Detecting water quality parameters in the Norfolk Broads,U.K., using Landsat imagery

Abstract

The water quality parameters chlorophyll-a, total phosphorus, Secchi disk depth, suspended solids, salinity and temperature in the Norfolk Broads have been studied using Landsat TM data. An empirical approach of relating TM data with ground referenced data for these parameters through regression analysis was employed. Significant relationships were established between them. These models were used to predict and map these parameters in 27 or the Norfolk Broads. All the predicted values are consistent with the available general knowledge about these Broads     

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.     

Remote sensing of phytoplankton An attempt from the Landsat Thematic Mapper

Abstract

An atmospheric correction scheme using Landsat Thematic Mapper (TM) bands 1, 2 and 4 was developed and the data corrected for atmospheric effects due to Rayleigh scattering and aerosols. Secondly phytoplankton pigment mapping was achieved through substitution of corrected radiances in TM bands 1 and 2 into a bio-optical algorithm developed for the study area off Azhikal in the Arabian Sea. A C map showing near-surface concentrations of phytoplankton pigments (mgm_?3)and a K map showing the pigment distribution (m?1) in one attenuation length were generated.     

A preliminary assessment of Landsat TM imagery for mapping vegetation and sediment distribution in the Wash estuary

Abstract

Analysis of Landsat Thematic Mapper (TM) image of 14 May 1984 has shown that such data can be used to survey vegetation and sediment distributions in the intertidal zone of the Wash estuary at a spatial detail comparable with current methods practised by the.Nature Conservancy Council. Multispectral classification of this TM image showed good separation of salt-marsh vegetation communities which had recently been surveyed by the Nature Conservancy Council and for which reliable training data could be taken. The sensitivity of classification performance, using both parametric and non-parametric algorithms, to apparently minor differences in phenology at training site locations demonstrates two requirements for improved salt-marsh classification. They are the need for strict definition of training data and that TM wave bands 2, 3, 4 and 5 provide suitable spectral vectors for classifying intertidal environments.     

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.     

Forest regeneration on abandoned clearances in central Amazonia

A time-series of Landsat and SPOT sensor data was used to approximate the age of tropical forests regenerating on abandoned agricultural land north of Manaus, Brazil, and to estimate the period of active land use prior to abandonment. Based on field data, two distinct regeneration pathways, dominated by the pioneer genera Cecropia and Vismia, respectively, were described, with the former regenerating on the least intensively used sites. Forests of mixed species composition and lacking numerical dominance by pioneer species were also observed. Transformed Divergence Analysis of Landsat Thematic Mapper (TM) radiance data revealed that forests of varying age and following different regeneration pathways were best discriminated using mid infrared (1.55-1.74 w m) wavelengths. As rates of carbon sequestration by forests vary with age and regeneration pathway, the potential exists for refining spatial estimates of the carbon balance of tropical forests regenerating on abandoned agricultural lands.     

Use of cartographic data and Landsat TM images to determine land use change in the vicinity of Mexico City

Evaluation of change in land use is important for planning further development in populated areas. Here we attempt to determine the growth of urban areas in the vicinity of Mexico City, using a 1993 Landsat Thematic Mapper (TM) image and cartographic data contained in maps published by the Instituto Nacional de Estadistica Geografia e Informatica (INEGI 1975, 1983). The area occupied by urban areas in 1975 and 1983 was quantified using raster images generated by scanning the maps. Supervised classification processes were applied to a 1993 Landsat TM image in bands 1, 2, 3, 4, 5 and 7, of the area of Chalco. The image was pre-processed and then processed to enhance the spectral response of the surface materials. The different land cover types that characterise distinct land uses in the study area were identified in the image and an overall classification accuracy of 82% was estimated using aerial photographs from the Chalco area. The resulting evaluation of the land use changes in the Chalco urban area was plotted, and a growth greater than 14% per year was estimated.     

A comparison of SPOT and Landsat-TM data for use in conducting inventories of forest resources

Abstract

SPOT multispectral (XS) and Landsat Thematic Mapper (TM) digital data were studied in an attempt to evaluate the use of this data in detailed assessments of forest conditions. Forest type, basal area, and age class information were collected from 256 sample sites within an intensively managed 80000acre experimental forest in North Carolina, U.S.A. A comparison of the SPOT and TM data with the sample site information showed that XS3, the near-infrared waveband, and TM bands 2, 3, 4, 5, and 7 were significantly correlated with basal area. Age class was not found to be significantly correlated with any of the three SPOT XS wavebands. TM bands 2, 3, 4, 5, and 7 were, however, shown to be significantly correlated with age class. Although significant, the correlation coefficients between the TM or SPOT waveband data and basal area or age class were low (<0.65).

Six forest cover types, and an additional water category, were selected as the basis of a land cover classification system for use with the TM and SPOT data. Verification of the classification of the seven cover types using the SPOT XS waveband data resulted in an estimated accuracy of 74.4 per cent. Classification accuracy was slightly reduced (70.8 per cent) when the TM wavebands corresponding to the SPOT XS bands were used as inputs to the classifier. When each of the six visible and reflective infrared TM wavebands were included in the classification process overall accuracy increased to 885 per cent.     

