Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).     

Trajectory-based warm season grassland mapping in Missouri prairies with multi-temporal ASTER imagery

Tallgrass prairie in North America has been largely converted to croplands and cool season grasslands. In Missouri, only 0.5% of the tallgrass prairie remains in a form of isolated prairie islands. This study sought to delineate prairie-native warm season grass (WSG) from cool season grass (CSG) using five ASTER images acquired on 03/11/2008, 05/12/2007, 07/12/2007, 08/16/2007 and 10/19/2007. Temporal trajectories of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were extracted to examine temporal variation of WSG and CSG grasslands in a growth cycle. It was found that the spring-summer period revealed maximal spectral differences between these two grass types. CSG reached peak NDVI in May while WSG tended to have peak NDMI in July. NDVI was more useful than NDMI in summer-fall. The NDVI trends in this period varied with both phenology and grassland treatments such as haying and grazing, which provided additional information in WSG/CSG delineation. A hierarchical decision tree was developed to delineate WSG and CSG grasslands in a 3-tier process. The WSG lands including those publically conserved prairies were identified in the lower Osage Plain in southwest Missouri. The accuracies were about 80% and could be improved with more frequent satellite observations. The trajectory-based decision tree in this study provides a robust approach of long-term WSG/CSG mapping.     

Assessment of grassland degradation near Lake Qinghai,West China,using Landsat TM and in situ reflectance spectra data

The severity of grassland degradation near Lake Qinghai, West China was assessed from a Landsat Thematic Mapper (TM) image in conjunction with in situ samples of per cent grass cover and proportion (by weight) of unpalatable grasses (PUG) collected over 1?m² sampling plots. Spectral reflectance at each sampling plot was measured with a spectrometer and its location determined with a Global Positioning System (GPS) receiver. After radiometric calibration, the TM image was geometrically rectified. Ten vegetation indices were derived from TM bands 3 and 4, and from the spectral reflectance data at wavelengths corresponding most closely to those of TM3 and TM4. Regression analyses showed that NDVI and SAVI are the most reliable indicators of grass cover and PUG, respectively. Significant relationships between TM bands-derived indices and in situ sampled grass parameters were established only after the former had been calibrated with in situ reflectance spectra data. Through the established regression models the TM image was converted into maps of grass cover parameters. These maps were merged to form a degradation map at an accuracy of 91.7%. It was concluded that TM imagery, in conjunction with in situ grass samples and reflectance spectra data, enabled the efficient and accurate assessment of grassland degradation inside the study area.     

Using MERIS fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes

In this paper we evaluate the potential of ENVISAT–Medium Resolution Imaging Spectrometer (MERIS) fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes. A series of MERIS fused images (15 spectral bands; 25 m pixel size) is created using the linear mixing model and a Landsat Thematic Mapper (TM) image acquired over the Netherlands. First, the fused images are classified to produce a map of the eight main land-cover types of the Netherlands. Subsequently, the maps are validated using the Dutch land-cover/land-use database as a reference. Then, the fused image with the highest overall classification accuracy is selected as the best fused image. Finally, the best fused image is used to compute three vegetation indices: the normalized difference vegetation index (NDVI) and two indices specifically designed to monitor vegetation status using MERIS data: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI).

Results indicate that the selected data fusion approach is able to downscale MERIS data to a Landsat-like spatial resolution. The spectral information in the fused images originates fully from MERIS and is not influenced by the TM data. Classification results for the TM and for the best fused image are similar and, when comparing spectrally similar images (i.e. TM with no short-wave infrared bands), the results of the fused image outperform those of TM. With respect to the vegetation indices, a good correlation was found between the NDVI computed from TM and from the best fused image (in spite of the spectral differences between these two sensors). In addition, results show the potential of using MERIS vegetation indices computed from fused images to monitor individual fields. This is not possible using the original MERIS full resolution image. Therefore, we conclude that MERIS–TM fused images are very useful to map heterogeneous landscapes.     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

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.     

