegetation indices and mapping the wheat rust. In the process of filtering the proper features, we applied wrapper feature selection instead of traditional filter feature selection combined with the classification methods (support vector machine and random forests). The experiment data was a scene of ZY-3 satellite image of the wheat field in Changge County in Henan Province with a certain portion of rust disease. The classification results can achieve overall accuracy of higher than 90%, ranging from 90.80% to 95.10%. The wrapper feature selection method with the overall accuracy of 93.60% is better than filters feature selection method with the overall accuracy of 92.65%. The random forests method with the overall accuracy of 94.80% is better than support vector machine method with the overall accuracy of 91.45%. The high accuracies thus justified the feasibility of using high-resolution multi-spectral satellite images for mapping wheat rust disease, which is promising for this technology to be applied in the practical wheat production management.

Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data

Abstract. Arctic tundra ecosystems will play a key role in future climate change due to intensifying permafrost thawing, plant growth and ecosystem carbon exchange, but monitoring these changes may be challenging due to the heterogeneity of Arctic landscapes. We examined spatial variation and linkages of soil and plant attributes in a site of Siberian Arctic tundra in Tiksi, northeast Russia, and evaluated possibilities to capture this variation by remote sensing for the benefit of carbon exchange measurements and landscape extrapolation. We distinguished nine land cover types (LCTs) and to characterize them, sampled 92 study plots for plant and soil attributes in 2014. Moreover, to test if variation in plant and soil attributes can be detected using remote sensing, we produced a normalized difference vegetation index (NDVI) and topographical parameters for each study plot using three very high spatial resolution multispectral satellite images. We found that soils ranged from mineral soils in bare soil and lichen tundra LCTs to soils of high percentage of organic matter (OM) in graminoid tundra, bog, dry fen and wet fen. OM content of the top soil was on average 14 g dm−3 in bare soil and lichen tundra and 89 g dm−3 in other LCTs. Total moss biomass varied from 0 to 820 g m−2, total vascular shoot mass from 7 to 112 g m−2 and vascular leaf area index (LAI) from 0.04 to 0.95 among LCTs. In late summer, soil temperatures at 15 cm depth were on average 14 ∘C in bare soil and lichen tundra, and varied from 5 to 9 ∘C in other LCTs. On average, depth of the biologically active, unfrozen soil layer doubled from early July to mid-August. When contrasted across study plots, moss biomass was positively associated with soil OM % and OM content and negatively associated with soil temperature, explaining 14–34 % of variation. Vascular shoot mass and LAI were also positively associated with soil OM content, and LAI with active layer depth, but only explained 6–15 % of variation. NDVI captured variation in vascular LAI better than in moss biomass, but while this difference was significant with late season NDVI, it was minimal with early season NDVI. For this reason, soil attributes associated with moss mass were better captured by early season NDVI. Topographic attributes were related to LAI and many soil attributes, but not to moss biomass and could not increase the amount of spatial variation explained in plant and soil attributes above that achieved by NDVI. The LCT map we produced had low to moderate uncertainty in predictions for plant and soil properties except for moss biomass and bare soil and lichen tundra LCTs. Our results illustrate a typical tundra ecosystem with great fine-scale spatial variation in both plant and soil attributes. Mosses dominate plant biomass and control many soil attributes, including OM % and temperature, but variation in moss biomass is difficult to capture by remote sensing reflectance, topography or a LCT map. Despite the general accuracy of landscape level predictions in our LCT approach, this indicates challenges in the spatial extrapolation of some of those vegetation and soil attributes that are relevant for the regional ecosystem and global climate models.

A Spectral Structural Approach for Building Extraction from Satellite Imageries

Automatic feature extraction from high resolution satellite imagery remains an open research area in the field of remote sensing, computer vision and machine learning. While many algorithms have been proposed for automatic building extraction, none of them solve the problem completely. This paper proposes a system for increasing the degree of automation in extraction of building features from high resolution multispectral satellite images. Image segmentation is a prerequisite for processing of very high spatial resolution imageries. Most image segmentation methods use spectral information of an image alone for generating image objects. A novel image segmentation method for very high spatial resolution multispectral images using combined spectral and structural information is proposed in this paper. The method involves computation of textural parameters from high resolution multispectral imagery and is combined with the spectral bands for extracting spectral-structural characteristics. Hence in addition to the spectral information, the tone, texture and shape information is evaluated for an object-oriented analysis. The support vector machines classification rules are applied on the generated object primitives. The proposed image segmentation method is well applicable to the segmentation of imagery over urban and suburban areas for large scale building extraction.

