Estimation of chlorophyll‐a concentration and trophic states for inland lakes in Northeast China from Landsat TM data and field spectral measurements

Landsat TM data and field spectral measurements were used to evaluate chlorophyll‐a (Chl‐a) concentration levels and trophic states for three inland lakes in Northeast China. Chl‐a levels were estimated applying regression analysis in the study. The results obtained from the field reflectance spectra indicate that the ratio between the reflectance peak at 700 nm and the reflectance minimum at 670 nm provides a relatively stable correlation with Chl‐a concentration. Their determination of coefficients R ² is 0.69 for three lakes in the area. From Landsat TM data, the results show that the most successful Chl‐a was estimated from TM3/TM2 with R ² = 0.63 for the two lakes on 26 July 2004, from TM4/TM3 with R ² = 0.89 for the two lakes on 14 October 2004, and from the average of TM2, TM3 and TM4 with R ² = 0.72 for the three lakes tested on 13 July 2005. These results are applicable to estimate Chl‐a from satellite‐based observations in the area. We also evaluate the trophic states of the three lakes in the region by employing Shu's modified trophic state index (TSI_M) for the Chinese lakes' eutrophication assessment. Our study presents the TSI_M from different TM data with R ² more than 0.73. The study shows that satellite observations are effectively applied to estimate Chl‐a levels and trophic states for inland lakes in the area.     

Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data

We evaluated the potential of two novel thermally enhanced Landsat Thematic Mapper (TM)‐derived spectral indices for discriminating burned areas and for producing fire perimeter data (as a potential surrogate to digital fire atlas data) within two wildland fires (1985 and 1993) in ponderosa pine (Pinus ponderosa) forests of the Gila Wilderness, New Mexico, USA. Image‐derived perimeters (manually produced and classified from an index image) were compared to fire perimeters recorded within a digitized fire atlas. For each fire, the highest spectral separability was achieved using the newly proposed Normalized Burn Ratio‐Thermal (NBRT₁) index (M = 1.18, 1.76, for the two fires respectively). Correspondence between fire atlas and manually digitized fire perimeters was high. Landsat imagery may be a useful supplement to existing historical fire perimeters mapping methods, but the timing of the post‐fire image will strongly influence the separability of burned and unburned areas.     

Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models

This article focuses on retrieving the multi-scale crown closure (CC) of Moso bamboo forest using Système Pour l’Observation de la Terre (SPOT5) and Landsat Thematic Mapper (TM) satellite remotely sensed imagery based on the geometric-optical model and the artificial neural network (ANN) model. CC at local scale was first retrieved using the Li-Strahler geometric-optical model (LSGM) and images from an unmanned aerial vehicle (UAV). Then, multi-scale CC was retrieved using the Erf-BP model (a kind of back-propagation (BP) feed-forward neural network, which takes a Gaussian error function (Erf) as an activation function of the hidden layer) based on a combination of SPOT5 and Landsat TM images. The results show that by combining multi-source remotely sensed data, the CC of Moso bamboo forest can be retrieved at the local region, township area, and county scale with high accuracy using the Erf-BP model. Estimated values have a linear relationship with the observed values at a significance level of 0.05. The highest accuracy of the retrieval of CC (referred to as LSGM-UAV-CC) was observed at the local region based on LSGM and UAV, with the coefficient of determination (R²) of 0.63, followed by that at the township area with an R² of 0.0.55 based on LSGM-UAV-CC and SPOT5 data using the Erf-BP model (Erf-BP-SPOT5-CC), and that at the county scale with an R² of 0.54 based on Erf-BP-SPOT5-CC and Landsat TM data using the Erf-BP model (Erf-BP-TM-CC).     

Combining dendrochronological data and the disturbance index to assess Engelmann spruce mortality caused by a spruce beetle outbreak in southern Utah, USA

