Preliminary evaluation of the relationships between SPOT-1 HRV data and forest stand parameters

Abstract

Image data recorded from SPOT-1 were correlated with five forest stand parameters: mean percentage canopy cover, tree density, mean tree diameter at breast height (DBH), mean tree height and sub-compartment age. With the exception of percentage canopy cover, the correlation coefficients for the near-infrared waveband (S3) were all significant at the 99 per cent confidence level. The correlation coefficients for the red (S2) and green (S1) wavebands were lower and this may be due to the low dynamic range of the data for forest canopies in these wavebands.     

Capability of SPOT XS data in producing detailed land cover maps at the urban-rural periphery

The capability of SPOT multi-spectral (XS) data in generating detailed land cover maps at the urban-rural fringe is critically evaluated using two images, one recorded in summer and another in winter. The factors affecting the mapping accuracy are also identified and assessed in this study. Covering an area of 90km2 in South Auckland, New Zealand, two subscenes of SPOT XS images were used to map 10 categories of land cover at level II of the Anderson scheme with an overall accuracy of 76.2 and 81.4 per cent from the winter and summer data, respectively. The higher accuracy achieved using the summer image is due to the higher distinctiveness of vegetative covers in summer. The main limiting factors are identified as the high heterogeneity of land-use patterns commonly found at the urban periphery, poor representativeness of training samples caused by the presence of the same land cover element across a diverse range of land cover classes, and varying conditions of vegetative covers.     

Updating land cover classification using a rule‐based decision system

This paper proposes a land cover classification methodology in agricultural contexts that provides satisfactory results with a single satellite image per year acquired during the growing period. Our approach incorporates ancillary data such as cropping history (inter‐annual crop rotations), context (altitude, soil type) and structure (parcels size and shape) to compensate for the lack of radiometric data resulting from the use of a single image. The originality of the proposed method resides in the three successive steps used: S1: per‐pixel classification of a single SPOT XS image with a restricted number of land cover classes (RL) chosen to ensure good accuracy; S2: conversion of RLs into a per‐parcel classification system using ancillary parcel boundaries; and S3: parcel allocation using exhaustive land cover classes (EL) and its refinement through the application of decision rules. The method was tested on a 120?km² area (Sousson river basin, Gers, France) where exhaustive knowledge of land cover for two successive years allowed complete validation of our method. It allocated 87% of the parcels with a 75% accuracy rate according to the exhaustive list (EL). This is a satisfactory result obtained with one SPOT XS image in a small agricultural parcel context.     

Remote sensing techniques for mangrove mapping

Different approaches to the classification of remotely sensed data of mangroves are reviewed, and five different methodologies identified. Landsat TM, SPOT XS and CASI data of mangroves from the Turks and Caicos Islands, were classified using each method. All classifications of SPOT XS data failed to discriminate satisfactorily between mangrove and non-mangrove vegetation. Classification accuracy of CASI data was higher than Landsat TM for all methods, and more mangrove classes could be discriminated. Merging Landsat TM and SPOT XP data improved visual interpretation of images, but did not enhance discrimination of different mangrove categories. The most accurate combination of sensor and image processing method for mapping the mangroves of the eastern Caribbean islands is identified.     

Canonical correlation analysis of coniferous forest spectral and biotic relations

Canonical correlation analysis was used to examine the relations between the six reflective Thematic Mapper bands and six forest structural variables for 70 lodgepole pine forest stands in Yellowstone National Park, U.S.A. Two significant canonical variate pairs were extracted, accounting for 96·4 per cent of the total information in the overall canonical correlation analysis. Results of the canonical redundancy analysis indicate that 78 per cent of the overall unstandardized variance in spectral data is explained by the first two spectral canonical variates, while the first and second biotic canonical variates explain 59 per cent and 5·9 per cent of the raw variance in the spectral data. The first two biotic canonical variates collectively explain 59 per cent of the raw variance in the biotic data, and the first and second spectral canonical variates explain 41 per cent and 6 per cent of the raw variance in the biotic data, respectively. Height, live basal area, leaf area index (LAI), and size diversity are highly intercorrelated and act in combination to affect the overall reflectance, or brightness, of a forest stand. Overstory live density and understory total living cover relate strongly to stand greenness, particularly TM band 4.     

Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study

A comparison of agricultural crop maps from independent field-based classifications of the Satellite Pour l'Observation de la Terre (SPOT) 4 multispectral (XS), SPOT5 XS, IKONOS XS, QuickBird XS and QuickBird pan-sharpened (PS) images is presented. An agricultural area within the north-west section of Turkey was analysed for field-based crop identification. The SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird images were collected in similar climatic conditions during July and August 2004. The classification of each image was carried out separately on a per-field basis on all bands and the coincident bands that are green, red and near-infrared (NIR). To examine the effect of filtering on field-based classification, the images were each filtered using the 3?×?3, 5?×?5, 7?×?7 and 9?×?9 mean filter and the filtered bands were also classified on per-field basis. For the unfiltered images, IKONOS XS provided the highest overall accuracies of 88.9% and 88.1% for the all-bands and the coincident bands classifications, respectively. On average, IKONOS XS performed slightly better than QuickBird XS and QuickBird PS, while it outperformed SPOT4 XS and SPOT5 XS. The use of filtered images in field-based classification reduced the accuracies for SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird XS. The results of this study indicate that smoothing images prior to classification does not improve the accuracies for the field-based classification. On the contrary, the accuracies for the filtered QuickBird PS images indicated a slight improvement. On the whole, both IKONOS and QuickBird images produced quite promising results for field-based crop mapping, yielding overall accuracies above 83%.     

NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa

Crop Normalized Difference Vegetation Index (NDVI) time profiles and crop acreage estimates were derived from the application of linear mixture modelling to Advanced Very High Resolution Radiometer (AVHRR) data over a test area in the southern part of the Pampa region, Argentina. Bands 1 and 2 from seven AVHRR scenes (June to January 1991) were combined to produce fraction images of winter crops, summer crops and pastures. A Landsat Thematic Mapper (TM) scene of the region was classified and superimposed to the AVHRR Local Area Coverage (LAC) data by means of a correlation technique. Each class signature was extracted by regressing the AVHRR response on the cover types proportions, estimated from Landsat-TM data, over sets of calibration windows. The crop NDVI profiles were hence derived from the class signatures in bands 1 and 2. These profiles appeared consistent with the cover types, but variability depending on the set of windows was noted. The assessment of the class signatures was indirectly accomplished through the subpixel classifications of the AVHRR data, performed using the different sets of class spectra. Although some discrepancies between AVHRR and Landsat–TM estimates were observed at the individual window level, the classification results compared quite well on a regional scale with Landsat–TM estimates: crop acreage was estimated to an overall accuracy ranging from 89 to 95 per cent according to the spectra used in the classification. Definitely, the proposed methodology should permit a better exploitation of the temporal resolution of AVHRR data in both the areas of yield prediction and vegetation classification. Furthermore, the perational application of such a methodology for crop monitoring will undoubtedlybe facilitated with the coming sensor systems such as the ModerateResolution Imaging Spectroradiometer (MODIS), the SPOT Vegetation Monitoring Instrument or the ‘Satelite Argentino Cientifico’ (SAC–C).     

Land cover discrimination potential of radar multitemporal series and optical multispectral images in a Mediterranean cultural landscape

The aim of the present study is (1) to evaluate the performances of two series of European Remote Sensing (ERS) Synthetic Aperture Radar (SAR) images for land cover classification of a Mediterranean landscape (Minorca, Spain), compared with multispectral information from Système Pour l'Observation de la Terre (SPOT) and Landsat Thematic Mapper (TM) sensors, and (2) to test the synergy of SAR and optical data with a fusion method based on the Demspter–Shafer evidence theory, which is designed to deal with imprecise information. We have evaluated as a first step the contribution of multitemporal ERS data and contextual methods of classification, with and without filtering, for the discrimination of vegetation types. The present study shows the importance of time series of the ERS sensor and of the vectorial MMSE (minimum mean square error) filter based on segmentation for land cover classification. Fifteen land cover classes were discriminated (eight concerning different vegetation types) with a mean producer's accuracy of 0.81 for a five-date time series within 1998, and of 0.71 for another four-date time series for 1994/1995. These results are comparable to those from SPOT XS images: 0.69 for July, 0.67 for October (0.85 for July plus October), and also from TM data (0.81). These results are corroborated by the kappa coefficient of agreement. The fusion between the 1994 series of ERS and XS (July), based on a derived method of the Dempster–Shafer evidence theory, shows a slight improvement on overall accuracies: +0.06 of mean producer's accuracy and +0.04 of kappa coefficient.     