Terrestrial CDOM in Lakes of Yamal Peninsula: Connection to Lake and Lake Catchment Properties

In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440)CDOM) and absorption slope (S300–500) in lakes using field sampling and optical remote sensing data for an area of 350 km2 in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a(λ)CDOM data from 18 lakes sampled in the field to 356 lakes in the study area (model R2 = 0.79). Values of a(440)CDOM in 356 lakes varied from 0.48 to 8.35 m−1 with a median of 1.43 m−1. This a(λ)CDOM dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440)CDOM = 5.3 m−1). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440)CDOM = 3.8 m−1) compared to lakes located on higher terraces.

Object based building footprint detection from high resolution multispectral satellite image using K-means clustering algorithm and shape parameters

Object-based image analysis (OBIA) has been a new area of research in satellite image processing applications, since it improves the quality of information acquisition about geospatial objects and also enables to add spatial and contextual information to the objects of interest. The extraction of buildings from High Resolution Satellite (HRS) image in an urban scenario has been an intricate problem due to their different size, shape, varying rooftop textures and low contrast between building and surrounding region. In this study, a new object-based automatic building extraction technique has been proposed to extract building footprints from HRS pan sharpened IKONOS multispectral image. The study is mainly emphasizing on obtaining optimal values for segmentation parameters, shape parameters, and defining rule set to extract buildings and eliminate misclassified other urban features. The suitability of the technique has been judged using different indicators, such as, completeness, correctness and quality.

Multiple Saliency Features Based Automatic Road Extraction from High‐Resolution Multispectral Satellite Images

Roads as important artificial objects are the main body of modern traffic system, which provide many conveniences for human civilization. With the development of remote sensing and hyperspectral imaging technology, how to automatically and accurately extract road network from high-resolution multispectral satellite images has become a hot and challenging research topic of geographic information technology. In this paper, an automatic road extraction method from high-resolution multispectral satellite images is proposed by using multiple saliency features. Firstly, road edge is extracted by detecting local linear edge with Singular value decomposition (SVD). Secondly, road regions are constructed by K-means clustering after extracting the feature of background difference. Then road network is achieved by integrating multiple saliency features with Total variation (TV) based image fusion algorithm. Finally, the non-road parts and noises are removed from road network by optimizing multiple salient features with post-processing and morphological operations. The experimental results show that the proposed method can achieve a superior performance in completeness and correctness.

AUTOMATIC OBJECT-ORIENTED ROUNDABOUTS EXTRCTION FROM HIGH RESOLUTION MULTISPECTRAL IMAGES

Abstract. Road roundabouts, a typical class of road facilities to avoid collision, are generally not directed extracted in existing road extraction methods. This paper presents a novel four-step approach for automatic vegetated roundabout extractions from high resolution multispectral satellite images, which combines object-oriented extraction, Support Vector Machine (SVM) classification and spatial relationship estimation. Firstly, after proper preconditioning, the vegetated roundabouts are extracted by object-oriented extract algorithm in ENVI with rules that simultaneously taking area, roundness and vegetation index (NDVI) into consideration. After a certain number of experiments, the set of three items’ thresholds can be found, which may stand as the general rules for vegetated roundabouts extraction in similar conditions. Next, the roads are classified using Support Vector Machine (SVM) and the outputs are several band shaped polygons. Then, the holes in road polygons will be detected by examining the topological relation in ArcGIS. Lastly, since the margin of extracted roundabout and the biggest detected hole may not strictly coincide, by comparing the distance between central points of both the extracted roundabout and the hole with the threshold, convincing determination can be made. The proposed automatic approach has been proved to have very high production accuracy that all above 85 % in each case of the test set, which is good enough for automatic vegetated roundabouts extraction from high resolution remote sensing images without manual interpretation.

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna.

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ = 0 . 93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.

Estimation of urban tree canopy cover using random point sampling and remote sensing methods

Trees play an important role in urban areas by improving air quality, mitigating urban heat islands, reducing stormwater runoff and providing biodiversity habitat. Accurate and up-to-date estimation of urban tree canopy cover (UTC) is a basic need for the management of green spaces in cities, providing a metric from which variation can be understood, change monitored and areas prioritised. Random point sampling methods, such as i-Tree canopy, provide a cheap and quick estimation of UTC for a large area. Remote sensing methods using airborne Light Detection And Ranging (LiDAR) and multi-spectral images produce accurate UTC maps, although greater processing time and technical skills are required. In this paper, random point sampling and remote sensing methods are used to estimate UTC in Williamstown, a suburb of Melbourne, Australia. High resolution multi-spectral satellite images fused with LiDAR data with pixel-level accuracy are employed to produce the UTC map. The UTC is also estimated by categorising random points (a) automatically using the LiDAR derived UTC map and (b) manually using Google Maps and i-Tree canopy software. There was a minimum 1% difference between UTC estimated from the map derived from remotely sensed data and only 1000 random points automatically categorised by that same map, indicating the level of error associated with a random sampling approach. The difference between UTC estimated by remote sensing and manually categorised random point sampling varied in range of 4.5% using a confidence level of 95%. As monitoring of urban forest canopy becomes an increasing priority, the uncertainties associated with different UTC estimates should be considered when tracking change or comparing different areas using different methods.