Forest disturbances such as bark beetle outbreaks are increasing in severity and extent across western North America. Classification of remote sensing imagery is a powerful way to analyze and detect large-scale disturbances. We used a temporal sequence of four Landsat TM images (1991, 1995, 1999, and 2003) to detect the spatiotemporal change in spectral response of Engelmann spruce (Picea engelmannii Parry ex. Engelm.) killed by an unprecedented spruce beetle outbreak in southern Utah, USA. After co-registration and masking out non-vegetation the Disturbance Index (DI) was calculated for each image. DI values associated with Engelmann spruce mortality, determined by comparing each image to a no outbreak baseline image, were then used to classify the images. Dendrochronologically determined dates of spruce death collected from across the outbreak area were used to assess the ability of the DI to accurately differentiate stands of dead spruce from live conifer forest. The overall classification accuracy of the DI varied from 80 to 82% while the accuracy to detect spruce beetle-killed spruce varied from 59 to 71%. Both user's and producer's accuracy to classify beetle infested stands increased over the temporal sequence of image dates. However, confusion matrix-derived statistics varied by image date. Consistent with previous studies, the spruce beetle outbreak began building in multiple, seemingly independent locations across the study area. Over time, areas attacked earlier in the outbreak enlarged and coalesced on the landscape.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R ² > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R ² > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R ²?=?0.78, accuracy?=?73%, root mean square error (RMSE)?=?0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy?=?79%, RMSE?=?0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.     

Seasonal reflectance trends of hemiboreal birch forests

MODIS, AVHRR and SPOT VEGETATION satellite images have recently been used to track coarse scale seasonal vegetation dynamics of boreal and temperate forests. However, the understanding of driving factors of reflectance seasonality at forest stand level is still in its infancy, and has only preliminarily been linked to, for example, forest structure or site fertility. We present results from a study on the seasonal reflectance trends of 145 hemiboreal birch stands in Estonia from budburst to initial senescence. A time series comprising 32 high resolution Landsat ETM+, TM and SPOT HRVIR, HRV images from April to September was assembled for analyzing empirical reflectance courses of birch stands. The most noteworthy seasonal reflectance dynamics were observed in the red and NIR channels, changes in the green and SWIR spectral channels were relatively small. The most stable period in stand reflectance in all the spectral channels occurred in midsummer i.e. when stand leaf area index (LAI) reached its highest level and changes in solar angle were the smallest. A twenty-day difference was observed between the reflectance development of birch stands growing on infertile and fertile sites. Next, to provide an explanation for the observed reflectance changes, we simulated the mean seasonal reflectance trajectories of the study stands at 10 day intervals for the same period using a radiative transfer model (FRT). Simulated seasonal reflectance courses for the different site fertility classes followed the general pattern of the measured courses. Simulation results indicated that the main driving factors for reflectance seasonality for all the site fertility classes in the red and green bands were stand LAI and leaf chlorophyll content, in the NIR band stand LAI, and in the SWIR band LAI and general water content. Finally, we discuss current limitations related to applying forest radiative transfer models in investigating the driving factors of seasonal reflectance changes in the boreal zone.     

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.     

Spectral characterization and regression-based classification of forest damage in Norway spruce stands in the Czech Republic using Landsat Thematic Mapper data

This study assessed the ability of Landsat Thematic Mapper (TM) sensor data to discriminate among three damage categories of Norway spruce in the Krusne Hory mountains using dichotomous logit regressions. Moderate and light damage stands, being the most spectrally similar, were separated with 83 per cent accuracy using TM1, TM4 and TM7. Moderate and heavy categories were best separated by TM3 (accuracy=88 per cent). Light and heavy damage classes were separated with up to 95 per cent accuracy. Ratios and indices did not improve the regression accuracies. The regression equations, when used to classify three categories of damage, accurately classified 71–75 per cent of Norway spruce stands.     

Changes in satellite-measured reflectances caused by thinning cuttings in Boreal forest

Reflectance changes caused by thinning cuttings in northern Sweden were measured using multitemporal Landsat TM data. The test area was dominated by Scots pine (Pinus sylvestris) mixed with Norway spruce (Picea abies) and birch (Betula spp). Landsat TM images from eight sequential summers were relatively calibrated pairwise using regression functions. For each combination of images from different years, local relative changes in digital numbers (DNs) were obtained from the differences between measured data from the late image and a prediction of these data based on the early image. These changes were scaled into units of reflectance using the radiative transfer code 5S. Between 19% and 57% of the basal area was removed in the thinning cuttings. This caused an increase of between 0.001 and 0.004 reflectance in the visible bands and around 0.01 in the middle infrared bands. The best single bands for discrimination of thinned stands from normally developed stands were, in order, TM5, TM7, and TM3. The reflectance changes in these bands increased with thinning grade and with the proportion of pine in the stand. In TM4, the thinning cuttings caused a decrease in reflectance that, to a large degree, could be explained by the proportion of deciduous forest removed. In detected thinning cuttings, around 50% of the variance in the thinning grade, basal area cut, or stem volume cut, could be explained by regression functions based on the TM data only. The reflectance changes caused by thinning cuttings were, in most bands, reduced to half between 4 and 5 years after cutting. The method used for calibration of local changes in DN to changes in reflectance allows data from different image acquisitions to be compared; thus it also may be suitable for creating a knowledge base containing reflectance changes caused by cuttings and local damages in forest.     