Point accuracy of a non-parametric method in estimation of forest characteristics with different satellite materials

The reference sample plot (RSP) method is a distance-weighted k nearest neighbour estimation method, which allows simultaneous interpretation of several variables. In the RSP method, the k spectrally nearest field plots are looked at separately for each unknown pixel, and the area weight of the unknown pixel is divided as a function of the spectral distances to the nearest plots. The RSP method was examined in a forest inventory for estimating stem volumes by tree species groups using different satellite materials. Two methods were tested both in searching for and weighting the nearest field plots. Euclidean distance functions worked steadily with all the volume variables studied. The other distance measure tested was based on regression modelling. With more than 15 plots, both covariance weighting and inverse distance weighting gave similar results. Considering the field data of this study, the suitable number of the nearest plots in plotwise estimation appeared to be between 10 and 15 plots. With Landsat TM, SPOT XS and SPOT P, the differences in standard errors were minor. When combined TM and SPOT P were used, the plotwise standard error of total volume was still over 60 per cent.     

The use of the Minnaert correction for land‐cover classification in mountainous terrain

Land‐cover classifications in mountainous terrain are often hampered by the topographic effect. Several strategies can be pursued to correct for this. A traditional approach is to use training areas for the same land‐cover class for different topographic positions and later merge those into one class. Other solutions involve topographic corrections, such as a Minnaert correction. In this study the classification result of the traditional training‐area approach was compared with the classification result of a Minnaert‐corrected image. In order to derive the Minnaert constants, a SPOT XS scene of the Santa Monica Mountains, USA, was divided into three visually relatively homogeneous regions. Eighty per cent of the pixels were assigned the same land cover in both classifications. Differences in classification were mainly in the section of the image that had more diverse land cover than in the more homogeneous chaparral‐covered eastern section. This supports previous findings that the Minnaert constant needs to be derived for individual land‐cover classes. The findings also suggest that after the Minnaert correction the resulting classification is comparable to the classification obtained using a more traditional approach.     

Vegetation mapping of a tropical freshwater swamp in the Northern Territory,Australia: A comparison of aerial photography,Landsat TM and SPOT satellite imagery

The tropical wetland environments of northern Australia have ecological, social, cultural and economic values. Additionally, these areas are relatively pristine compared to the many other wetland environments in Australia, and around the world, that have been extensively altered by humans. However, as the remote northern coastline of Australia becomes more populated, environmental problems are beginning to emerge that highlight the need to manage the tropical wetland environments. Lack of information is currently considered to be a major factor restricting the effective management of many ecosystems and for the expansive wetlands of the Northern Territory, this is especially the case, as these areas are generally remote and inaccessible. Remote sensing is therefore an attractive technique for obtaining relevant information on variables such as land cover and vegetation status. In the current study, Landsat TM, SPOT (XS and PAN) and large-scale, true-colour aerial photography were evaluated for mapping the vegetation of a tropical freshwater swamp in Australia's Top End. Extensive ground truth data were obtained, using a helicopter survey method. Fourteen cover types were delineated from 1:15 000 air photos (enlarged to 1:5000 in an image processing system) using manual interpretation techniques, with 89% accuracy. This level of detail could not be extracted from any of the satellite image data sets, with only three broad land-cover types identified with accuracy above 80%. The Landsat TM and SPOT XS data provided similar results although superior accuracy was obtained from Landsat, where the additional spectral information appeared to compensate in part for the coarser spatial resolution. Two different classification algorithms produced similar results.     

Cover Airborne MSS data processing for forest classification

Abstract

Four SPOT HRV images of the same area of East Anglia, acquired between February and September 1986, have been evaluated at the National Remote Sensing Centre for their potential use in agricultural land cover mapping. Spectral coincidence plots were used in feature selection. Information from single images contained a high level of spectral confusion between cover types. Vegetation index images and original data were used in supervised maximum likelihood classification. Higher classification accuracies were achieved using the original data than the vegetation indices. An overall classification accuracy of 71 per cent for 10 land cover types was improved to 88 per cent by reducing the number of classes. Although the imagery acquired for the study did not correspond well to key dates in the crop calendar, the broad land cover categories, cereal crops, field crops (sugar beet and vegetables), grass land and broadleaved woodlands could be mapped from SPOT. Using vegetation indices from the whole scene, a map of land cover has been produced for an administrative district within the scene. Comparison with simulated Thematic Mapper data indicates greater crop discrimination is provided in the mid-infrared part of the spectrum.     

