Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances

Vegetation indices and transformations have been used extensively in forest change detection studies. In this study, we processed multitemporal normalized difference moisture index (NDMI) and tasseled cap wetness (TCW) data sets and compared their statistical relationships and relative efficiencies in detecting forest disturbances associated with forest type and harvest intensity at five, two and one year Landsat acquisition intervals. The NDMI and TCW were highly correlated (>0.95 r²) for all five image dates. There was no significant difference between TCW and NDMI for detecting forest disturbance. Using either a NDMI or TCW image differencing method, when Landsat image acquisitions were 5 years apart, clear cuts could be detected with nearly equal accuracy compared to images collected 2 years apart. Partial cuts had much higher commission and omission errors compared to clear cut. Both methods had 7-8% higher commission and 12-22% higher omission error to detect hardwood disturbance when it occurred in the first year of the 2-year interval (as compared to 1-year interval). Softwood and hardwood change detection errors were slightly higher at 2-year Landsat acquisition intervals compared to 1-year interval. For images acquired 1 and 2 years apart, NDMI forest disturbance commission and omission errors were slightly lower than TCW. The NDMI can be calculated using any sensor that has near-infrared and shortwave bands and is at least as accurate as TCW for detecting forest type and intensity disturbance in biomes similar to the Maine forest, particularly when Landsat images are acquired less than 2 years apart. Where partial cutting is the most dominant harvesting system as is currently the case in northern Maine, we recommend images collected every year to minimize (particularly omission) errors. However, where clear cuts or nearly complete canopy removal occurs, Landsat intervals of up to 5 years may be nearly as accurate in detecting forest change as 1 or 2 year intervals.     

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.     

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.     

Multi-temporal and multi-source cartography of the glacial cover of Nevado Coropuna (Arequipa,Peru) between 1955 and 2003

This study reports on the glacial cover evolution of the Nevado Coropuna between 1955 and 2003, based on Peruvian topographic maps and satellite images taken from the Landsat 2 and 5 multispectral scanner (MSS), Landsat 5 Thematic Mapper (TM) and Landsat 7 (ETM+). The normalized difference snow index has been applied to these images to estimate the glacierized area of Coropuna. The satellite-based results show that the glacier area was 105 ± 16 km² in 1975 (Landsat 2 MSS), which then reduced to 96 ± 15 km² in 1985 (Landsat 5 MSS), 64 ± 8 km² in 1996 (Landsat 5 TM) and 56 ± 6 km² in 2003 (Landsat 5 TM). Altogether, between 1955 and 2003, Coropuna lost 66 km² of its glacial cover, which represents a mean retreat of 1.4 km² year^?1, that is, a loss of 54% in 48 years (11% loss per decade). The maximum rate of retreat occurred during the 1980s and 1990s, a phenomenon probably linked with the pluviometric deficit of El Niño events of 1983 and 1992.     

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.     

Near‐infrared discrimination of leafless saltcedar in wintertime Landsat TM

To test a hypothesis that leafless riparian canopies enable accurate multi‐spectral discrimination of saltcedar (Tamarix ramosissima Ledeb.) from other native species, winter Landsat TM5 data (16 November 2005) were analysed for a reach of the Arkansas River in Colorado, USA. Supporting spectroscopic analysis confirmed that saltcedar could not easily be discriminated from other riparian vegetation using TM5 data when in‐leaf, but bare branches could be easily distinguished due to much lower reflectance than other riparian cover. Use of TM Band 4 (B4) allowed differentiation of wintertime saltcedar into four qualitative density classes judged from high‐resolution low‐oblique aerial photography: high (76%–100%), medium (51%–75%), low (16%–50%), and none (0%–15%). Spectral overlap was removed from the B4 saltcedar classification using TM Band 5 (B5) thresholds to eliminate low‐reflectant wet areas and higher‐reflectant multi‐year darkened weed canopies. The accuracy of a classification algorithm that used B5 thresholds followed by a B4 density slice was judged against high‐resolution aerial photography as providing 98% discrimination of saltcedar cover from other riparian cover and about 90% discrimination of the qualitative density classes. Applying this method to the 2835 km² riparian corridor study area, 1298 km² (45.78%) was identified as containing saltcedar, with over 43% having medium or greater density.     

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.     