Remote sensing of biophysical variables in boreal forest stands of Picea mariana

Using two hybrid radiative transfer models to represent conifer canopies and stands, algorithms to infer several important structural parameters of stands of black spruce (Picea mariana), the most common boreal forest dominant, were developed and evaluated. Spectral mixture analysis and multi-spectral reflectance data for 31 black spruce stands of varying density and structure were used to infer the values for the areal proportions of sunlit canopy, sunlit background and shadow fraction, which we call radiometric elements, and the areal proportions of these radiometric elements were strongly related to leaf area index, biomass density, and annual above ground net primary productivity. The best overall correspondence between the radiometric elements and biophysical variables was found from the shadow fraction obtained with the cone-based canopy reflectance model corrected for variations in solar zenith angle.     

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.     

Post‐earthquake building collapse: a comparison of government statistics and estimates derived from SPOT HRVIR data

On 17 August 1999 at 3:02 a.m. local time the Izmit earthquake occurred on the North Anatolian Fault Zone (NAFZ) in north‐west Turkey. This earthquake caused considerable damage in the urban areas of Izmit, Adapazari (Sakarya), Golcuk and Yalova. This study used three different data sources to estimate the proportion of Adapazari that contained collapsed buildings: (i) government statistics on the number of collapsed buildings; (ii) the difference between pre‐ and post‐earthquake land cover estimated from classified SPOT HRVIR XI images; and (iii) land cover estimated from density‐sliced SPOT HRVIR Panchromatic image recorded after the earthquake. The results were similar at 16%, 16.1% and 15.5%, respectively. These were all slight overestimates; however, the remotely sensed estimates provided the spatial context of building collapse and in doing so highlighted areas of previously uncontrolled building.     

Discrimination of conifer height,age and crown closure classes using Landsat-5 TM imagery in the Canadian Northwest Territories

Empirical relationships between forest stand attributes and Landsat-5 Thematic Mapper spectral response were developed in order to assess its informational value in support of forest inventory operations in the Northwest Territories. An existing large-area classification procedure, based on a supervised methodology, has been able to generate classes of white spruce and jack pine. Within individual forest species groups, spectral variability related to differences in crown closure, height and age is of interest to forest managers in the region. The objective of this study was to determine the accuracy with which stands within the white spruce and jack pine classes could be further separated into two stand height classes (< 15 m and S 15 m), two age classes (< 100 years and S 100 years) and two crown closure classes (< 30% and S 30%) with a single (summer) Landsat-5 Thematic Mapper (TM) image. Discrimination generally improved with the addition of spectral texture measures where independently assessed accuracies ranged from 60 to 90%. A look-up table was devised for conifer-dominated areas (> 80% dominant species) which could subsequently be assigned for height, age and crown closure class values based on Landsat TM spectral response patterns.     

Measuring forest structure with terrestrial laser scanning

This paper investigates the application of a ground‐based laser scanning system for providing quantitative tree measurements in densely stocked plantation forests. A methodology is tested in Kielder Forest, northern England using stands of mature Sitka spruce (Picea sitchensis) and a structured mixture of Sitka spruce and lodgepole pine (Pinus contorta), standing at tree densities of 600 and 2800?stems?ha^?1 respectively. Three laser scans, two in the Sitka spruce and one in structured mixture, were collected using a Reigl Inc. LPM‐300VHS high‐speed laser scanner. Field measurements were recorded at the same time and included tree diameter at breast height (dbh) and tree height. These measurements were then compared with those derived from the scanner. The results demonstrate that accurate measurements of tree diameter can be derived directly from the laser scan point cloud return in instances where the sensor's view of the tree is not obstructed. Measurements of upper stem diameters, branch internodal distance and canopy dimensions can also be measured from the laser scan data. However, at the scanning spatial resolution selected, it was not possible to measure branch size. The level of detail that can be obtained from the scan data is dependent on the number and location of scans within the plot as well as the scanning resolution. Essentially, as the shadowing caused by tree density or branching frequency increases, the amount of useful information contained in the scan decreases.     