Multitemporal Analysis of High-Spatial-Resolution Optical Satellite Imagery for Mangrove Species Mapping in Bali, Indonesia

Mapping zonations of mangrove species (ZMS) is important when assessing the functioning of such specific ecosystems. However, the reproducibility of remote sensing methods for discriminating and mapping mangrove habitats is often overstated due to the lack of temporal observations. Here, we investigated the potential use of temporal series of high-resolution multispectral satellite images to discriminate and map four typical Asian ZMS. This study was based on the analysis of eight images acquired between 2001 and 2014 over the mangrove area of Nusa Lembongan, Bali, Indonesia. Variations between years in the top-of-atmosphere reflectance signatures were examined as functions of the acquisition angles. We also applied maximum likelihood supervised classification to all of the images and determined the variability in the classification errors. We found that the distinction between spectral signatures of ZMS characterized by a close canopy was fairly independent of the season and sensor characteristics. By contrast, the variability in the multispectral signatures of ZMS with open canopies and associated classification errors could be attributed to variability in ground surface scattering. In both cases, sun-viewing geometry could alter the separability between ZMS classes in near-nadir viewing or frontward sun-viewing configurations, thereby explaining why the overall accuracy of ZMS classification might vary from 65% to 80%. Thus, multitemporal analysis is an important stage in the development of robust methods for ZMS mapping. It must be supported by physical-based research aiming to quantify the influences of canopy structure, species composition, ground surface properties, and viewing geometry parameters on ZMS multispectral signatures.

AEGIS: a wildfire prevention and management information system

Abstract. We describe a Web-GIS wildfire prevention and management platform (AEGIS) developed as an integrated and easy-to-use decision support tool to manage wildland fire hazards in Greece (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing online access to information that is essential for wildfire management. The system uses a number of spatial and non-spatial data sources to support key system functionalities. Land use/land cover maps were produced by combining field inventory data with high-resolution multispectral satellite images (RapidEye). These data support wildfire simulation tools that allow the users to examine potential fire behavior and hazard with the Minimum Travel Time fire spread algorithm. End-users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps are used to generate integrated output map of fire hazard prediction. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps. The system and associated computation algorithms leverage parallel processing techniques (i.e., High Performance Computing and Cloud Computing) that ensure computational power required for real-time application. All AEGIS functionalities are accessible to authorized end-users through a web-based graphical user interface. An innovative smartphone application, AEGIS App, also provides mobile access to the web-based version of the system.

IKONOS Image-Based Extraction of the Distribution Area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China

Stellera chamaejasme L. (S. chamaejasme) is one of the primary toxic grass species (poisonous plants) distributed in the alpine meadows of Qinghai Province, China. In this study, according to the distinctive phenological characteristics of S. chamaejasme, the spectral differences between S. chamaejasme in the full-bloom stage and other pasture grasses were analyzed and the red, blue, and near-infrared bands of IKONOS image were determined as the diagnostic bands of S. chamaejasme recognition. Feature indexes related to S. chamaejasme were established using the diagnostic bands, and \(NDVI_{blue} = (\rho_{nir} − \rho_{blue})/(\rho_{nir} + \rho_{blue})\) obtained as S. chamaejasme sensitive index based on the linear regression analysis between the indexes derived from field spectra and the actual cover fraction of S. chamaejasme communities. The distribution area of S. chamaejasme was extracted by using the index \(NDVI_{blue}\) derived from IKONOS multispectral image in Qilian County of Qinghai Province, China and the verified result reached an overall accuracy of 90.71%. The study indicated that high resolution multispectral satellite images (such as IKONOS images) had significant potential in remote sensing recognition of toxic grass species.

