Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas

The relative abundance and distribution of trees in savannas has important implications for ecosystem function. High spatial resolution satellite sensors, including QuickBird and IKONOS, have been successfully used to map tree cover patterns in savannas. SPOT 5, with a 2.5 m panchromatic band and 10 m multispectral bands, represents a relatively coarse resolution sensor within this context, but has the advantage of being relatively inexpensive and more widely available. This study evaluates the performance of NDVI threshold and object based image analysis techniques for mapping tree canopies from QuickBird and SPOT 5 imagery in two savanna systems in southern Africa. High thematic mapping accuracies were obtained with the QuickBird imagery, independent of mapping technique. Geometric properties of the mapping indicated that the NDVI threshold produced smaller patch sizes, but that overall patch size distributions were similar. Tree canopy mapping using SPOT 5 imagery and an NDVI threshold approach performed poorly, however acceptable thematic accuracies were obtained from the object based image analysis. Although patch sizes were generally larger than those mapped from the QuickBird image data, patch size distributions mapped with object based image analysis of SPOT 5 have a similar form to the QuickBird mapping. This indicates that SPOT 5 imagery is suitable for regional studies of tree canopy cover patterns.     

Resolution vs. image quality in pre-tsunami imagery used for tsunami impact models in Aceh,Indonesia

Land cover roughness coefficients (LCRs) have been used in multivariate spatial models to test the mitigation potential of coastal vegetation to reduce impacts of the 2004 tsunami in Aceh, Indonesia. Previously, a Landsat 2002 satellite imagery was employed to derive land cover maps, which were then combined with vegetation characteristics, i.e., stand height, stem diameter and planting density to obtain LCRs. The present study tested LCRs extracted from 2003 and 2004 Landsat (30 m) images as well as a combination of 2003 and 2004 higher spatial resolution SPOT (10 m) imagery, while keeping the previous vegetation characteristics. Transects along the coast were used to extract land cover, whenever availability and visibility allowed. These new LCRs applied in previously developed tsunami impact models on wave outreach, casualties and damages confirmed previous findings regarding distance to the shoreline as a main factor reducing tsunami impacts. Nevertheless, the models using the new LCRs did not perform better than the original one. Particularly casualties models using 2002 LCRs performed better (δAIC > 2) than the more recent Landsat and SPOT counterparts. Cloud cover at image acquisition for Landsat and low area coverage for SPOT images decreased statistical predictive power (fewer observations). Due to the large spatial heterogeneity of tsunami characteristics as well as topographic and land-use features, it was more important to cover a larger area. Nevertheless, if more land cover classes would be referenced and high resolution imagery with low cloud cover would be available, the full benefits of higher spatial resolution imagery used to extract more precise land use roughness coefficients could be exploited.     

Monitoring Detailed Land‐Cover Changes in Shrubland Habitat Reserves Using Multi‐temporal IKONOS Data

Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.     

Mapping trees in high resolution imagery across large areas using locally variable thresholds guided by medium resolution tree maps

Large area tree maps, important for environmental monitoring and natural resource management, are often based on medium resolution satellite imagery. These data have difficulty in detecting trees in fragmented woodlands, and have significant omission errors in modified agricultural areas. High resolution imagery can better detect these trees, however, as most high resolution imagery is not normalised it is difficult to automate a tree classification method over large areas. The method developed here used an existing medium resolution map derived from either Landsat or SPOT5 satellite imagery to guide the classification of the high resolution imagery. It selected a spatially-variable threshold on the green band, calculated based on the spatially-variable percentage of trees in the existing map of tree cover. The green band proved more consistent at classifying trees across different images than several common band combinations. The method was tested on 0.5 m resolution imagery from airborne digital sensor (ADS) imagery across New South Wales (NSW), Australia using both Landsat and SPOT5 derived tree maps to guide the threshold selection. Accuracy was assessed across 6 large image mosaics revealing a more accurate result when the more accurate tree map from SPOT5 imagery was used. The resulting maps achieved an overall accuracy with 95% confidence intervals of 93% (90–95%), while the overall accuracy of the previous SPOT5 tree map was 87% (86–89%). The method reduced omission errors by mapping more scattered trees, although it did increase commission errors caused by dark pixels from water, building shadows, topographic shadows, and some soils and crops. The method allows trees to be automatically mapped at 5 m resolution from high resolution imagery, provided a medium resolution tree map already exists.     