Characterization and classification of South American land cover types using satellite data

Abstract

Various methods are compared for carrying out land cover classifications of South America using multitemporal Advanced Very High Resolution Radiometer data. Fifty-two images of the normalized difference vegetation index (NDVI) from a 1-year period are used to generate multitemporal data sets. Three main approaches to land cover classification are considered, namely the use of the principal components transformed images, the use of a characteristic curves procedure based on N DVI values plotted against time, and finally application of the maximum likelihood rule to multitemporal data sets. Comparison of results from training sites indicates that the last approach yields the most accurate results. Despite the reliance on training site figures for performance assessment, the results are nevertheless extremely encouraging, with accuracies for several cover types exceeding 90 per cent.     

Monitoring northern mixed prairie health using broadband satellite imagery

The mixed prairie in Canada is characterized by its low to medium green vegetation cover, high amount of non‐photosynthetic materials, and ground level biological crust. It has proven to be a challenge for the application of remotely sensed data in extracting biophysical variables for the purpose of monitoring grassland health. Therefore, this study was conducted to evaluate the efficiency of broadband‐based reflectance and vegetation indices in extracting ground canopy information. The study area was Grasslands National Park (GNP) Canada and the surrounding pastures, which represent the northern mixed prairie. Fieldwork was conducted from late June to early July 2005. Biophysical variables—canopy height, cover, biomass, and species composition—were collected for 31 sites. Two satellite images, one SPOT 4 image on 22 June 2005, and one Landsat 5 TM image on 14 July 2005, were collected for the corresponding time period. Results show that the spectral curve of the grass canopy was similar to that of the bare soil with lower reflectance at each band. Consequently, commonly used vegetation indices were not necessarily better than reflectance when it comes to single wavelength regions at extracting biophysical information. Reflectance, NDVI, ATSAVI, and two new coined cover indices were good at extracting biophysical information.     

Crown closure estimation of oak savannah in a dry season with Landsat TM imagery: Comparison of various indices through correlation analysis

In this paper, we assess the capability of Landsat Thematic Mapper (TM) for oakwood crown closure estimation in Tulare County, California. Measurements made from orthorectified aerial photographs for the same area were used as a reference. The linear relationship between crown closure and digital values of each band of the TM image was examined. TM Band 3 had the highest correlation ( @ = m 0.828; R 2 = 0.687) with crown closure measurements. The simple ratio (SR) and the normalized difference vegetation index (NDVI) were generated for correlation analysis and only NDVI showed better correlation ( A = 0.836; R 2 = 0.699) than use of single bands. An additional index (NIR N - R N )/(NIR N + R N ), called NDVIN, was experimented, NDVISQ ( N = 2) and NDVICUB ( N = 3) showed some improvements over SR and NDVI ( A = 0.855; R 2 = 0.732 for N = 3). Through multiple regression with all six bands, we found that there was a considerable amount of improvement in variability explanation over any individual band or index tested ( R 2 = 0.803). NIR, red and blue bands were able to adequately model crown closure as using all the six TM bands ( R 2 = 0.802). Principal component analysis (PCA) and Kauth-Thomas (K-T) transform were applied to reduce multi-collinearity among bands. The third principal component and greenness in K-T transform showed similar effects to those of NDVI. Transformation of digital numbers (DNs) to radiances kept the results of single band and multiple band estimation the same, and did not improve the index estimation very much. A simple radiometric correction of the TM image improved results for the NDVI ( A = 0.840; R 2 = 0.705) and NDVISQ estimation ( A = 0.861; R 2 = 0.741), but worsened estimation results of single band and multiple bands.     

Comparing direct image and wavelet transform-based approaches to analysing remote sensing imagery for predicting wildlife distribution

In this study we tested the ability to predict the probability of elephant (Loxodonta africana) presence in an agricultural landscape of Zimbabwe based on three methods of measuring the spatial heterogeneity in vegetation cover, where vegetation cover was measured using the Landsat Thematic Mapper (TM)-derived normalized difference vegetation index (NDVI). The three methods of measuring spatial heterogeneity were: one wavelet-derived spatial heterogeneity measure; and two direct image measures. The wavelet-derived spatial heterogeneity measure consists of the intensity, which measures the maximum contrast in the vegetation cover, and the dominant scale, which determines the scale at which this intensity occurs. The two direct image measures use the NDVI average and the NDVI coefficient of variation (NDVIcv). The results show that the wavelet-derived spatial heterogeneity significantly explains 80% of the variance in elephant presence compared with 60% and 48% variance explained by the NDVI average and NDVIcv, respectively. We conclude that the wavelet transform-based approach predicts elephant distribution better than the direct image measures of spatial heterogeneity.     

