SPOT-5 HRG Research Articles

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Capabilities of Multispectral Satellite Data in an Assessment of the Status of Abandoned Fire Hazardous and Rewetting Peat Extraction Lands

The capabilities of several multispectral satellite data types to identify the status of peatlands affected by peat extraction and abandoned deposits are examined to assess potential fire dangers and rewetting effectiveness. The available level of detail of describing land/vegetation cover for monitoring abandoned peat extraction sites using Spot-5 HRG, Spot-6 HRG, and Landsat-7 ETM+ satellite images has been demonstrated using the example of peatlands in the Meschera National Park (Vladimir oblast). The results reflect the pros and cons of using different data types to analyze the status of abandoned peat-extraction lands for purposes of peatland inventory, land-cover monitoring, and the prioritization of sites subject to rewetting and mire restoration, as well as for an evaluation of the effectiveness of these measures.

Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework

Accurate modelling of leaf chlorophyll content over a range of spatial and temporal scales is central to monitoring vegetation stress and physiological condition, and vegetation response to different ecological, climatic and anthropogenic drivers. A process-based modelling approach can account for variation in other factors affecting canopy reflectance, providing a more accurate estimate of chlorophyll content across different vegetation species, time-frames, and broader spatial extents. However, physically-based modelling studies usually use hyperspectral data, neglecting a wealth of data from broadband and multispectral sources. In this study, we assessed the potential for using canopy (4-Scale) and leaf radiative transfer (PROSPECT4/5) models to estimate leaf chlorophyll content using canopy Landsat satellite data and simulated Landsat bands from leaf level hyperspectral reflectance data. Over 600 leaf samples were used to test the performance of PROSPECT for different vegetation species, including black spruce (Picea mariana), sugar maple (Acer saccharum), trembling aspen (Populus tremuloides) and jack pine (Pinus banksiana). At the leaf level, hyperspectral and simulated Landsat bands showed very similar results to laboratory measured chlorophyll (R2=0.77 and R2=0.75, respectively). Comparisons between PROSPECT4 modelled chlorophyll from simulated Landsat and hyperspectral spectra showed a very close correspondence (R2=0.97, root mean square error (RMSE)=3.01μg/cm2), as did simulated reflectance bands from other broadband and narrowband sensors (MODIS: R2=0.99, RMSE=1.80μg/cm2; MERIS: R2=0.97, RMSE=2.50μg/cm2 and SPOT5 HRG: R2=0.96, RMSE=5.38μg/cm2). Modelled leaf chlorophyll content from Landsat 5 TM canopy reflectance data, acquired from over 40 ground validation sites, demonstrated a strong relationship with measured leaf chlorophyll content (R2=0.78, RMSE=8.73μg/cm2, p<0.001), and a high linearity with negligible systematic bias. Study results demonstrate the small number of input bands required for PROSPECT inversion and provide a theoretical and operational basis for the future retrieval of leaf chlorophyll content using broadband or multispectral sensors within a physically-based approach.

GeoEtrim, SharpQ and Epix: Trio of Tools for Geospatial Image Analysis

Abstract. This paper presents an overview of three approaches developed in Matlab for geospatial analysis of images. GeoEtrim has two subpackages, GeoSpot and GeoFigcon. GeoSpot aims to perform bundle adjustment of stereo linear array remotely sensed images considering their interior and exterior orientation parameters. The correlation among orientation parameters, their validation and efficiency on the final accuracy can be estimated. The current version is available for SPOT-5 HRG level 1A stereoimages, reaching ±1 pixel accuracy at ICPs (Independent Check Points). GeoFigcon is the other sub-package of GeoEtrim, developed for estimation of georeferencing accuracy of orthoimages generated by various sensor-independent mathematical models and RFM (Rational Function Model). Using GeoFigcon, one can estimate the combined effect of the accuracy of transformation parameters estimated/updated by GCPs and DEM accuracy on the georeferencing accuracy of orthoimages. The experiments with IKONOS Geo, QuickBird OrthoReady Standard, OrbView-3 Basic and Pléiades-1A Primary prove that using RFM produces higher accuracy than using sensor-independent transformation models. Moreover, accuracy varies reducing from geometric centre of GCPs accommodating the high profile of topography. SharpQ generates pan-sharp images using the methods PCA (Principal Component Analysis), Brovey, and IHS (Intensity Hue Saturation), validates their quality with quantitative analysis by the methods CC (Correlation Coefficient), RMSE (Root Mean Square Error), RASE (Relative Average Spectral Error), SAM (Spectral Angle Mapper) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthése). epix, the last member of the trio, can be used for estimation of effective GSD (Ground Sampling Distance) value of original or generated (such as pan-sharp) images, depending the principle of ESF (Edge Spread Function). So the real geometric resolution can be estimated for any kind of image. This trio is being still developed with the continuous research.