Discriminating between cool season and warm season grassland cover types in northeastern Kansas

This study assesses the ability of multitemporal Landsat Thematic Mapper (TM) data and the normalized difference vegetation index (NDVI) to spectrally separate grazed cool season and warm season grassland cover types in Douglas County, Kansas. Biophysical data collected during the summer of 1997 suggest that differences in the per cent of total living vegetation cover, per cent of senescent vegetation, and proportion of forb cover between the two grassland cover types could make cool season and warm season grassland cover types spectrally distinct. The results show that the two grassland cover types were spectrally different in several spring (May) and mid-summer (July) bands, but not in any fall (September) bands. Furthermore, the two grassland cover types could be discriminated with a high level of accuracy. Accuracy assessments of the three single dates showed that the mid-summer (July) image and NDVI discriminated between the grassland cover types most accurately (81.8%). The multitemporal TM and NDVI data did not improve the spectral discrimination of the two grassland cover types over the mid-summer image or NDVI and had classification accuracy levels of 63.6% and 68.2%, respectively.     

Subpixel measurement of tropical forest cover using AVHRR data

Abstract

Tropical forest assessment using data from the Advanced Very High Resolution Radiometer (AVHRR) may lead to inaccurate estimates of forest cover in regions of small subpixel forest or non-forest patches and in regions where the pattern of clearance is particularly convoluted. Test sites typifying these two patterns were chosen in Ghana and Rondonia, respectively. To capture the subpixel proportions of forest cover, a linear mixture model was applied to two AVHRR test images over the test sites. The model produced image outputs in which pixel intensities indicated the proporton of forest cover per km². For comparison, supervised maximum likelihood classifications were also performed. The outputs were assessed against classified Landsat TM scenes, converted to proportions maps and coregistered to the AVHRR images. An empirical method was applied for determining the critical forest cover per km² needed for an AVHRR pixel to be classified as forest. The critical values exceeded 50 per cent, indicating a tendency for AVHRR classification to underestimate forest cover. This was confirmed by comparing estimates of total forest cover obtained from the AVHRR and TM classifications. In the case of Ghana, a more accurate estimate of forest cover was obtained from the AVHRR mixture model than from the classification. Both mixture model outputs were found to be well correlated with those from Landsat TM. Further work should test the robustness of the approach adopted here when applied to much larger areas.     

Assessment of environmental impacts of river basin development on the riverine forests of eastern Kenya using multi-temporal satellite data

The utility of Landsat MSS (Multispectral Scanner) and SPOT XS data in monitoring the impacts of river basin development on a riverine forest located in the lower Tana River Basin of eastern Kenya was evaluated. Land cover change maps derived from Landsat MSS indicated little change in total forest area between 1975 and 1984. Land cover change maps derived from SPOT XS data indicated a 27% decline in forest area between 1989 and 1996. Mean patch size and area-perimeter ratio of the closed riverine forest remained virtually unchanged whereas these parameters for the open forest class decreased by 31% and 4% respectively. In addition, the average extent of the open riverine forest from the river channel declined by about 200 m between 1989 and 1996. This decline was attributed to decreased extent of floods along the floodplain following construction of dams in the upper river basin, and increased exploitation of the forests for fuelwood, especially in the vicinity of the established Bura Irrigation and Settlement Project. The greater lateral movement observed in the location of the river channel for the 1975-1985 period, compared to the 1985-1996 period, was also attributed to construction of dams in the upper river basin.     

Mapping Arctic tundra vegetation types using digital SPOT/HRV-XS data A preliminary assessment

Abstract

Multispectral (XS) image data recorded by the High Resolution Visible (HRV) sensor aboard the SPOT-1 satellite are being evaluated for the mapping of Arctic tundra vegetation in the Arctic Foothill Province of Alaska. This research is part of a current ecosystems study that requires an efficient means for mapping vegetation types over large areas. Conventional spectral-based image classification techniques were applied to SPOT/HRV-XS data from a single date. The unique characteristics of the vegetation cover (mainly tussock tundra) and illumination conditions of the location necessitated a detailed examination of classification approaches that have generally been applied in mid-latitude studies. Preliminary results suggest that areal estimates of Arctic tundra vegetation types can be made accurately (±2·5 per cent per category), but maps generated by classifying spectral features of SPOT/HRV-XS data alone arc unsuitably accurate (56 per cent). This is partly due to the high occurrence of relatively small vegetation parcels, determined by measuring the characteristic lengths of vegetation parcels from a ‘ground reference’ map covering the same area as the SPOT/HRV-XS subscene.     