NDVI-derived land cover classifications at a global scale

Phenological differences among vegetation types, reflected in temporal variations in the Normalized Difference Vegetation Index (NDVI) derived from satellite data, have been used to classify land cover at continental scales. Extending this technique to global scales raises several issues: identifying land cover types that are spectrally distinct and applicable at the global scale; accounting for phasing of seasons in different parts of the world; validating results in the absence of reliable information on global land cover; and acquiring high quality global data sets of satellite sensor data for input to land cover classifications. For this study, a coarse spatial resolution (one by one degree) data set of monthly NDVI values for 1987 was used to explore these methodological issues. A result of a supervised, maximum likelihood classification of eleven cover types is presented to illustrate the feasibility of using satellite sensor data to increase the accuracy of global land cover information, although the result has not been validated systematically. Satellite sensor data at finer spatial resolutions that include other bands in addition to NDVI, as well as methodologies to better identify and describe gradients between cover types, could increase the accuracy of results of global land cover data sets derived from satellite sensor data.     

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation.Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.     

Classifying tropical deciduous vegetation: a comparison of multiple approaches in Popa Mountain Park,Myanmar

Although several studies have reported that rule-based methods are better than other image classification methods, no study has quantified their performance for tropical deciduous vegetation classification. We compared rule-based and maximum likelihood classification (MLC) approaches in classifying tropical deciduous vegetation in Popa Mountain Park, Myanmar. Classification was primarily based on Thematic Mapper (TM) bands of multi-season Landsat images, normalized difference vegetation indices (NDVIs), NDVI differences, mean NDVI and elevation (advanced spaceborne thermal emission and reflection radiometer digital elevation model (Aster DEM)). We used two main approaches for classification, a single-step approach in which all vegetation types were classified in one procedure, and a two-step approach in which forest and non-forest were discriminated first and then forest was classified into additional classes. Each of those approaches was conducted with and without elevation under the rule-based and MLC approaches, yielding eight separate methods. The two-step approaches generated more accurate results and all classifications improved markedly when elevation was included. The rule-based two-step with elevation approach produced the best overall accuracy and reliability.     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

城市地区ETM和QUICKBIRD影像NDVI值比较研究

通过比较ETM和Quickbird两个数据的归一化插值植被指数,来判断它们在反映植被覆盖度方面的效果。结果表明,在尺度较小并且地物景观比较复杂的城市地区,高分辨率的Quickbird影像能够更好地观测到小范围地区的NDVI值。对于城市地区,由于绿地面积相对较小,因此最好利用高分辨率的Quickbird数据,而对于大尺度或植被景观比较单一地区,二者的差异不明显。由于ETM影像的成本相对较低而且波谱范围更加广泛,故在大尺度地区使用TM影像监测植被变化更加合适。     

LandsatTM在矿区生态环境动态监测中的应用

以湖北大冶为研究区，采用多时相陆地卫星遥感图像，通过不同波段组合，以及铁矿指数(iron oxide)和归一化差异植被指数(NDVI)等的应用，在建立一定精确的分类模板的基础上，采用最大似然法执行监督分类，得到了具有较高精度的分类结果图。最后对不同时相分类结果图中各类地物的比较，定量分析了矿区生态环境的动态变化，并着重讨论了30m分辨率的陆地卫星影像在矿区生态环境监测中的作用及不足。     