Estimating forest biomass and height using optical stereo satellite data and a DTM from laser scanning data

This paper investigates the possibilities of improving aboveground forest biomass and basal area weighted mean height estimates from optical multispectral satellite data by adding Canopy Height Models (CHMs) obtained from matching multiple view-angle satellite data. The analysis was carried out using data collected over the Remningstorp test site in southern Sweden from 2008–2011 and used training and validation data from airborne laser scanning and field data. CHMs were produced by subtracting a Digital Elevation Model (based on airborne laser scanning data) from the Digital Surface Models created by matching multiple view-angle SPOT-5 HRS and ALOS PRISM data. By modeling biomass and height using regression analysis on multispectral data from SPOT-5 HRG in combination with height metrics from the CHMs, an improved root mean squared error (RMSE) was attained, compared with using the individual satellite data sources alone. A comparison between SPOT-5 HRS and ALOS PRISM CHMs in combination with multispectral data was made at stand level using biomass and height estimates from laser scanning data as reference data. For biomass, the relative RMSE improved from 32.9% when using only multispectral data, to 29.2% and 22.4% when adding the CHM from SPOT-5 HRS and ALOS PRISM, respectively. The corresponding improvements for height were from 16.1% to 15.3% with the SPOT-5 HRS CHM and to 12.9% with the ALOS PRISM CHM. A further analysis of combining the ALOS PRISM CHM and multispectral data was made at sub-stand level with field measurements as reference data. This combination gave a relative RMSE of 20.6% for biomass and 10.5% for height. In conclusion, the estimation accuracy for aboveground biomass and basal area weighted mean height was improved by adding CHM data to multispectral data from optical satellites.

Identifying potential areas of Cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data

The rapid and efficient detection of illicit drug cultivation, such as that of Cannabis sativa, is important in reducing consumption. The objective of this study was to identify potential sites of illicit C. sativa plantations located in the semi-arid, southern part of Pernambuco State, Brazil. The study was conducted using an object-based image analysis (OBIA) of Système Pour l'Observation de la Terre high-resolution geometric (SPOT-5 HRG) images (overpass: 31 May, 2007). OBIA considers the target's contextual and geometrical attributes to overcome the difficulties inherent in detecting illicit crops associated with the grower's strategies to conceal their fields and optimizes the spectral information extracted to generate land-cover maps. The capabilities of the SPOT-5 near-infrared and shortwave infrared bands to discriminate herbaceous vegetation with high water content, and employment of the support vector machine classifier, contributed to accomplishing this task. Image classification included multiresolution segmentation with an algorithm available in the eCognition Developer software package. In addition to a SPOT-5 HRG multispectral image with 10 m spatial resolution and a panchromatic image with 2.5 m spatial resolution, first-order indices such as the normalized difference vegetation index and ancillary data including land-cover classes, anthropogenic areas, slope, and distance to water sources were also employed in the OBIA. The classification of segments (objects) related to illegal cultivation employed fuzzy logic and fixed-threshold membership functions to describe the following spectral, geometrical, and contextual properties of targets: vegetation density, topography, neighbourhood, and presence of water supplies for irrigation. The results of OBIA were verified from a weight of evidence analysis. Among 15 previously known C. sativa sites identified during police operations conducted on 5–17 June 2007, eight sites were classified as maximum-alert areas (total area of 22.54 km2 within a total area of object-oriented image classification of ∼1800 km2). The approach proposed in this study is feasible for reducing the area to be searched for illicit cannabis cultivation in semi-arid regions.