Comparison of a new algorithm with the supervised classifications

In this study, a new classification algorithm in which only the selected pixels have been attempted to be classified (selected pixels classification: SPC) has been introduced and compared with the well known supervised classification methods such as maximum likelihood, minimum distance, nearest neighbour and condensed nearest neighbour. To examine the algorithm, Landsat Thematic Mapper (TM) data have been used to classify the crop cover in the selected region. It is clearly demonstrated that the SPC method has the higher accuracy with comparable CPU times.     

Statistical problems in the discrimination of land cover from satellite images: a case study in lowland Britain

Abstract

This paper discusses underlying statistical problems in identifying land classes from satellite imagery. Problems in assessing accuracy of automatic classifiers are illustrated using results from analysis of two Landsat Thematic mapper (TM) subscenes collected for the same areas within Cambridgeshire in the summer and autumn of 1984. Problems arising from between-site variability of training sites are discussed. Use of rejection thresholds is also discussed. Recommendations are given for improving choice of training data to attempt lo overcome some of these problems.     

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: A Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow/ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSII_TM=(TM3-TM5)/(TM3+TM5); for VGT data, NDSII is calculated as NDSII_VGT=(B2-MIR)/(B2+MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSII_VGT.     

Technical note GPS location data An aid to satellite image analyses of poorly mapped regions

Abstract

In inaccessible and highly heterogeneous areas where aerial photography and accurate up-to-date topographic maps are unavailable and where suitable features for ground-based navigation by triangulation are absent, accurately locating ground truth sites and then integrating the field data with digital imagery is often extremely difficult. The advent of the satellite global positioning system (GPS) offers a solution to this problem. Over a 6 month period a battery powered, solar recharged, backpack mounted GPS was used to collect the precise location data essential to the ground truth component of a Landsat Thematic Mapper (TM) landcover classification of the Ituri rain forest of northeastern Zaire. Three-dimensional locations were readily obtained in forest openings > 0·125 ha where the angle to the horizon did not exceed 50^° and canopy closure was less than 30 per cent. GPS location data are presently being used to reference a TM scene geographically and to assign pixels to appropriate landcover classes accurately.     

Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation

Thirty‐five stands of mature, closed canopy black spruce (Picea mariana), white spruce (Picea glauca) and balsam fir (Abies balsamea) in Prince Albert National Park, Saskatchewan, were assessed for cumulative defoliation caused by eastern spruce budworm (Choristoneura fumiferana). Multitemporal Landsat 5 TM images (15 June 1992 and 18 July 2004) and a single‐date SPOT 4 HRVIR (high resolution visible and infrared) image (19 August 2004) were obtained over these stands. Correlation analysis suggested that the strength of the relationship between the defoliation and various vegetation indices was generally moderate. The SPOT HRVIR indices were more highly correlated to cumulative defoliation than the Landsat indices, and the multitemporal Landsat TM index outperformed the single‐date Landsat TM index. These results may help in the design of defoliation assessment procedures that integrate satellite remotely‐sensed data and aerial sketch mapping techniques.     

An IHS-based change detection approach for assessment of urban expansion impact on arable land loss in China

China is experiencing a drastic loss of arable land as a result of the continuing process of rapid urbanization. This loss could have an adverse impact not only on China's sustainable development but also on the world food market. To provide a robust and economical tool for evaluating the impact of this process, we developed an IHS (Intensity, Hue, Saturation)-based approach for detecting the change from arable land to urban land by using Landsat TM and SPOT Pan images in different years. This approach takes full advantage of the colour distortion related to the changes in objects after IHS and reverse IHS transformation. Based on case studies in Guangzhou city and Nanyang city, choosing Landsat TM bands 7, 4, and 3 as the input RGB component and employing an exponential contrast stretch and a high-pass filter in SPOT Pan pre-processing led to the greatest change in detection accuracy.     

Maximum likelihood classification,optimal or problematic? A comparison with the nearest neighbour classification

Abstract

The maximum likelihood and the nearest neighbour classification algorithms are reviewed, particularly from the point of view of user/analyst requirements. The two algorithms were put to use for the classification or Landsat TM data of agricultural scenes and accuracy with respect to ‘ground truth’ was evaluated using different parametric settings. Results show that within the maximum likelihood classification, accuracies and errors can vary to a considerable degree depending on the formation of the statistical classes from the training data. More interestingly, it was found that the nearest neighbour algorithm produced higher accuracies and was judged to be more robust, but it has computer implementation problems with high data dimensionality.     

Estimation of tropical forest biomass using Landsat TM imagery and permanent plot data in Xishuangbanna,China

Because of its complexity, it is very difficult to obtain information about distribution of biomass in tropical forests. This article describes the estimation of tropical forest biomass by using Landsat TM and forest plot data in Xishuangbanna, PR China. The method includes several steps. First, the biomass for each forest permanent plot is calculated by using field inventory data. Second, Landsat TM images are geometrically corrected by using topographic maps. Third, a map of the tropical forest is obtained by using data from a variety of sources such as Landsat TM, digital elevation model (DEM), temperature and precipitation layers and expert knowledge. Finally, the biomass and carbon storage of each forest vegetation type in the forest map is calculated by using the tropical forest map and the forest plot biomass GIS database. In the study area, forest area accounts for 57% of the total 1.7?×?10⁶ hectares. The total forest biomass is 2.0?×?10⁸ tonne. It is shown that the forest vegetation map, the forest biomass and the forest carbon storage can be obtained by effectively integrating Landsat TM, ancillary data including DEM, temperature and precipitation, forest permanent plots and knowledge using the method proposed here.     

