Extracting a cluster of clouds in the SPOT-4 satellite images

In this paper, rules of the segmentation of cloud cover for SPOT-4 satellite images, which permit reducing the segmentation error of cloud cover to 10%, and a method of measurement of a cluster of clouds as image regions are proposed.     

卫星云图中热带风暴云区的三维绘制技术研究

实现热带风暴场景的模拟对于灾害预防、评估等工作具有十分重要的意义。改进了一种基于图像色彩和灰度值的算法,能够较好地从卫星云图中分割提取出热带风暴的云层区域,进而利用一定规则生成了云区体数据。针对生成体数据的特点,采用了一种改进的快速光线投射算法来完成热带风暴云区的三维绘制,最终实现了对于云区的任意角度实时观察和动态绘制。     

Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping

This study proposed a multi-scale, object-based classification analysis of SPOT-5 imagery to map Moso bamboo forest. A three-level hierarchical network of image objects was developed through multi-scale segmentation. By combining spectral and textural properties, both the classification tree and nearest neighbour classifiers were used to classify the image objects at Level 2 in the three-level object hierarchy. The feature selection results showed that most of the object features were related to the spectral properties for both the classification tree and nearest neighbour classifiers. Contextual information characterized by the composition of classified image objects using the class-related features assisted the detection of shadow areas at Levels 1 and 3. Better classification results were achieved using the nearest neighbour algorithm, with both the producer’s and user’s accuracy higher than 90% for Moso bamboo and an overall accuracy of over 85%. The object-based approach toward incorporating textural and contextual information in classification sequence at various scales shows promise in the analysis of forest ecosystems of a complex nature.     

A satellite data set for tropical forest area change assessment

A database of largely cloud-free (less than 2.5% of all sites have more than 5% cloud cover), geo-referenced 20 km?×?20 km sample sites of 30 m resolution optical satellite imagery have been prepared for the 1990 and 2000 epochs. This spans the tropics with a systematic sample located at the degree confluence points of the geographic grid. The resulting 4016 sample pairs are to be used to measure changes in the area of forest cover between the two epochs. The primary data source was the National Aeronautics and Space Administration's (NASA's) global land survey (GLS) data sets. Visual screening of GLS images at all 4016 confluence points from each date identified 2868 suitable pairs where no better alternatives exist (71.6% of the sample). Better alternatives could be found for 26.6% of the sample, substituting cloudy or missing GLS data sets at one or the other epoch or both (GLS-1990 or GLS-2000). Gaps were filled from the United States Geological Survey (USGS) Landsat archives (1070 samples), data from other Landsat archives (53 samples) or with alternatives to Landsat, that is, 15 samples from Satellite Pour l'Observation de la Terre (SPOT). This increased the effective number of sample pairs to 3945 representing 98% of all target samples. No suitable image pairs were found for 71 confluence points, which were not randomly distributed, but mostly concentrated in the Congo basin, where around 15% of the region remains un-sampled. Variations in date of image acquisition and geometric fidelity are documented. Results highlight the importance of combining systematic data-processing schemes with targeted image acquisition and archiving strategies for global scale applications such as deforestation monitoring and shows that by replacing cloudy or missing GLS data with alternative imagery, the overall coverage of the sample sites within the ecological zones ‘Tropical rainforest’ and ‘Tropical mountain system’ can be improved by 16%.     

Using SPOT-4 HRVIR and VEGETATION sensors to assess impact of tropical forest fires in Roraima,Brazil

Due to the El Niño phenomenon, the 1997-1998 dry season in Roraima (Brazil, Amazonia) was particularly pronounced. Consequently, vegetation fires spread widely and were monitored by many satellites in real time. Satellite images are currently being used to monitor vegetation fires either globally for climate studies or more regionally for impact assessment. After reviewing different satellite data used for impact assessment, this paper focuses on the contribution of SPOT-4's imagery provided by high resolution HRVIR and coarse resolution VEGETATION sensors. These sensors are described with emphasis on those characteristics of potential benefit for forest mapping and fire detection. Early images of Roraima from SPOT-4 are analysed and interpreted to delineate the areas already damaged by fire. VEGETATION images provide a first estimate of damaged areas on a regional scale and an indication of the main ecosystems affected. SPOT HRVIR is used to establish a much more precise classification of various ecosystems. Each vegetation class is associated with a biomass density. From the known burned areas, an estimate of burned biomass during the 1998 dry season is computed, as well as total carbon release. On an intensive study site of 20 400 km 2, 3060 km 2 of savannahs and crops and 6980 km 2 of forest have been burned; the corresponding carbon release is estimated as 210 000 t for croplands and savannahs and 23 M t for the evergreen seasonal forest. The estimated burnt surface areas derived from VEGETATION are then cross-validated with HRVIR and thus an attempt is made to extrapolate the burned biomass with the help of VEGETATION on a regional scale.     