The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees

Estimating the extent of tropical rainforest types is needed for biodiversity assessment and carbon accounting. In this study, we used statistical comparisons to determine the ability of Landsat Thematic Mapper (TM) bands and spectral vegetation indices to discriminate composition and structural types. A total of 144 old-growth forest plots established in northern Costa Rica were categorized via cluster analysis and ordination. Locations for palm swamps, forest regrowth and tree plantations were also acquired, making 11 forest types for separability analysis. Forest types classified using support vector machines (SVM), a theoretically superior method for solving complex classification problems, were compared with the random forest decision tree classifier (RF). Separability comparisons demonstrate that spectral data are sensitive to differences among forest types when tree species and structural similarity is low. SVM class accuracy was 66.6% for all forest types, minimally higher than the RF classifier (65.3%). TM bands and the Normalized Difference Vegetation Index (NDVI) combined with digital elevation data notably increased accuracies for SVM (84.3%) and RF (86.7%) classifiers. Rainforest types discriminated here are typically limited to one or two categories for remote sensing classifications. Our results indicate that TM bands and ancillary data combined via machine learning algorithms can yield accurate and ecologically meaningful rainforest classifications important to national and international forest monitoring protocols.     

Equivalent water thickness in savanna ecosystems: MODIS estimates based on ground and EO-1 Hyperion data

The Brazilian savanna biome, known locally as the Cerrado, with an area of about 2 million km² and marked by a conspicuous seasonality, comprises a vertically structured mosaic of ecosystem types, ranging from grassland to tropical dry forests. The Cerrado is a major agricultural frontier in Brazil, with nearly 50% of its original vegetative cover already converted to pastures and crop fields. Such large-scale conversion has severely affected regional runoff, river discharge and the atmosphere water transfer from soil reservoirs through vegetation. In this study, we used multitemporal Earth Observing-1 (EO-1) Hyperion hyperspectral imagery to derive canopy water content (validated by ground truth measurements), whose estimates were regionally extrapolated, over the entire Cerrado biome, based on the normalized difference vegetation index (NDVI) Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13Q1. MODIS-based canopy-level equivalent water thickness (EWT_C) values were significantly distinct for each of the major anthropogenic and natural Cerrado land-cover types, at both the beginning and end of the dry season, and were correlated with land surface temperatures (LSTs). This method provides reasonable estimates of precipitable canopy water. Potential applications of EWT_C estimates based on moderate resolution imagery include early fire warnings and validation and constraining of regional hydrological models.     

On the relationship between monthly mean and maximum-value composite normalized vegetation indices

Abstract

A relationship between the maximum-value composite and monthly mean normalized difference vegetation index (NDVI) is derived statistically using data over the U.S. Great Plains during 1986. The monthly mean NDVI is obtained using a simple nine-day compositing technique based on the specifics of the scan patterns of the NOAA-9 Advanced Very High Resolution Radiometer (AVHRR). The results indicate that these two quantities are closely related over grassland and forest during the growing season. It is suggested that in such areas a monthly mean NDVI can be roughly approximated by 80 per cent of the monthly maximum NDVI, the latter being a standard satellite data product. The derived relationship was validated using data for the growing season of 1987.     

Landscape-scale characterization of cropland in China using Vegetation and Landsat TM images

In this landscape-scale study we explored the potential for multitemporal 10-day composite data from the Vegetation sensor to characterize land cover types, in combination with Landsat TM image and agricultural census data. The study area (175 km by 165 km) is located in eastern Jiangsu Province, China. The Normalized Difference Vegetation Index (NDVI ) and the Normalized Difference Water Index (NDWI ) were calculated for seven 10-day composite (VGT-S10) data from 11 March to 20 May 1999. Multi-temporal NDVI and NDWI were visually examined and used for unsupervised classification. The resultant VGT classification map at 1 km resolution was compared to the TM classification map derived from unsupervised classification of a Landsat 5 TM image acquired on 26 April 1996 at 30 m resolution to quantify percent fraction of cropland within a 1 km VGT pixel; resulting in a mean of 60% for pixels classified as cropland, and 47% for pixels classified as cropland/natural vegetation mosaic. The estimates of cropland area from VGT data and TM image were also aggregated to county-level, using an administrative county map, and then compared to the 1995 county-level agricultural census data. This landscape-scale analysis incorporated image classification (e.g. coarse-resolution VGT data, fineresolution TM data), statistical census data (e.g. county-level agricultural census data) and a geographical information system (e.g. an administrative county map), and demonstrated the potential of multi-temporal VGT data for mapping of croplands across various spatial scales from landscape to region. This analysis also illustrated some of the limitations of per-pixel classification at the 1 km resolution for a heterogeneous landscape.