Neotectonic analysis of Mendha river basin,Rajasthan, India

Abstract

The study presented here examined the tectonically active Mendha River basin, Rajasthan, India. Landsat TM data in bands 2, 3 and 4 were used in this study. The neotectonic features were extracted through the digital analysis of the principal component (PC) and directional filtered images. However, the regional view of the study area was examined through the false colour composite (FCC). A map of the neotectonic features was generated by incorporating the limited field observations and this yielded the extensions of the neotectonic features.     

Determining the rate of forest conversion in Mato Grosso,Brazil, using Landsat MSS and AVHRR data

Abstract

AVHRR-LAC thermal data and Landsat MSS and TM spectral data were used to estimate the rate of forest clearing in Mato Grosso, Brazil, between 1981 and 1984. The Brazilian state was stratified into forest and non-forest. A list sampling procedure was used in the forest stratum to select Landsat MSS scenes for processing based on estimates of fire activity in the scenes. Fire activity in 1984 was estimated using AVHRR-LAC thermal data. Slate-wide estimates of forest conversion indicate that between 1981 and 1984, 353966 ha ±77 000 ha (0·4 percent of the state area) were converted per year. No evidence of reforestation was found in this digital sample. The relationship between forest clearing rate (based on MSS-TM analysis)and fire activity (estimated using AVHRR data)was noisy (R²= 0·41). The results suggest that AVHRR data may be put to better use as a stratification tool rather than as a subsidiary variable in list sampling.     

A non-parametric,supervised classification of vegetation types on the Kaibab National Forest using decision trees

Traditional land classification techniques for large areas that use Landsat Thematic Mapper (TM) imagery are typically limited to the fixed spatial resolution of the sensors (30 m). However, the study of some ecological processes requires land cover classifications at finer spatial resolutions. We model forest vegetation types on the Kaibab National Forest (KNF) in northern Arizona to a 10-m spatial resolution with field data, using topographical information and Landsat TM imagery as auxiliary variables. Vegetation types were identified by clustering the field variables total basal area and proportion of basal area by species, and then using a decision tree based on auxiliary variables to predict vegetation types. Vegetation types modelled included pinyon-juniper, ponderosa pine, mixed conifer, spruce- and deciduous-dominated mixes, and openings. To independently assess the accuracy of the final vegetation maps using reference data from different sources, we used a post-stratified, multivariate composite estimator. Overall accuracy was 74.5% (Kappa statistic = 49.9%). Sources of error included differentiating between mixed conifer and spruce-dominated types and between openings in the forest and deciduous-dominated mixes. Overall, our non-parametric classification method successfully identified dominant vegetation types on the study area at a finer spatial resolution than can typically be achieved using traditional classification techniques.     

Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images

Routine acquisition of Landsat 5 Thematic Mapper (TM) data was discontinued recently and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) has an ongoing problem with the scan line corrector (SLC), thereby creating spatial gaps when covering images obtained during the process. Since temporal and spatial discontinuities of Landsat data are now imminent, it is therefore important to investigate other potential satellite data that can be used to replace Landsat data. We thus cross-compared two near-simultaneous images obtained from Landsat 5 TM and the Indian Remote Sensing (IRS)-P6 Advanced Wide Field Sensor (AWiFS), both captured on 29 May 2007 over Los Angeles, CA. TM and AWiFS reflectances were compared for the green, red, near-infrared (NIR), and shortwave infrared (SWIR) bands, as well as the normalized difference vegetation index (NDVI) based on manually selected polygons in homogeneous areas. All R ² values of linear regressions were found to be higher than 0.99. The temporally invariant cluster (TIC) method was used to calculate the NDVI correlation between the TM and AWiFS images. The NDVI regression line derived from selected polygons passed through several invariant cluster centres of the TIC density maps and demonstrated that both the scene-dependent polygon regression method and TIC method can generate accurate radiometric normalization. A scene-independent normalization method was also used to normalize the AWiFS data. Image agreement assessment demonstrated that the scene-dependent normalization using homogeneous polygons provided slightly higher accuracy values than those obtained by the scene-independent method. Finally, the non-normalized and relatively normalized ‘Landsat-like’ AWiFS 2007 images were integrated into 1984 to 2010 Landsat time-series stacks (LTSS) for disturbance detection using the Vegetation Change Tracker (VCT) model. Both scene-dependent and scene-independent normalized AWiFS data sets could generate disturbance maps similar to what were generated using the LTSS data set, and their kappa coefficients were higher than 0.97. These results indicate that AWiFS can be used instead of Landsat data to detect multitemporal disturbance in the event of Landsat data discontinuity.