Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data

In this article, Landsat TM images acquired during the same season from both 1984 and 1997 were analysed for urban built-up land change detection in Beijing, China, where great changes have taken place during the recent decades. To reduce the spectral confusion between urban 'built-up' and rural 'non built-up' land cover categories, we propose a new structural method based on road density combined with spectral bands for change detection. The road density represents one type of structural information while the multiple Landsat TM bands represent spectral information. Road density maps for both dates were produced using a gradient direction profile analysis (GDPA) algorithm and then integrated with spectral bands. Results from the spectral-structural postclassification comparison (SSPCC) and spectral-structural image differencing (SSID) methods were evaluated and compared with spectral-only change detection methods. The proposed SSPCC method greatly reduced spectral confusion and increased the accuracy of land cover classification compared with spectral classification, which in turn improved the change detection results. This article also shows that the SSID change detection result complemented spectral band differencing by detecting areas with greater structural changes, some of which were missed, by spectral band differencing.     

Image segmentation for humid tropical forest classification in Landsat TM data

Humid tropical forest types have low spectral separability in Landsat TM data due to highly textured reflectance patterns at the 30m spatial resolution. Two methods of reducing local spectral variation, low-pass spatial filtering and image segmentation, were examined for supervised classification of 10 forest types in TM data of Peruvian Amazonia. The number of forest classes identified at over 90% accuracy increased from one in raw imagery to three in filtered imagery, and six in segmented imagery. The ability to derive less generalised tropical forest classes may allow greater use of classified imagery in ecology and conservation planning.     

Reconstructing the paleoclimate record of Lupobupo salt lake with Landsat TM data

Luobupo salt lake is famous for its ring shape which looks like a big ear observed from satellite image. These rings recorded the information about water retreat and advance. In this paper TM data is used to study the brightness of these rings and then deduce the paleoclimate. The result showed that the brightness curve of these rings has a similar climate trend to that deduced from Guliya ice core record.     

A comparison of Landsat TM and MODIS vegetation indices for estimating forage phenology in desert bighorn sheep (Ovis canadensis nelsoni) habitat in the Sonoran Desert,USA

Sonoran Desert bighorn sheep (Ovis canadensis mexicana) occupy rugged upland areas that experience irregular periods of vegetation growth associated with precipitation events. These episodic and often spatially limited events provide important forage and preformed water resources that may be important drivers of animal movement and habitat use. Habitat-use models that incorporate forage phenology would broaden our understanding of desert bighorn ecology and have considerable potential to inform conservation efforts for the species. Field-based methods are of limited utility to characterize vegetation phenology across large areas. Vegetation indices (VI) derived from satellite imagery are a viable alternative, but may be confounded by areas of high relief and shadow effects that can degrade VI values. The varying spatial and temporal resolutions of readily available satellite sensors, such as the Landsat thematic mapper (TM) and moderate-resolution imaging spectrometer (MODIS), present additional challenges. In this study, we sought to minimize degrading effects of terrain on TM- and MODIS-based estimates of vegetation phenology. We compared effects of high topographic relief on time series MODIS- and TM-based VI such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) using VI departures from average (DA) in shaded and unshaded areas. Sun elevation angle negatively impacted TM-derived NDVI and EVI values in areas of steep terrain. In contrast, MODIS-derived NDVI values were insensitive to sun elevation and terrain effects, whereas MODIS-derived EVI was degraded in areas of steep terrain. Time series MODIS NDVI and EVI DA values differed significantly during months of low sun elevation angle. Average MODIS EVI departure values were ≥20% lower than NDVI under these conditions, confounding time series estimates of plant phenology. Our best results were obtained from MODIS 16-day composited NDVI. These remote-sensing-based VI estimates of seasonal plant phenology and productivity can be used to inform models of habitat use and movements of desert bighorn over large areas.     

Reduction of atmospheric and topographic effect on Landsat TM data for forest classification

The incident radiance in forested areas with rugged terrain varies greatly with the changes in solar elevation and azimuth, slope and aspect of the terrain, and the relative position of trees. The geotropic nature must be considered in the course of topographic correction. The Sun‐Canopy‐Sensor (SCS) model is introduced to substitute the cosine correction in a physical model. We used an atmospheric simulation code, MODTRAN, and a digital elevation model (DEM) to calculate the path radiance, downwards diffuse radiance and two‐way transmittance of direct and diffuse light at different altitudes. Based on the atmospheric parameters derived above and the Lambertian assumption, surface reflectance in a forested area was retrieved from Landsat Thematic Mapper (TM) imagery using a revised physical model. Meanwhile, a smoothed DEM was used to assess the effect of noise on the DEM and misregistration between the DEM and the satellite imagery. Correlation analysis, spectral comparison between sunlit and shaded slopes and a support vector machine (SVM) classification were performed to assess the effect of the revised radiometric correction algorithm. Results indicate that the revised physical model with smoothed DEM is more adequate for forested terrain and more consistent spectra for similar vegetation under different illuminations can be obtained. Finally, higher classification accuracy of forested land can be achieved with the revised correction algorithm compared with the SCS correction and the original physical correction model.     