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.     

Estimation of variable atmospheric parameters for the atmospheric correction of satellite images

The information about variable components of the atmosphere (aerosol, water vapour, and ozone) during acquisition is required for the atmospheric correction of spectral images acquired by shortwave sensors of the Earth observing remote-sensing satellites. The procedure to estimate aerosol optical depth and columnar water vapour by the inversion of the atmospheric radiative transfer model 6S using moderate-resolution spectra of incident solar radiation is proposed. Comparison to the results obtained by the Aerosol Robotic Network AERONET at an AERONET site at the distance of 50 km on days when both sensors were in the same air mass shows systematic overestimation both of aerosol optical depth and of columnar water vapour if aerosol optical depth is estimated in the wavelength range of 365–425 nm and columnar water in the range of 895–985 nm using spectra of total irradiance. If more wavelengths and diffuse-to-total spectral irradiance ratio are implemented in the inversion, the bias of estimated water vapour decreases, but aerosol optical depth is underestimated. The estimates at 50 km distance are well correlated. The modelled spectral irradiance using estimated atmospheric parameters matches the measured spectra with high accuracy. In the spectral bands of the Sentinel-2 MultiSpectral Instrument (MSI), the differences do not exceed 2%.     

Reconstruction of cloud geometry from multi-view satellite images

Reflected solar radiances measured by the pushbroom cameras of the Multiangle Imaging SpectroRadiometer (MISR) on the Terra satellite at nine viewing angles are combined to give eight stereo pairs. These are analyzed with stereo-photogrammetric methods to measure the geometry of a convective cloud system. Both cloud-top heights and cloud sides are retrieved with a precision of about 200-300 m. Two case studies of deep, convective clouds over ocean are considered. The accuracy of the MISR retrieval is tested in the first case study by reference to coincident, higher resolution stereo data from ASTER, showing how the accuracy of the cloud-top height retrieval is improved using the oblique MISR views. In the second case study, the entire cross-section of the cloud aligned with the viewing azimuthal direction is measured, using all nine cameras. The methodology presented is an important step towards more routine retrievals of the 3D geometrical reconstruction of isolated, deep-convective clouds. Such reconstructions are a necessary prerequisite to the subsequent 3D radiative transfer modeling used to aid the remote sensing of the elusive microphysical properties of such clouds.     

Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors

Tropical forests are an important component of the global carbon balance, yet there is considerable uncertainty in estimates of their carbon stocks and fluxes, which are typically estimated through analysis of aboveground biomass in field plots. Remote sensing technology is critical for assessing fine-scale spatial variability of tropical forest biomass over broad spatial extents. The goal of our study was to evaluate relatively new technology, small-footprint, discrete-return lidar and hyperspectral sensors, for the estimation of aboveground biomass in a Costa Rican tropical rain forest landscape. We derived a suite of predictive metrics for field plots: lidar metrics were calculated from plot vertical height profiles and hyperspectral metrics included fraction of spectral mixing endmembers and narrowband indices that respond to photosynthetic vegetation, structure, senescence, health and water and lignin content. We used single- and two-variable linear regression analyses to relate lidar and hyperspectral metrics to aboveground biomass of plantation, managed parkland and old-growth forest plots. The best model using all 83 biomass plots included two lidar metrics, plot-level mean height and maximum height, with an r² of 0.90 and root-mean-square error (RMSE) of 38.3 Mg/ha. When the analysis was constrained to plantation plots, which had the most accurate field data, the r² of the model increased to 0.96, with RMSE of 10.8 Mg/ha (n = 32). Hyperspectral metrics provided lower accuracy in estimating biomass than lidar metrics, and models with a single lidar and hyperspectral metric were no better than the best model using two lidar metrics. These results should be viewed as an initial assessment of using these combined sensors to estimate tropical forest biomass; hyperspectral data were reduced to nine indices and three spectral mixture fractions, lidar data were limited to first-return canopy height, sensors were flown only once at different seasons, and we explored only linear regression for modeling. However, this study does support conclusions from studies at this and other climate zones that lidar is a premier instrument for mapping biomass (i.e., carbon stocks) across broad spatial scales.     