Utilizing the Potential of World View −2 for Discriminating Urban and Vegetation Features Using Object Based Classification Techniques

With the availability of very high resolution multispectral imagery, it is possible to identify small features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. This paper demonstrates the potential of 8 bands capability of World View 2 satellite for better automated feature extraction and discrimination studies. Multiresolution segmentation and object based classification techniques were then applied for discrimination of urban and vegetation features in a part of Dehradun, Uttarakhand, India. The study demonstrates that scale, colour, shape, compactness and smoothness have a significant influence on the quality of image objects achieved, which in turn governs the classified result. The object oriented analysis is a valid approach for analyzing high spatial and spectral resolution images. World View 2 imagery with its rich spatial and spectral information content has very high potential for discrimination of the less varied varieties of vegetation.     

Using a Combination of Digital Aerial Photography and Satellite Remote Sensing to Assist Contemporary Restructuring in an Urban Area of South Africa

Abstract

The transition and restructuring process of urban South Africa are currently in the phase of identifying land development objectives. These objectives aim to integrate previously segregated areas through integrated development plans. This research aims firstly to identify and describe the historical development of the spatial form and structure of the secondary city and capital of the Northern Province, Pietersburg and its dispersed peripheral towns. Supervised classification of SPOT HRV multispectral imagery is used to support the theoretical explanation. Images from an airborne digital Kodak DCS 420 camera are used to provide training sites in the pre‐classification stages, and also provide field data to the process of post‐classification accuracy assessment. Secondly, SPOT HRV imagery is applied to identify the stark contrast in urban development between the city of Pietersburg and its surrounding former homeland towns. Both built and natural environmental aspects are investigated. In conclusion benefits and problems of assessing urban morphology and development in a developing country by means of a combination of satellite imagery and digital aerial photography are discussed.     

Using Maximum Likelihood (ML) and Maximum A Prior Probability (MAP) in Iterative Self‐Organizing Data (ISODATA)

Abstract

With the availability of high‐resolution satellite imagery featuring as high as 1 meter resolution in the panchromatic mode and 3‐meter resolution in the multispectral mode, there is an interest by many new commercial and public service applications such as cellular telephones coverage area design, urban/land cover planning, and real estate marketing to extract features from images automatically. To that end, the demand for unsupervised classification techniques is growing. In this paper, the Maximum Likelihood (ML) and Maximum A prior Probability (MAP) algorithms are used as decision rules to find boundaries of classes computed by the Iterative Self‐Organizing Data (ISOADATA) algorithm. Different satellite images with different resolutions were used to experiment with these algorithms. The results of comparing and analyzing the algorithms revealed that MAP‐ISODATA performed better than ML‐ISODATA even when the same initial matrix was used. It was shown that there was no significant difference between ML‐ISODATA and MAP‐ISODATA in terms of accuracy. It was also realized that better results could be obtained if homogenous initialization strategies were used.     

Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region

In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting.     

Earth observation-based multi-scale impact assessment of internally displaced person (IDP) camps on wood resources in Zalingei,Darfur

This study describes the development of a semi-automatic object-based image analysis approach for the detection and quantification of deforestation in Zalingei, Darfur, in consequence of the increasing concentration of refugees or internally displaced persons (IDPs) in the region. The classification workflow is based on a multi-scale approach, ranging from the analysis of high resolution SPOT-4 to very high resolution IKONOS and QuickBird satellite imagery between 2003 and 2008. The overall accuracy rates for the classification of the SPOT 4 data ranged from 92% up to 95%, while those for the QuickBird and IKONOS classification have shown values of 88 and 87%, respectively. The resulting trends in woody vegetation cover were compared with the development of the local population and the variability of precipitation. The results show that the strong increase in human population in the Zalingei IDP camps can be associated with considerable decrease in woody vegetation in the camp vicinity.     

Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS,Landsat 7 ETM+, SRTM and multiple Random Forests

Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil, the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery. In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape.The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eight landscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derived standard deviation in elevation.In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit.Burrows were successfully classified in all landscape units. In the ‘steppe on floodplain’ areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the ‘floodplain’ areas with a more heterogeneous vegetation cover, classification worked least well; there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively.In this study, an innovative stratification method using high- and medium resolution imagery was applied in order to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data set which can be used as input for epidemiological plague models. This is an important step in understanding the dynamics of plague.     

The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

Abstract

This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001–2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multi-temporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p=0.01.     

Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery

Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km²). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r² = 0.64, r² = 0.71, respectively). Best results were achieved for LSU (r² = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r² = 0.39 for LSU, r² = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.     

Distribution of Errors in a Classified Map of Satellite Data

Abstract

The output from any spatial data processing method may contain some uncertainty. With the increasing use of satellite data products as a source of data for Geographical Information Systems (GIS), there have been some major concerns about the accuracy of the satellite‐based information. Due to the nature of spatial data and remotely sensed data acquisition technology, and conventional classification, any single classified image can contain a number of mis‐classified pixels. Conventional accuracy evaluation procedures can report only the number of pixels that are mis‐classified based on some sampling observation. This study investigates the spatial distribution and the amount of these pixels associated with each cover type in a product of satellite data. The study uses Thematic Mapper (TM) and SPOT multispectral data sets obtained for a study area selected in North East New South Wales, Australia. The Fuzzy c‐Means algorithm is used to identify the classified pixels that contained some uncertainty. The approach is based on evaluating the strength of class membership of pixels. This study is important as it can give an indication of the amount of error resulting from the mis‐classification of pixels of specific cover types as well as the spatial distribution of such pixels. The results show that the spatial distribution of erroneously classified pixels are not random and varies depending on the nature of cover types. The proportions of such pixels are higher in spectrally less clearly defined cover types such as grasslands.     

Multidate image analysis of forest degradation in Equatorial Africa

Abstract

In this paper we process diachronic SPOT satellite images acquired with different viewing angles in order to assess the aerosol optical thickness (AOT) spatial distribution over Brescia.

This urban area, located in Northern Italy, is subject to frequent visibility‐reducing smog episodes. AOT was calculated by using the Differential Textural Analysis (DTA) code on a satellite data series consisting by one pollution‐free and various pollution‐loaded SPOT images. The resulting maps showed the horizontal distribution of AOT with a resolution of 500 metres. These maps can be readily integrated with the results obtained by mesoscale modelling, ground measurements, and respond to local scale application requirements. Satellite AOT retrieval compared successfully with available AOT ground‐based measurements and with pollution measurements in the ambient air. In this study the use of diachronic multiangle SPOT imagery allowed us to analyse the effect of the viewing angle variation on AOT retrieval accuracy based on the contrast reduction method.     

SPOT multispectral analysis for producing supraglacial debris‐load estimates for Batura glacier,Pakistan

Abstract

SPOT multispectral and panchromatic data were evaluated to determine their utility to detect debris‐load characteristics of the Batura Glacier located in the Karakoram Himalaya. Debris‐depth measurements, surface samples, and ground photography were obtained and used with satellite‐derived information to produce supraglacial debris‐load and discharge estimates. Visual analysis of panchromatic data indicated that structural characteristics of the glacier exhibited unique textures associated with surface structure characteristics. Multispectral analysis revealed that stratified unsupervised classification of principal components can be used to produce classifications depicting supraglacial lithology and shallow debris‐load variability. Debris‐load discharge estimates ranged from 48–97 x 103 m3 yr1. These results indicate that SPOT multispectral data may be used to produce reasonable quantitative estimates of debris‐load characteristics for glacier mass balance and regional denudation studies.     

Remote Sensing of Spatial and Temporal Vegetation Dynamics in Hispaniola: A Comparison of Haiti and the Dominican Republic

Abstract

Spatial and temporal vegetation contrasts between the nations of Haiti and the Dominican are analyzed using NDVI data derived from 30m resolution Landsat imagery and 8km resolution AVHRR imagery from the NOAA / NASA Pathfinder database. Analysis of vegetation dynamics in the Hispaniola border region indicates denser vegetation cover and a stronger correlation between elevation, slope, and NDVI on the Dominican side of the frontier. Temporal patterns of NDVI dynamics along the frontier suggest that changes in biomass are both more homogeneous and more extreme on the Haitian side. Analysis of 17 years of 8km resolution AVHRR imagery for the entire island of Hispaniola reveals consistently higher NDVI values for the Dominican Republic and a distinct intra‐annual pattern of mean monthly NDVI deviations that have important implications for future studies of vegetation dynamics in the region.     