基于Fuzzy ARTMAP神经网络的高分辨率图象土地覆盖分类及其评价

主要讨论了基于Fuzzy ARTMAP神经网络的高分辨率遥感图象土地覆盖分类方法及其实践．首先介绍了Fuzzy ARTMAP神经网络的原理，然后用SPOT XS图象试验数据进行土地覆盖分类．分类结果与传统的最大似然监督分类(MLC)、反馈式(Back Propagation，BP)神经网络的分类结果进行了比较．通过抽取500个样点对3种分类结果进行精度评价表明，Fuzzy ARTMAP神经网络相对其他两种方法，分类精度均有不同程度的改善，具有更好的分类结果，总分类精度比MLC和BP算法分别提高17．41％、7．32％．最后，对不同分类方法对于土地覆盖分类结果的影响进行了评价和分析．试验表明，Fuzzy ARTMAP神经网络用于高分辨图象土地覆盖分类研究可以获得相对较好的分类结果．     

Classification of SPOT HRV imagery and texture features

Abstract

Spatial co-occurrence matrices were computed for a SPOT HRV multispectral image for a moderate-relief environment in eastern Canada. The texture features entropy and inverse difference moment were used with the spectral data in landcover classification, and substantive increases in accuracy were noted. These range from 10 per cent for exposed bedrock to over 40 per cent in forest and wetland classes. The average classification accuracies were increased from 511 per cent (spectral data alone) to 86.7 per cent (spectral data plus entropy measured in band 2 and inverse difference moment in band 3). Classes that are homogeneous on the ground were characterized adequately by spectral tone alone, but classes containing mixed vegetation patterns or strongly related to structure were characterized more accurately by using a mixture of spectral tone and texture.     

TM digital processing of a tropical forest region in southeastern Mexico

A humid forest in the neotropical area of Los Tuxtlas, in southeastern Mexico has been used as a test area (900km²) for classification of landscape and vegetation by means of Landsat Thematic Mapper (TM) data, aerial photography and 103 ground samples. The area presents altitudinal variations from sea level to 1640m, providing a wide variety of vegetation types. A hybrid (supervised/unsupervised) classification approach was used, defining spectral signatures for 14 clustering areas with data from the reflective bands of the TM. The selected clustering areas ranged from vegetation of the highlands and the rain forest to grassland, barren soil, crops and secondary vegetation. The digital classification compared favourably with results from aerial photography and with those from a multivariate analysis of the 103 ground data. The statistical evaluation (error matrix) of the classified image indicated an overall 84·4 per cent accuracy with a kappa coefficient of agreement of 0·83. A geographical information system (GIS) was used to compile a land unit and a vegetation map. The TM data allowed for delineation of boundaries in the land unit map, and for a finer differentiation of vegetation types than those identified during field work. Digital value patterns of several information classes are shown and discussed as an indirect guide of the spectral behaviour of vegetation of highlands, rain forest, secondary vegetation and crops. The method is considered applicable to the inventory of other forested areas, especially those with significant variations in vegetation.     

Synergistic effects of combined Landsat-TM and SIR-B data for forest resources assessment

Abstract

Three different incidence angle data sets, obtained by the Shuttle Imaging Radar-B in October 1984 over a forested area in northern Florida, were combined with Landsat-5 Thematic Mapper data, to create a digitally registered 10 channel optical/microwave data set. The work discussed in this paper involved the analysis of the data obtained by the two sensors separately and in combination, to determine if there are synergistic effects obtained through the simultaneous use of data obtained from both the optical and microwave portion of the spectrum. The radar data were filtered with a low-pass filter to eliminate the speckle noise. Classifications of the TM, SIR-B and combined TM +SIR-B data sets were performed with both per-point and contextual classifiers. The results showed that filtered radar data can be used to classify accurately major cover types (i.e., pine forest, swamplands and radar smooth targets) and that the contextual classifier provided better classification performance. The combined TM and SIR-B data provided statistically improved classification performances compared to classifications from the three incidence angle SIR-B data, or the TM data alone. A four band subset (TM-2, TM-4, TM-5, and SIR-B 28°) of the 10 channels of the combined TM and SIR-B data set provided higher classification performances (91 per cent overall performance) than the 10 channel data set (86 per cent overall performance).