An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia

Operational monitoring of vegetation and land surface change over large areas can make good use of satellite sensors that measure radiance reflected from the Earth’s surface. Monitoring programs use multiple images for complete spatial coverage over time. Accurate retrievals of vegetation cover and vegetation change estimates can be hampered by variation, in both space and time, in the measured radiance, caused by atmospheric conditions, topography, sensor location, and sun elevation. In order to obtain estimates of cover that are comparable between images, and to retrieve accurate estimates of change, these sources of variation must be removed. In this paper we present a preprocessing scheme for minimising atmospheric, topographic and bi-directional reflectance effects on Landsat-5 TM, Landsat-7 ETM+ and SPOT-5 HRG imagery. The approach involves atmospheric correction to compute surface-leaving radiance, and bi-directional reflectance modelling to remove the effects of topography and angular variation in reflectance. The bi-directional reflectance model has been parameterised for eastern Australia, but the general approach is more widely applicable. The result is surface reflectance standardised to a fixed viewing and illumination geometry. The method can be applied to the entire record for these instruments, without intervention, which is of increasing importance with the increased availability of long term image archives. Validation shows that the corrections improve the estimation of reflectance at any given angular configuration, thus allowing the removal from the reflectance signal of much variation due to factors independent of the land surface. The method has been used to process over 45,000 Landsat-5 TM and Landsat-7 ETM+ scenes and 2,500 SPOT-5 scenes, over eastern Australia, and is now in use in operational monitoring programs.

THE USE OF EXISTING GLOBAL ELEVATION DATASET FOR ABSOLUTE ORIENTATION OF HIGH RESOLUTION IMAGE WITHOUT GCPS

Abstract. For precise mapping using high resolution satellite images, various ways to eliminate the need of ground control points have been proposed. DEM matching, a technique to match two different surfaces by adjusting their position and attitude, is also one way of them. Our previous study demonstrated that DEM matching can be used for bias compensation of rigorous pushbroom sensor models. In this paper, we exploit the DEM matching technique further using existing global elevation dataset for absolute orientation of high resolution satellite image without ground control points. In particular, we investigated how the existing global DEM available worldwide can substitute ground control points when DEM matching is applied to linear pushbroom imagery. We used Spot-5 HRG with 2.5m ground sampling distance(GSD) as high resolution satellite images and used SRTM-derived DEMs(DTEDs) with gird spacing of 30m and 90m as existing elevation dataset. We firstly established relative orientation and generated a relative DEM without use of ground control points. DEM matching was then applied between the relative DEM and the DTED. We applied DEM matching based on linear pushbroom images. Through DEM matching, accuracy of about 2 GSD or better could be achieved with in the horizontal and in the vertical direction, respectively. We confirmed automatic DEM matching processing can be accomplished for absolute orientation of pushbroom image and as results, absolute DEM can be achieved from relative DEM. We tested the applicability of 90m existing global dataset for DEM matching. Our experimental results showed the potential of existing DEM for precise mapping without ground control points. This is very encouraging in terms of applicability of the existing global elevation data. This paper will contribute to understanding of DEM matching for linear pushbroom images and of solution to precise mapping without ground control points.

Reprint of: Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area

Remote sensing is a valuable tool for estimating forest biomass in remote areas. This study explores retrieval of forest above-ground biomass (AGB) over a cold and arid region in Northwest China, using two different methods (non-parametric and parametric), field data, and three different remote sensing data: a SPOT-5 HRG image, multi-temporal dual-polarization ALOS PALSAR and airborne LiDAR data. The non-parametric method was applied in 300 different configurations, varying both the mathematical formulation and the data input (SPOT-5 and ALOS PALSAR), and the quality of the performance of each configuration was evaluated by Leave One Out (LOO) cross-validation against ground measurements. For the parametric method (the multivariate linear regression), the same remote sensing data were used, but in one additional configuration the airborne LiDAR data were used for stepwise multiple regression.The result of the best performing non-parametric configuration was satisfactory (R=0.69 and RMSE=20.7tons/ha). The results for the parametric method were notoriously inaccurate, except for the case where airborne LiDAR data were included. The regression method with airborne low density LiDAR point cloud data was the best of all tested methods (R=0.84 and RMSE=15.2tons/ha). A cross comparison of the two best results showed that the non-parametric method performs nearly as well as the parametric method with LiDAR data, except for some areas where forests have a very heterogeneous structure. It is concluded that the non-parametric method with SPOT data is able to map forest AGB operatively over the cold and arid region as an alternative to the more expensive airborne LiDAR data.