A multi-resolution satellite imagery approach for large area mapping of ericaceous shrubs in Northern Quebec, Canada

Invasive ericaceous shrubs (e.g. Kalmia angustifolia, Rhododendron groenlandicum, Vaccinium spp.) may reduce the regeneration and early growth of black spruce (Picea mariana) seedlings, the most economically important boreal tree species in Quebec. Our study focused, therefore, on developing a method for mapping ericaceous shrubs from satellite images. The method integrates very high resolution satellite imagery (IKONOS) to guide classifiers applied to medium resolution satellite imagery (Landsat-TM). An object-oriented image classification approach was applied using Definiens eCognition software. An independent ground survey revealed 80% accuracy at the very high spatial resolution. We found that the partial use (70%) of classified polygons derived from the IKONOS images were an effective way to guide classification algorithms applied to the Landsat-TM imagery. The results of this latter classification (78.4% overall accuracy) were assessed by the remaining portion (30%) of unused very high resolution classified polygons. We further validated our method (65.5% overall accuracy) by assessing the correspondence of an ericaceous cover classification scheme done with a Landsat-TM image and results of our ground survey using an independent set of 275 sample plots. Discrimination of ericaceous shrub cover from other land cover types was achieved with precision at both spatial resolutions with producer accuracies of 87.7% and 79.4% from IKONOS and Landsat, respectively. The method is weaker for areas with sparse cover of ericaceous shrubs or dense tree cover. Our method is adapted, therefore, for mapping the spatial distribution of ericaceous shrubs and is compatible with existing forest stand maps.     

Multiple band digital orthophoto quadrangle data: A source for generating land use maps

Abstract

The purpose of this study was to investigate the use of color infrared‐digital orthophoto quadrangle (CIR‐DOQ) data to generate land use/land cover (LULC) maps and to incorporate them as data layers in geographic information systems (GIS) involving various resource management scenarios. The Danville 7.5‐minute quadrangle located in the southern part of Limestone and Morgan counties, Alabama, was used as the study site. Data for the special CIR‐DOQ were generated by scanning four 9x9 inch CIR aerial photographs at a uniform pixel sample grid of 25 microns resulting in 2 meters ground sample resolution. One‐half of the quadrangle was used to identify training sites for performing a supervised classification of the data and the other half to verify the accuracy of the classification. The CIR‐DOQ data were found to be adequate for using a supervised classification algorithm to differentiate major LULC classes, resulting in a classification accuracy of 93 percent. The superior spatial quality of the data over commençai satellite data affords resource managers an opportunity to more effectively study land cover and surface hydrological properties of an area, soil moisture and surface soil textures, as well as differentiate among vegetation species, using remote sensing techniques. However, caution must be exercised when using multispectral classification techniques to classify mosaicked CIRDOQ data because of the image enhancements used to generate the final product. In its present form, there are some limitations to the use of the data for performing spectral classifications. Hozvever, the high spatial resolution of the data enables even the novice resource planner to effectively use the data in visual interpretations of major LULC classes.     

A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America

Abstract

It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations. To better manage the natural resources, and to better understand the closely linked Earth systems, the concept of Digital Earth has been strongly promoted since US Vice President Al Gore's speech in 1998. One core element of Digital Earth is the use and integration of remote sensing data. Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use, but also to detect more subtle changes related for instance to climate change. To uncover global change effects on vegetation activity and phenology, it is important to establish high quality time series characterizing the past situation against which the current state can be compared. With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution. Particular emphasis was put on noise removal. Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale. Without temporal smoothing, subtle spatio-temporal patterns in vegetation composition, density and phenology would be hidden by atmospheric noise and undetected clouds. Such noise is immanent in data that have undergone solely a maximum value compositing. Within the present study, the Whittaker smoother (WS) was applied to a SPOT VGT time series. The WS balances the fidelity to the observations with the roughness of the smoothed curve. The algorithm is extremely fast, gives continuous control over smoothness with only one parameter, and interpolates automatically. The filtering efficiently removed the negatively biased noise present in the original data, while preserving the overall shape of the curves showing vegetation growth and development. Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data. Analysis of the data also revealed spatially consistent key phenological markers. Extracted seasonality parameters followed a clear meridional trend. Compared to the unfiltered data, the filtered time series increased the separability of various land cover classes. It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.     

单侧控制条件下SPOT5 HRG影像的定向试验

高分辨率卫星影像已经成为获取地理信息的重要数据源,而其定向是一项关键技术。本文利用单侧不同数量及分布的控制点对SPOT5 HRG影像进行了定向试验,探讨了在单侧控制条件下SPOT5 HRG影像的定向精度情况,为地面控制点获取较困难地区的影像定向提供了一条参考途径。