Road network extraction from high resolution satellite images

In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine(SVM). Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM's classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.     

Urban road extraction from high-resolution optical satellite images

In recent years, many approaches have been exploited for automatic urban road extraction. Most of these approaches are based on edge and line detecting algorithms. In this paper, a new integrated system for automatic extraction of main roads in high-resolution optical satellite images is present. Firstly, a multi-scale greylevel morphological cleaning algorithm is proposed to reduce the grey deviation of the road regions. Secondly, based on the greylevel difference between road surfaces and environmental objects, a colour high-resolution satellite image is segmented into a simplified imagemap by using the mean shift algorithm, which consists of three stages. The first stage deals with image filtering, the second stage deals with colour segmentation, and the third stage is proposed to fuse small regions in the segmented image. The mean shift filter algorithm not only smoothes the image, but also preserves abrupt changes (i.e. edges) in the local structure. The mean shift segmentation algorithm is a straightforward extension of the smoothing algorithm, which preserves discontinuity. From the histogram of the simplified imagemap, we can find the potential road surfaces, and use greylevel threshold to convert the segmented image into a binary one. The binary image is processed by using binary mathematical morphological closing and opening to remove small objects and to open the connected street blocks. We use a contour tracing algorithm to remove holes in street-block regions and to detect the street blocks' contours. In this research we found that many street blocks' contours were preserved perfectly, except for some of them which were depressed. Finally, we utilize the convex hull algorithm to smooth the street blocks' zigzag edges and to close the gaps in some street blocks, and then, we get the road edges. The integrated system for road network extraction is tested on the red band of an IKONOS multispectral image; all algorithms in this study are developed in C++ under Windows XP operating system. Results of the road network extraction are presented to illustrate the validation of the extracting strategy and the corresponding algorithms in this research, and future prospects are exposed.     

Validating the use of metre-scale multi-spectral satellite image data for identifying tropical forest tree species

Identifying and mapping tropical trees at the species level from space can support an improved assessment of forest composition, forest carbon uptake, tree species distribution and preferred habitat as well as a better understanding of the response of forests to climate change. In this study, the development of a validated data and image-processing schema demonstrated the capability of current metre-scale satellite technology (WorldView-3) to identify specific tree species within an unmanaged tropical forest. The experimental site, La Selva Biological Station in Costa Rica, provided access for field validation and spectral data acquisition of individual tree canopies from established canopy towers. It is also a representative biome of diverse lowland Atlantic tropical forests in Central America. The process defined in this paper calibrated and corrected field-acquired ASD field spectra for ten tree species and corrected WorldView-3 image data for viewing and illumination geometry. In addition, assessments of three current atmospheric compensation methods for correcting recent WorldView-3 satellite imagery established the most accurate compensation process for a tropical forest setting. Corrected reflectance in the satellite data matched the spectrometer data to ±0.25% for visible bands and ±0.5% for near-infrared bands. This study shows that spectral data from the satellite and field spectrometer data are nearly equivalent when applying the appropriate atmospheric compensation, band response emulation, and viewing correction processes established in this study.     

A new approach for tropical forest area monitoring using multiple spatial resolution satellite sensor imagery

Tropical forest deforestation is a major concern at global, regional, and local scales. Recent advances have demonstrated the feasibility of using coarse spatial resolution remote sensing imagery to assess tropical forest over large areas. To date, few methods have been developed for the estimation of change over large areas. A change detection method based on a combination of NOAAAVHRR, Landsat TM, and SPOT-HRV data is evaluated on a study site in Vietnam, Southeast Asia. Changes detected with fine spatial resolution imagery are related to AVHRR-derived forest class proportions and fragmentation patterns.     