Model development and comparison to predict softwood and hardwood per cent cover using high and medium spatial resolution imagery

The recent availability of high spatial resolution multispectral scanners provides an opportunity to adapt existing methods and test models to derive spatially explicit forest type and per cent cover information at the Landsat pixel level. A regression modelling methodology was applied for scaling‐up high resolution (IKONOS) to medium spatial resolution satellite imagery (Landsat) to predict softwood and hardwood forest type and density (per cent cover) in a northern Maine study area. Regression relationships (63 different models) were developed and compared. The model variables included vegetation indices and several date (season) combinations of Landsat Enhanced Thematic Mapper Plus (ETM+) imagery (August, September, October and May).

A model incorporating all variables from four dates of Landsat ETM+ imagery produced the highest coefficient of variation in predicting both softwood (0.655) and hardwood cover (0.66). The addition of vegetation indices with the six ETM+ reflected bands did not significantly improve or detract from the regression relationships for any of the multi‐date or single date models examined. A two‐date combination of October and May variables provided an acceptable (and arguably more cost‐effective) model as the adjusted R ² value was 0.645 for softwood and 0.649 for hardwood. A significant result was that all single‐date models produced inferior results with a sharp drop in adjusted R ², compared with the multi‐date seasonal models. This research has demonstrated that the regression models including multi‐date variables produce good results and can provide spatially explicit forest type and stand structure data that has been difficult or infeasible to obtain from medium spatial resolution imagery using traditional classification methods.     

Detecting an invasive shrub in a deciduous forest understory using late‐fall Landsat sensor imagery

Landsat TM and ETM+ imagery was used to distinguish areas of high vs. low cover of Amur honeysuckle (Lonicera maackii), taking advantage of the late leaf retention of this invasive shrub. L. maackii cover was measured in eight stands and compared to 15 Landsat 5 TM and Landsat 7 ETM+ images from spring and autumn dates from 1999 to 2006. Jeffries–Matusita (JM) distance calculations showed potential separability between high vs. low/zero cover classes of L. maackii on some late fall images. The Soil Adjusted Atmospheric Resistant Vegetation Index (SARVI2) revealed higher levels of green biomass in high L. maackii cover plots than low/zero cover plots for November images only. These findings justify further investigation of the effectiveness of late fall images to map the historical spread of L. maackii and other forest understory invasives with similar phenology.     

Comparison of MODIS and Landsat TM5 images for mapping tempo–spatial dynamics of Secchi disk depths in Poyang Lake National Nature Reserve,China

Landsat has successfully been applied to map Secchi disk depth of inland water bodies. Operational use for monitoring a dynamic variable like Secchi disk depth is however limited by the 16‐day overpass cycle of the Landsat system and cloud cover. Low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) image captured twice a day could potentially overcome these problems. However, its potential for mapping Secchi disk depth of inland water bodies has so far rarely been explored. This study compared two image sources, MODIS and Landsat Thematic Mapper (TM), for mapping the tempo–spatial dynamics of Secchi disk depth in Poyang Lake National Nature Reserve, China. Secchi disk depths recorded at weekly intervals from April to October in 2004 and 2005 were related to 5 Landsat TM and 22 MODIS images respectively. Two multiple regression models including the blue and red bands of Landsat TM and MODIS respectively explained 83% and 88% of the variance of the natural logarithm of Secchi disk depth. The standard errors of the predictions were 0.20 and 0.37 m for Landsat TM and MODIS‐based models. A high correlation (r = 0.94) between the predicted Secchi disk depth derived from the two models was observed. A discussion of advantages and disadvantages of both sensors leads to the conclusion that MODIS offers the possibility to monitor water transparency more regularly and cheaply in relatively big and frequently cloud covered lakes as is with Poyang Lake.     

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.     

On the correlation of indices of vegetation and surface temperature over south-eastern Australia

Abstract

Relationships between radiant surface temperature (T _R) and vegetation indices for scenes with equal areas of forest and agricultural land use were studied using a Landsat Thematic Mapper (TM) scene during spring and a NOAA-Advanced Very High Resolution Radiometer (AVHRR) scene during summer. The relationships between TR and the Normalized Difference (ND) index of vegetation for agricultural land use were different from those for forests. At the same T _R, the forests had lower near infrared reflectance. This caused the ND of forests to fall below the T _R/ND relationships formed by agricultural land use. This difference between forest and agricultural land use did not exist when visible reflectance (VIS) was used as the index of vegetation. When the two land use systems were combined VIS accounted for about 86 per cent of the variance in T _R. The slope of the relationships between VIS and T _R differed for TM and AVHRR scenes. This was explained by differences in the T _R and VIS reflectance of surfaces with near-zero evaporation. These surfaces were predominantly bare soil in the TM scene and senesced vegetation in the AVHRR scene.