Automated Georeferencing of Optical Satellite Data with Integrated Sensor Model Improvement

The geometric processing of remotely sensed image data is one of the key issues in data interpretation, added value product generation, and multi-source data integration. Although optical satellite data can be orthorectified without the use of Ground Control Points (GCP) to absolute geometric accuracies of some meters up to several hundred meters depending on the satellite mission, there is still a need to improve the geometric accuracy by using GCP. The manual measurement of GCP is time consuming work, and leads, especially for larger data sets with hundreds of satellite images, to a cost and time ineffective workload. To overcome these shortcomings, an autonomous processing chain to georeference and orthorectify optical satellite data is proposed which uses reference data and digital elevation models to generate GCP and to improve sensor model parameters (namely for rigorous and universal sensor models) for a series of optical Earth observation satellite systems. Using a restrictive blunder removal strategy, the proposed procedure leads to high quality orthorectified products or at least to a geometrically consistent data set in terms of relative accuracy. The geometric processing chain is validated using SPOT-4 HRVIR, SPOT-5 HRG, IRS-P6 LISS III, and ALOS AVNIR-2 optical sensor data, for which a huge amount of satellite data (3,200 scenes) has been processed. Relative and absolute geometric accuracies of approximately half the pixel size (linear Root Mean Square Error) are achieved.

A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery

Pixel-based and object-based image analysis approaches for classifying broad land cover classes over agricultural landscapes are compared using three supervised machine learning algorithms: decision tree (DT), random forest (RF), and the support vector machine (SVM). Overall classification accuracies between pixel-based and object-based classifications were not statistically significant (p>0.05) when the same machine learning algorithms were applied. Using object-based image analysis, there was a statistically significant difference in classification accuracy between maps produced using the DT algorithm compared to maps produced using either RF (p=0.0116) or SVM algorithms (p=0.0067). Using pixel-based image analysis, there was no statistically significant difference (p>0.05) between results produced using different classification algorithms. Classifications based on RF and SVM algorithms provided a more visually adequate depiction of wetland, riparian, and crop land cover types when compared to DT based classifications, using either object-based or pixel-based image analysis. In this study, pixel-based classifications utilized fewer variables (15 vs. 300), achieved similar classification accuracies, and required less time to produce than object-based classifications. Object-based classifications produced a visually appealing generalized appearance of land cover classes. Based exclusively on overall accuracy reports, there was no advantage to preferring one image analysis approach over another for the purposes of mapping broad land cover types in agricultural environments using medium spatial resolution earth observation imagery.

Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image

This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested. The LM–ANN and RBF–ANN, which outperform the other two, were selected to make a detailed comparison with support vector machines (SVMs). The experiments show that those well-trained ANNs and SVMs have no significant difference in classification accuracy, but the SVM usually performs slightly better. Analysis of the effect of the training set size highlights that the SVM classifier has great tolerance on a small training set and avoids the problem of insufficient training of ANN classifiers. The testing also illustrates that the ANNs and SVMs can vary greatly with regard to training time. The LM–ANN can converge very quickly but not in a stable manner. By contrast, the training of RBF–ANN and SVM classifiers is fast and can be repeatable.

Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area

Remote sensing is a valuable tool for estimating forest biomass in remote areas. This study explores retrieval of forest above-ground biomass (AGB) over a cold and arid region in Northwest China, using two different methods (non-parametric and parametric), field data, and three different remote sensing data: a SPOT-5 HRG image, multi-temporal dual-polarization ALOS PALSAR and airborne LiDAR data. The non-parametric method was applied in 300 different configurations, varying both the mathematical formulation and the data input (SPOT-5 and ALOS PALSAR), and the quality of the performance of each configuration was evaluated by Leave One Out (LOO) cross-validation against ground measurements. For the parametric method (the multivariate linear regression), the same remote sensing data were used, but in one additional configuration the airborne LiDAR data were used for stepwise multiple regression.The result of the best performing non-parametric configuration was satisfactory (R=0.69 and RMSE=20.7tons/ha). The results for the parametric method were notoriously inaccurate, except for the case where airborne LiDAR data were included. The regression method with airborne low density LiDAR point cloud data was the best of all tested methods (R=0.84 and RMSE=15.2tons/ha). A cross comparison of the two best results showed that the non-parametric method performs nearly as well as the parametric method with LiDAR data, except for some areas where forests have a very heterogeneous structure. It is concluded that the non-parametric method with SPOT data is able to map forest AGB operatively over the cold and arid region as an alternative to the more expensive airborne LiDAR data.

Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data

In this study, a combination of low and high density airborne LiDAR and satellite SPOT-5 HRG data were used in conjunction with ground measurements of forest structure to parameterize four models for zero-plane displacement height d(m) and aerodynamic roughness length z 0 m (m), over cool-temperate forests in Heihe River basin, an arid region of Northwest China. For the whole study area, forest structural parameters including tree height (Ht) (m), first branch height (FBH) (m), crown width (CW) (m) and stand density (SD)(trees ha − 1 ) were derived by stepwise multiple linear regressions of ground-based forest measurements and height quantiles and fractional canopy cover ( f c ) derived from the low density LiDAR data. The high density LiDAR data, which covered a much smaller area than the low density LiDAR data, were used to relate SPOT-5's reflectance to the effective plant area index (PAIe) of the forest. This was done by linear spectrum decomposition and Li–Strahler geometric–optical models. The result of the SPOT-5 spectrum decomposition was applied to the whole area to calculate PAIe (and leaf area index LAI). Then, four roughness models were applied to the study area with these vegetation data derived from the LiDAR and SPOT-5 as input. For validation, measurements at an eddy covariance site in the study area were used. Finally, the four models were compared by plotting histograms of the accumulative distribution of modeled d and z 0 m in the study area. The results showed that the model using by frontal area index (FAI) produced best d estimate, and the model using both LAI and FAI generated the best z 0 m . Furthermore, all models performed much better when the representative tree height was Lorey's mean height instead of using an arithmetic mean.

Large-scale monitoring of coppice forest clearcuts by multitemporal very high resolution satellite imagery. A case study from central Italy

Reliable assessment of forest resource stock, productivity and harvesting is a commonly agreed objective of environmental monitoring programs. Distinctively, the assessment of wood harvesting has become even more relevant to evaluate the sustainability of forest management and to quantify forest carbon budget. This paper presents the development and testing of procedures for assessing forest harvesting in coppice forests by very high resolution (VHR) satellite imagery. The study area is located in central Italy over approximately 34,000 km 2. A set of SPOT5 HRG multispectral images was acquired for the study years (2002–2007). Official administrative statistics of coppice clearcuts were also acquired. More than 9500 clearcuts were mapped and dated by on-screen interpretation of the SPOT5 images. In a subset of the study area various methods for semi-automatic clearcut mapping were tested by pixel- and object-oriented approaches. The following results are presented: ( i) clearcut map developed by visual interpretation of the SPOT5 images resulted in high thematic (overall accuracy of 0.99) and geometric (root mean square error of clearcut boundary delineation of 5.3 m) reliability; ( ii) object-oriented approach achieved significantly better accuracy than pixel-based methods for semi-automatic classification of the coppice clearcuts; ( iii) comparison between mapped clearcut area and official forest harvesting statistics proved a significant underestimation by the latter (65% of the total mapped clearcut area). A sample-based procedure exploiting VHR satellite imagery is finally proposed to correct the official statistics of coppice clearcuts.

Alternatives to Landsat-5 Thematic Mapper for operational monitoring of vegetation cover: considerations for natural resource management agencies

There is a serious concern as to whether the Landsat-5 Thematic Mapper (TM) will provide imagery up until the launch of the Landsat Data Continuity Mission (LDCM), which is expected in December 2012. The concern is due to fuel shortages and sporadic satellite and sensor problems. The Landsat-7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) malfunction in 2003 means that ETM+ imagery contains strips of missing data. Consequently there is concern that the highest quality Landsat imagery may not be available for monitoring for the years 2011 and 2012. Natural resource monitoring agencies, therefore, are faced with the serious challenge of determining a suitable alternative to Landsat-5 TM imagery should the need arise. While literature exists on possible alternatives, there is a lack of studies that provide guidelines to monitoring agencies on the issues to be explored when considering an alternative data source. We undertook a study that identified Landsat-7 ETM+, SPOT4 HRVIR, SPOT5 HRG, and IRS-P6 LISS-III as the most suitable alternatives for monitoring vegetation cover in Queensland and New South Wales (NSW) in eastern Australia. None of the sensors were found to be ideal candidates due to a combination of one or more of lower radiometric quality, increased data volumes, additional processing requirements, and higher purchasing costs. We found that the low cost and ease of access to Landsat-7 ETM+ made it a technically and economically viable option for annual monitoring of woody vegetation extent and change in eastern Australia. However, Landsat-7 ETM+ may not be an option in many areas of the world due to high cloud coverage. We used the experiences gained through this work to recommend a process that natural resource management agencies can use to explore the issues related to selecting an alternative to Landsat-5 TM for land cover monitoring.