Defining the Landsat TM/ETM+ and CHRIS/PROBA spectral regions in which turbidity can be retrieved in inland waterbodies using field spectroscopy

This article describes the results obtained by an existing campaign in which in situ spectroradiometric measurements using a GER1500 field spectroradiometer, Secchi disk depth, and turbidity measurements (using a portable turbidity meter) were acquired at Asprokremmos Reservoir in Paphos District, Cyprus. Field spectroradiometric and water quality data span 18 sampling campaigns during the period May 2010–October 2010. By applying several regression analyses between ‘In-Band’ mean reflectance values against turbidity values for all spectral bands corresponding to Landsat TM/ETM+ (Bands 1 to 4) and CHRIS/PROBA (Bands A1 to A62), the highest correlation was found for Landsat TM/ETM+ Band 3 (R² = 0.85) and for CHRIS/PROBA Bands A30 to A32 (R² = 0.90).     

Possibility investigation of tree diversity mapping using Landsat ETM+ data in the Hyrcanian forests of Iran

Lack of data often limits understanding and management of biodiversity in forested areas. Remote sensing imagery has considerable potential to aid in the monitoring and prediction of biodiversity across many spatial and temporal scales. In this paper, we explored the possibility of defining relationships between species diversity indices and Landsat ETM+ reflectance values for Hyrcanian forests in Golestan province of Iran. We used the COST model for atmospheric correction of the imagery. Linear regression models were implemented to predict measures of biodiversity (species richness and reciprocal of Simpson indices) using various combinations of Landsat spectral data. Species richness was modeled using the band set ETM5, ETM7, DVI, wetness and variances of ETM1, ETM2 and ETM5 (adjusted R² = 0.59, RMSE = 1.51). Reciprocal of Simpson index was modeled using the band set NDVI, brightness, greenness, variances of ETM2, ETM5 and ETM7 (adjusted R² = 0.459 RMSE = 1.15). The results demonstrated that spectral reflectance from Landsat can be used to effectively model tree species diversity. Predictive map derived from the presented methodology can help evaluate spatial aspects and monitor tree species diversity of the studied forest. The methodology also facilitates the evaluation of forest management and conservation strategies in northern Iran.     

The effect of soil salinity on the use of the universal triangle method to estimate saline soil moisture from Landsat data: application to the SMAPEx-2 and SMAPEx-3 campaigns

In this article, a method based on UTM called salinity-based soil moisture content (S_SMC) is developed. Since the soil moisture depends on the soil salinity (SS) in semi-arid regions, the S_SMC method employs the SS as an effective and augmented variable in conventional UTM to estimate SMC in these areas. In calibration step, initially, a linear regression model between the land surface temperature (LST), the normalized difference vegetation index (NDVI), and the SS is applied using in situ measurements to assess the influence of the SS in SMC estimation. Then, a non-linearity model is conducted through insertion of more terms in the linear equation and an optimal model of S_SMC is yielded. Moreover, the SS is obtained using a linear model from two selected salinity indices derived from Landsat images and in situ measurements. In estimation step, the LST, NDVI, and the SS are obtained using Landsat data. The S_SMC method is evaluated in the Soil Moisture Active Passive Experiment (SMAPEx)-2 and SMAPEx-3 campaigns in wet and dry conditions, respectively, over two scenes of Landsat images. The results demonstrated that the S_SMC method is appropriate in non-irrigated areas. In these areas, the S_SMC method improves R² (coefficient of determination) from 22% to 65% in SMAPEx-2 and from 24% to 50% in SMAPEx-3. Moreover, the results have shown that the SMC can be estimated at satellite level with a root mean square error of 0.06 and 0.02 (m³ m^?3) in wet and dry condition, respectively. Therefore, the SS is a key parameter to adjust conventional UTM to improve the SMC estimation by the S_SMC method.     

Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain,China

A method is presented for bi‐directional reflectance distribution function (BRDF) parametrization for topographic correction and surface reflectance estimation from Landsat Thematic Mapper (TM) over rugged terrain. Following this reflectance, albedo is calculated accurately. BRDF is parametrized using a land‐cover map and Landsat TM to build a BRDF factor to remove the variation of relative solar incident angle and relative sensor viewing angle per pixel. Based on the BRDF factor and radiative transfer model, solar direct radiance correction, sky diffuse radiance and adjacent terrain reflected radiance correction were introduced into the atmospheric‐topographic correction method. Solar direct radiance, sky diffuse radiance and adjacent terrain reflected radiance, as well as atmospheric transmittance and path radiance, are analysed in detail and calculated per pixel using a look‐up table (LUT) with a digital elevation model (DEM). The method is applied to Landsat TM imagery that covers a rugged area in Jiangxi province, China. Results show that atmospheric and topographic correction based on BRDF gives better surface reflectance compared with sole atmospheric correction and two other useful atmospheric‐topographic correction methods. Finally, surface albedo is calculated based on this topography‐corrected reflectance and shows a reasonable accuracy in albedo estimation.     

Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance

PROSAIL is a combination of the leaf optical properties spectra (PROSPECT) model and the scattering by arbitrarily inclined leaves (SAIL) canopy bidirectional reflectance model. When modelling forest canopy reflectance using the PROSAIL radiative transfer model, the sensitivities of parameters can affect the modelling accuracy. Traditionally, sensitivities have been assessed using local sensitivity analysis (LSA); however, drawbacks to this approach include a lack of consideration for coupled effects between different parameters. In this study, parameter sensitivities in the PROSAIL model were calculated using two global sensitivity analysis (GSA) methods (the Extended Fourier Amplitude Sensitivity Test (EFAST) method and the Morris method), field measurements, and Landsat 5 Thematic Mapper (TM) data for a Moso bamboo forest. The results of GSA were compared with those of LSA in order to identify the key parameters impacting the Moso bamboo forest canopy reflectance, and to provide a reference for model optimization and vegetation canopy inversion improvement. The results showed that: (1) the sensitivities of six major input parameters of the PROSAIL model were generally consistent with the sorting orders of the two GSA methods, but were not in accordance with those from the LSA method, especially in the mid-infrared band; (2) coupled effects among parameters acting on reflectance simulation in visible light bands were greater than those in infrared bands; (3) the simulated canopy reflectance was evaluated using Landsat 5 TM data, and the results simulated based on LSA analysis showed higher error than those based on GSA analysis, because the LSA method ignored the influence of some parameters on canopy reflectance, e.g. leaf mesophyll structure (N), average leaf angle (ALA), leaf water content (C_w), and leaf dry matter content (C_m). However, GSA was able to fully consider the coupled effects among parameters, and thus identified the sensitive parameters impacting on reflectance more accurately.     

Scale dependence of biophysical structure in deforested areas bordering the Tapajós National Forest, Central Amazon

Spatial variation of land-surface properties is a major challenge to ecological and biogeochemical studies in the Amazon basin. The scale dependence of biophysical variation (e.g., mixtures of vegetation cover types), as depicted in Landsat observations, was assessed for the common land-cover types bordering the Tapajós National Forest, Central Brazilian Amazon. We first collected hyperspectral signatures of vegetation and soils contributing to the optical reflectance of landscapes in a 600-km² region. We then employed a spectral mixture model AutoMCU that utilizes bundles of the field spectra with Monte Carlo analysis to estimate sub-pixel cover of green plants, senescent vegetation and soils in Landsat Thematic Mapper (TM) pixels. The method proved useful for quantifying biophysical variability within and between individual land parcels (e.g., across different pasture conditions). Image textural analysis was then performed to assess surface variability at the inter-pixel scale. We compared the results from the textural analysis (inter-pixel scale) to spectral mixture analysis (sub-pixel scale). We tested the hypothesis that very high resolution, sub-pixel estimates of surface constituents are needed to detect important differences in the biophysical structure of deforested lands. Across a range of deforestation categories common to the region, there was strong correlation between the fractional green and senescent vegetation cover values derived from spectral unmixing and texture analysis variance results (r²>0.85, p<0.05). These results support the argument that, in deforested areas, biophysical heterogeneity at the scale of individual field plots (sub-pixel) is similar to that of whole clearings when viewed from the Landsat vantage point.     

Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images

The distribution of phytoplankton chlorophyll concentration in Lake Garda (Italy) was estimated using Landsat Thematic Mapper (TM) data acquired at two different times, February 1992 and March 1993. To investigate the waterleaving radiance adequately, the contribution of the atmospheric path radiance reaching the sensor should be removed. In this work a completely image-based atmospheric correction method was applied by means of an inversion technique based on a simplified radiative transfer code (RTC). A semi-empirical approach of relating atmospherically corrected TM spectral reflectances to in situ measurements through regression analysis was used. Limnological parameters were measured near to the TM images dates; some of the in situ measurements were used to define algorithms relating chlorophyll concentration measurements to water surface reflectance and the others too were used to validate the results of the predictive model. The models developed, which performed better (r² = 0.818) when concentrations were higher than > 3.0 mg m³, were used to map chlorophyll concentration throughout the lake. Spatial distribution maps of chlorophyll concentration and concentration changes were produced with contour intervals of 1 mg m³.     

Predicting temperate conifer forest successional stage distributions with multitemporal Landsat Thematic Mapper imagery

Forest succession is a fundamental ecological process which can impact the functioning of many terrestrial processes, such as water and nutrient cycling and carbon sequestration. Therefore, knowing the distribution of forest successional stages over a landscape facilitates a greater understanding of terrestrial ecosystems. One way of characterizing forest succession over the landscape is to use satellite imagery to map forest successional stages continuously over a region. In this study we use a forest succession model (ZELIG) and a canopy reflectance model (GORT) to produce spectral trajectories of forest succession from young to old-growth stages, and compared the simulated trajectories with those constructed from Landsat Thematic Mapper (TM) imagery to understand the potential of mapping forest successional stages with remote sensing. The simulated successional trajectories captured the major characteristics of observed regional mean succession trajectory with Landsat TM imagery for Tasseled Cap indices based on age information from the Pacific Northwest Forest Inventory and Analysis Integrated Database produced by Pacific Northwest Research Station, USDA Forest Service. Though the successional trajectories are highly nonlinear in the early years of succession, a linear model fits well the regional mean successional trajectories for brightness and greenness due to significant cross-site variations that masked the nonlinearity over a regional scale (R² = 0.8951 for regional mean brightness with age; R² = 0.9348 for regional mean greenness with age). Regression analysis found that Tasseled Cap brightness and greenness are much better predictors of forest successional stages than wetness index based on the data analyzed in this study. The spectral history based on multitemporal Landsat imagery can be used to effectively identify mature and old-growth stands whose ages do not match with remote sensing signals due to change occurred during the time between ground data collection and image acquisition. Multitemporal Landsat imagery also improves prediction of forest successional stages. However, a linear model on a stand basis has a limited predictive power of forest stand successional stages (adjusted R² = 0.5435 using the Tasseled Cap indices from all four images used in this study) due to significant variations in remote sensing signals for stands at the same successional stage. Therefore, accurate prediction of forest successional stage using remote sensing imagery at stand scale requires accounting for site-specific factors influence remotely sensed signals in the future.     

应用Landsat TM影像估算渤海叶绿素a和总悬浮物浓度

利用23个实测样点的渤海叶绿素a和总悬浮物浓度数据及同步Landsat TM影像数据,分别分析了Landsat TM离水辐射亮度对渤海叶绿素a和总悬浮物浓度的敏感性,选择合适的波段,通过回归分析构建了基于Landsat TM离水辐射亮度的渤海叶绿素a和总悬浮物浓度反演模型。结果表明,TM1、TM2和TM3波段对叶绿素a的敏感性较高,以TM4/TM1和TM3/TM2的对数为自变量,以叶绿素a浓度的对数为因变量的线性估算模型可以有效反演渤海叶绿素a浓度,决定系数R2达到0.97;TM3波段对悬浮物的敏感性最高,以TM2、TM3和TM3/TM2为自变量,以总悬浮物浓度的以10为底的对数为因变量的多元线性模型获得的结果最佳,决定系数R2达到0.91。     

Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat

Remote sensing of forest vertical structure is possible with lidar data, but lidar is not widely available. Here we map tropical dry forest height (RMSE = 0.9 m, R² = 0.84, range 0.6-7 m), and we map foliage height profiles, with a time series of Landsat and Advanced Land Imager (ALI) imagery on the island of Eleuthera, The Bahamas, substituting time for vertical canopy space. We also simultaneously map forest disturbance type and age. We map these variables in the context of avian habitat studies, particularly for wintering habitat of an endangered Nearctic-Neotropical migrant bird, the Kirtland's Warbler (Dendroica kirtlandii). We also illustrate relationships between forest vertical structure, disturbance type and counts of forage species important to the Kirtland's Warbler. The ALI imagery and the Landsat time series are both critical to the result for forest height, which the strong relationship of forest height with disturbance type and age facilitates. Also unique to this study is that seven of the eight image time steps are cloud-cleared images: mosaics of the clear parts of several cloudy scenes. We created each cloud-cleared image, including a virtually seamless ALI image mosaic, with regression tree normalization. We also illustrate how viewing time series imagery as red-green-blue composites of tasseled cap wetness (RGB wetness composites) aids reference data collection for classifying tropical forest disturbance type and age. Our results strongly support current Landsat Program production of co-registered imagery, and they emphasize the value of seamless time series of cloud-cleared imagery.     

Monitoring land-use change in the Pearl River Delta using Landsat TM

The Pearl River Delta in the People's Republic of China is experiencing rapid rates of economic growth. Government directives in the late 1970s and early 1980s spurred economic development that has led to widespread land conversion. In this study, we monitor land-use through a nested hierarchy of land-cover. Change vectors of Tasseled Cap brightness, greenness and wetness of Landsat Thematic Mapper (TM) images are combined with the brightness, greenness, wetness values from the initial date of imagery to map four stable classes and five changes classes. Most of the land-use change is conversion from agricultural land to urban areas. Results indicate that urban areas have increased by more than 300% between 1988 and 1996. Field assessments confirm a high overall accuracy of the land-use change map (93.5%) and support the use of change vectors and multidate Landsat TM imagery to monitor land-use change. Results confirm the importance of field-based accuracy assessment to identify problems in a land-use map and to improve area estimates for each class.     

In situ reflectance measurements and TM data of some sedimentary rocks with emphasis on white sandstone,southwestern Sinai,Egypt

This work presents the results of in situ reflectance measurements and Landsat-TM data analysis for some sedimentary rocks exposed in southwestern Sinai. Particular emphasis was given to white sandstone. In situ reflectance measurements were carried out in four selected sites (Musaba Salama, Abu Natash, El Dehisa and Abu Qafas areas) to define the distinctive surface reflectance patterns caused by the abundance of silicates, carbonates, clay minerals and iron oxides in white sandstone and other sedimentary rock types (clay, kaolin, limestone and dolomite). Moreover, in situ measured radiometric data were used to establish the theoretical basis for lithological discrimination.

Enhanced band ratios (2/3, 3/4, 4/5 and 5/7) and colour stretched ratio composite (2/3, 3/4 and 5/7) of the TM data 25 January 1984 of the Musaba Salama area were utilized to identify rocks rich in silicates and thereby to distinguish the white sandstone. The results demonstrated that the processed TM data can be used reliably in arid regions to distinguish white sandstone from other sandstone varieties as well as other rock types (limestone, dolomite, kaolin and shale).     

Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery

Soil moisture is important information in semiarid rangelands where vegetation growth is heavily dependent on the water availability. Although many studies have been conducted to estimate moisture in bare soil fields with Synthetic Aperture Radar (SAR) imagery, little success has been achieved in vegetated areas. The purpose of this study is to extract soil moisture in sparsely to moderately vegetated rangeland surfaces with ERS-2/TM synergy. We developed an approach to first reduce the surface roughness effect by using the temporal differential backscatter coefficient (Δσ_wet-dry⁰). Then an optical/microwave synergistic model was built to simulate the relationship among soil moisture, Normalized Difference Vegetation Index (NDVI) and Δσ_wet-dry⁰. With NDVI calculated from TM imagery in wet seasons and Δσ_wet-dry⁰ from ERS-2 imagery in wet and dry seasons, we derived the soil moisture maps over desert grass and shrub areas in wet seasons. The results showed that in the semiarid rangeland, radar backscatter was positively correlated to NDVI when soil was dry (m_v<10%), and negatively correlated to NDVI when soil moisture was higher (m_v>10%). The approach developed in this study is valid for sparse to moderate vegetated areas. When the vegetation density is higher (NDVI>0.45), the SAR backscatter is mainly from vegetation layer and therefore the soil moisture estimation is not possible in this study.