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO₂ between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO₂ flux measurements at individual CO₂ eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO₂ flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.     

Derivation of 3D cloud animation from geostationary satellite images

Large-scale cloud animation is crucial to TV weather presentation, weather observer training and video products. In this paper, a physically based system is presented for the derivation of time-varying 3D clouds from geostationary satellite images. Cloud properties are derived from a set of meteorological models while the clouds are rendered by graphics models, the proposed method thus presents a new modeling methodology, which integrates the reality of the data with the realistic visual feeling. In particular, image pixels are first classified into cloud-free, water cloud, ice cloud, thin cirrus cloud in terms of their spectral signature. Then, cloud top surface, cloud bottom surface and cloud extinction are generated by applying different combinations of images. Finally, clouds are rendered under various light directions or view directions. The results have indicated that the proposed method can yield a realistic and approximately valid clouds with similar appearance to those in the input satellite images.     

Estimation of object motion parameters from noisy images

An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.     

Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images

ABSTRACT

Mapping of the distribution of individual seagrass species is essential for any attempts to manage seagrass ecosystems. It is therefore important to understand how the spectra of different seagrass species vary, in order to establish their unique absorption features and how these can be utilised for mapping by making use of remote-sensing images. This paper presents measurements of the reflectance spectra between 400 and 900 nm for nine tropical species of seagrass. Continuum removal and multispectral resampling procedures were applied to the spectra. Dendrogram analysis was carried out to identify species clustering as the basis for a mapping scheme. Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) approaches were employed for the classification of seagrass species using WorldView-2 images and measured spectra as the input endmember. Classification Tree Analysis (CTA) and an image segmentation approach using CTA (Object-Based Image Analysis – OBIA) were performed as a means of comparison. The results indicate that the absorption features and overall shape of the spectra for all seagrass species are relatively similar, and implied that the major differences are attributable to the absolute reflectance values. Consequently, SAM and SID produced results of low accuracy (<30%), whereas, CTA and OBIA delivered results exhibiting higher accuracy (60–92%). The use of a spectral-based classification algorithm was ineffective for the classification and mapping of seagrass species using multispectral images. The utilisation of absolute reflectance values was beneficial for the classification of seagrass species having similar spectral shape.     

Analysis of lacunarity and scales of spatial homogeneity in IKONOS images of Amazonian tropical forest canopies

Extensive plot studies across Amazonia have demonstrated that there are large regional gradients in forest productivity and that the dynamics of the forests seem to have accelerated substantially in recent decades, with ensuing impacts on forest structure. Most of these sites are, however, one hectare plots nested within a heterogeneous landscape, and a clear need exists to understand the landscape and regional context of these studies. Remote sensing offers the potential to scale up from plot to higher landscape levels but it has proven complex to evaluate forest structure, and therefore biomass patterns in tropical areas, due to saturation, signal noises, and unclear relationships between reflectance values and structural properties, both for optical and radar systems. In this study, we explore the potential of a textural approach to detect landscape and regional variations in the structure of tropical forest canopies, as viewed from high resolution IKONOS satellite imagery. We used lacunarity analysis and a derived variable, the index of translational homogeneity (ITH), as a tool to search for structural and dynamic forest properties within and among different Amazonian landscapes. The main goals of this research were: (1) to examine the sensitivity and robustness of ITH analysis to details of the analysis procedure; (2) to explore the intra- and inter-regional textural properties of a variety of tropical forest canopies [Caxiuanã, Manaus, Sinop, Santarem (Brazil), and Tambopata (Peru)], and (3) to relate textural properties derived from lacunarity to structural properties of the forest canopy, mainly crown size. Our results show how ITH and lacunarity analyses offer insights into the spatial distribution of structural properties of forest canopies, easily differentiating between terra firme forests and swamp forests. The studied forest canopies are self-similar on length-scales of 5–11 m, and show translational invariance on scales above 20 m (central and western Amazonia) and 30 m (eastern Amazonia) For a restricted range of solar elevation angles, the ITH appears to be determined mainly by the mean size of tree crowns, and by the fraction of large (shadow-generating) trees.