RS-based geomorphic analysis of Zhangjiajie Sandstone Peak Forest Geopark, China

This study aims to characterize the regional geomorphic features of Zhangjiajie Sandstone Peak Forest Geopark in northwest Hunan, China. High-resolution French SPOT-5 HRG data (red, green and NIR-10 m, SWIR-20 m and Pan-2.5 m ground resolution), in conjunction with topographic mapping and field surveys, were utilized for systematic analysis of various morphometric landforms and their possible constraints. Four major types of landforms were discriminated in the study area based on visual interpretation of satellite sensor data. A high diversity of sandstone landforms is in the west and southeast geopark, involving mesa, square mountain, peak wall, peak cluster, peak forest, single peak, remnant peak. Surrounding the sandstones is the widely distributed denudation terrain, occupying 44% of the region. The alluvium locally scatters in the middle Suoxi River, whilst the karst landscapes preferably emerge in the limestone area, covering an area of about 82 km 2 . Our study indicates that the distribution of various landscapes is primarily controlled by the distinct linear features and river system, which is of significance to increase understanding the landform evolutionary processes. The remote sensing approach described in this paper can quickly and accurately map terrain at coarse approximation and semi-detailed levels. This method, in combination with appropriate conventional methods, can associate diverse landforms with their possible controls, thus allowing further scientific geomorphic evaluation and sustainable management at river basin level.

Estimating models of vegetation fractional coverage based on remote sensing images at different radiometric correction levels

The images of post atmospheric correction reflectance (PAC), top of atmosphere reflectance (TOA), and digital number (DN) of a SPOT5 HRG remote sensing image of Nanjing, China were used to derive four vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), transformed vegetation index (TVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI). Based on these VIs and the vegetation fractional coverage (VFC) data obtained from field measurements, thirty-six VI-VFC relationship models were established. The results showed that cubic polynomial models based on NDVI and TVI from PAC were the best, followed by those based on SAVI and MSAVI from DN, with their accuracies being slightly higher than those of the former two models when VFC>0.8. The accuracies of these four models were higher in medium densely vegetated areas (VFC = 0.4–0.8) than in sparsely vegetated areas (VFC = 0–0.4). All the models could be used elsewhere via the introduction of a calibration model. In VI-VFC modeling, using VIs derived from different radiometric correction levels of remote sensing images could help explore and show valuable information from remote sensing data and thus improve the accuracy of VFC estimation.

Using SPOT-5 HRG Data in Panchromatic Mode for Operational Detection of Small Ships in Tropical Area.

Nowadays, there is a growing interest in applications of space remote sensing systems for maritime surveillance which includes among others traffic surveillance, maritime security, illegal fisheries survey, oil discharge and sea pollution monitoring. Within the framework of several French and European projects, an algorithm for automatic ship detection from SPOT–5 HRG data was developed to complement existing fishery control measures, in particular the Vessel Monitoring System. The algorithm focused on feature–based analysis of satellite imagery. Genetic algorithms and Neural Networks were used to deal with the feature–borne information. Based on the described approach, a first prototype was designed to classify small targets such as shrimp boats and tested on panchromatic SPOT–5, 5–m resolution product taking into account the environmental and fishing context. The ability to detect shrimp boats with satisfactory detection rates is an indicator of the robustness of the algorithm. Still, the benchmark revealed problems related to increased false alarm rates on particular types of images with a high percentage of cloud cover and a sea cluttered background.

Modeling forest aboveground biomass by combining spectrum, textures and topographic features

Many textural measures have been developed and used for improving land cover classification accuracy, but they rarely examined the role of textures in improving the performance of forest aboveground biomass estimations. The relationship between texture and biomass is poorly understood. In this paper, SPOT5 HRG datasets were ortho-rectified and atmospherically calibrated. Then the transform of spectral features is introduced, and the extraction of textural measures based on the Gray Level Co-occurrence Matrix is also implemented in accordance with four different directions (0°, 45°, 90° and 135°) and various moving window sizes, ranging from 3 × 3 to 51 × 51. Thus, a variety of textures were generated. Combined with derived topographic features, the forest aboveground biomass estimation models for five predominant forest types in the scenic spot of the Mausoleum of Sun Yat-Sen, Nanjing, are identified and constructed, and the estimation accuracies exhibited by these models are also validated and evaluated respectively. The results indicate that: 1) Most textures are weakly correlated with forest biomass, but minority textural measures such as ME, CR and VA play a significantly effective and critical role in estimating forest biomass; 2) The textures of coniferous forest appear preferable to those of broad-leaved forest and mixed forest in representing the spatial configurations of forests; and 3) Among the topographic features including slope, aspect and elevation, aspect has the lowest correlation with the biomass of a forest in this study.