Multi-temporal assessment of ground cover restoration and soil erosion risks along petroleum and gas pipelines in Azerbaijan using GIS and remote sensing

The main goal of this research was to perform vegetation cover restoration and soil erosion risk assessment based on multi-temporal NDVI monitoring and deterministic USLE erosion prediction model along Baku–Tbilisi–Ceyhan Oil and South Caucasus Gas pipelines. The categorization of NDVI derived from IKONOS 2007 and PLEIADES 2012 high-resolution multispectral satellite images into the bare lands, sparse and dense vegetation revealed the positive vegetation cover restoration along oil and gas pipelines. The change detection analysis of NDVIs showed that the class of ‘Significant Increase in Vegetation’ was 5.40 km2 within the croplands. However, the classes of ‘Significant Decrease in Vegetation’ and ‘No Significant Change in Vegetation’ were observed to be 2.56 and 5.10 km2 within croplands, correspondingly. This decrease can be explained by the encumbrances applied for the partial restrictions of land-use activities along oil and gas pipelines to mitigate potential risks to pipelines and by the slow natural vegetation restoration. NDVI analysis for 50-m section polygons of pipeline corridor and contiguous areas showed that 7072 polygons had no significant NDVI difference in 2012, whereas in 2007 it was 4383. USLE model run with cover-management factor derived from PLEIADES NDVI 2012 showed higher number of polygons with predicted erosion class of ‘0–10 ton/ha’ which is acceptable and not critical to pipelines. For higher erosion classes more than ‘0–10 ton/ha’, USLE model run with IKONOS NDVI 2007 revealed higher number of polygons. USLE model run predicted 37 % of total number of erosion occurrences identified during 2005–2014.

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.

Classification of high resolution satellite images using spatial constraints-based fuzzy clustering

A spatial constraints-based fuzzy clustering technique is introduced in the paper and the target application is classification of high resolution multispectral satellite images. This fuzzy-C-means (FCM) technique enhances the classification results with the help of a weighted membership function (Wmf). Initially, spatial fuzzy clustering (FC) is used to segment the targeted vegetation areas with the surrounding low vegetation areas, which include the information of spatial constraints (SCs). The performance of the FCM image segmentation is subject to appropriate initialization of Wmf and SC. It is able to evolve directly from the initial segmentation by spatial fuzzy clustering. The controlling parameters in fuzziness of the FCM approach, Wmf and SC, help to estimate the segmented road results, then the Stentiford thinning algorithm is used to estimate the road network from the classified results. Such improvements facilitate FCM method manipulation and lead to segmentation that is more robust. The results confirm its effectiveness for satellite image classification, which extracts useful information in suburban and urban areas. The proposed approach, spatial constraint-based fuzzy clustering with a weighted membership function (SCFCWmf), has been used to extract the information of healthy trees with vegetation and shadows showing elevated features in satellite images. The performance values of quality assessment parameters show a good degree of accuracy for segmented roads using the proposed hybrid SCFCWmf-MO (morphological operations) approach which also occluded nonroad parts.

Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images

Many public and private decisions rely on geospatial information stored in a GIS database. For good decision making this information has to be complete, consistent, accurate and up-to-date. In this paper we introduce a new approach for the semi-automatic verification of a specific part of the, possibly outdated GIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR) multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classification based on a Markov Random Field is employed to extract image regions which contain agricultural areas (without distinction between cropland and grassland), and these regions are intersected with boundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into different homogeneous regions by means of image segmentation, followed by a classification of these segments into either cropland or grassland using a Support Vector Machine. The classification result of all segments belonging to one GIS object are finally merged and compared with the GIS database label. The developed approach was tested on a number of images. The evaluation shows that errors in the GIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process.

Satellite-based irrigation advisory services: A common tool for different experiences from Europe to Australia

Earth Observation techniques are widely recognised in supporting the management of land and water resources and they are nowadays being transferred to operative applications. In this paper, we present the current status of a satellite-based irrigation advisory system based on dedicated webGIS or farmers and district managers, in three different agricultural systems and environments: Southern Italy, Austria and Southern Australia. Maps of canopy development (leaf area index, albedo and soil cover) are derived from high-resolution (20m) multispectral satellite images, delivered in near real time (24–36h) and processed by using in-situ agro-meteorological data. The outputs of this procedure are: (i) a personalised irrigation advice, based on the calculation of crop evapotranspiration under standard conditions (according to FAO-56 definition and by using the direct approach) by taking into account the actual canopy development and crop variability at sub-plot scale; (ii) timely delivery of the information, consisting in maps and suggested irrigation volume applications, timely published on a dedicated webGIS-site with access restricted to growers and basin authorities in order to better control the irrigation process and consequently improve its overall efficiency. The key-points of this procedure are: (a) personalised irrigation advice; (b) timely delivery of the information. Final users have provided important feedback on the usage of the information provided; i.e. farmers are able to recognise without difficulties their parcels on the images and they schedule the irrigations by taking into account the information provided. The crop heterogeneity captured by the high resolution images is considered as a valuable add-on information to identify the variability of soil texture and fertility, plant nutrition, or different performance of irrigation systems. All the farmers have evaluated positively the usefulness of the information provided, and in most cases an increase of irrigation efficiency was achieved, because of the reduction of water volumes.