An optimal model for estimating suspended sediment concentration from Landsat TM images in the Caofeidian coastal waters

Suspended sediment concentration (SSC) is one of the most critical parameters in water quality and environmental evaluations. Remote sensing has the potential for monitoring the dynamics and spatial distribution of SSC efficiently. The primary objective of this study is to develop retrieval models that are reliable and sensitive to SSC levels in the Caofeidian area, a new seaport in northeast China, based on Landsat-5 Thematic Mapper (TM) images and a set of in situ data sets, including spectral reflectance data and water quality data. The study finds that the band reflectance ratio and binary combination factor (i.e. the ratio of the reflectance to the particle size) are more effective than single band reflectance, and a non-linear model is more potent than a linear model for predicting SSC in the Caofeidian waters. A quadratic polynomial regression model of the R_TM3/R_TM2 ratio is proposed as the optimal retrieval model after evaluating various models with respect to different sensitive factors. The accuracy of the model is acceptable with a relative error and a root mean square error of 25.35% and 7.22 mg l^–1, respectively; the correlation coefficient between the observed and estimated SSCs is 0.986. This study also indicates that the band reflectance ratio and binary combination factor are effective in weakening and even partially eliminating the effects of the changes in the sediment type (i.e. particle size and refractive index). And the band reflectance ratio is more efficient. Using the proposed model and TM data, SSC levels for the entire region were estimated. Such results can serve as a baseline for future environmental monitoring efforts.     

Pattern decomposition method in the albedo space for Landsat TM and MSS data analysis

We have developed a 'pattern decomposition method' based on linear spectral mixing of ground objects for n-dimensional satellite data. In this method, spectral response patterns for each pixel of an image are decomposed into three components using three standard spectral shape patterns determined from the image data. Applying this method to Landsat Thematic Mapper data, six-dimensional data are successfully transformed into three-dimensional data. Nearly 94% of the information in the six-dimensional data is retained in the three components. This method is very useful for classifying and monitoring changes in land cover.     

On the feasibility of diachronic regression estimators with ground survey and Landsat TM data

A paper by Gonzalez-Alonso et al . (1997) presents a regression estimator with excellent relative efficiency values to estimate the area of barley in an intensive agricultural area in Lleida (Catalonia, Spain) using 1994 ground survey data and a 1993 Landsat TM classified image. The paper concludes that the regression estimator can be efficiently applied with ground data from the current year and a classified satellite image from a previous year. If these conclusions are applicable to large areas, using the same classified image for several years would greatly increase the cost-efficiency of the regression estimator. This Letter evaluates the suitability of the method in other areas of Spain.     

Estimation of aboveground grassland phytomass with a growth model using Landsat TM and climate data

Estimation of aboveground phytomass in meadow grasslands was carried out using multitemporal satellite data of fine resolution but of low frequency from Landsat TM observations in 1984-1990. We developed two growth models for the estimation of the first-cut yield, based on an exponential plant growth of which initial phytomass was determined using NDVI or TM2/TM3 on the Landsat observation date together with the effective cumulative temperature during the growth period after the observation date. Validations of these models with different data sets of Landsat TM in 1990-1994 indicated that the measured and estimated yields agreed well, suggesting the great potential of applying fine resolution satellite data coupled with a growth model to phytomass study, in spite of the low frequency of observation.     

A qualitative approach to the mapping of post-fire regrowth in Mediterranean vegetation with Landsat TM data

This Letter describes a method for using Landsat Thematic Mapper ( TM) data to monitor vegetation growth on a large burnt area in northern Sardinia. Five different vegetational classes, characteristic of the Mediterranean region are described in which green biomass increased from the herbaceous to the arbustive and arboreal. For each class the values of the infrared index II ( TM4— TM5)( TM4-f-TM5 ) were calculated. The ability of this index to enhance green biomass differences made it appropriate to the monitoring of post-fire regrowth.     

A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests

Remote sensing of low biomass forests has challenges related to the contribution of soil and understory reflectance recorded by sensors, hampering accurate forest aboveground carbon (AGC) quantification. To improve Landsat-based AGC estimates in forests with low biomass, this study explored the use of multi-temporal Landsat 8 Operational Land Imager (OLI) derived spectral information in Zagros forests by testing four machine learning algorithms: support vector machine (SVM), boosted regression trees (BRT), random forest (RF) and multivariate adaptive regression splines (MARS). We selected two forest areas with different levels of human activity for AGC reference plots: un-degraded forest (UD) and highly-degraded forest (HD). The results of the study showed that the Landsat image acquired in the peak of the growing season (10 August) provided the best AGC estimates for the UD site, but that for the HD site, AGC estimates were not affected by the timing of the imagery. The comparison of different modelling methods demonstrated lower accuracies from BRT, considerably biased estimates from SVM, and generally robust results from the RF algorithm. Overall, the study demonstrated the utility of applying the free Landsat 8 OLI dataset to AGC estimation, in particular non-commercial forests in developing countries where little budget is allocated for management.     

Linear spectral mixture analysis of Landsat TM data for monitoring invasive exotic plants in estuarine wetlands

This study assessed the feasibility of spectral mixture analysis (SMA) of Landsat thematic mapper (TM) data for monitoring estuarine vegetation at species level. SMA modelling was evaluated, using χ² test, by comparing SMA fraction images with a precisely classified QuickBird image that has a higher spatial resolution. To clearly understand the strengths and weaknesses of SMA, eight SMA models with different endmember combinations were assessed. When the TM data dimension for SMA and the endmember number required were balanced, a model with three endmembers representing water and two vegetation types was most accurate, whereas a model with five endmembers approximated the actual surface situation and generated a relatively accurate result. Our results indicate that an SMA model with appropriate endmembers had relatively satisfactory accuracy in monitoring vegetation. However, errors might occur in SMA fraction images, especially in models with an inappropriate endmember combination, and the errors were mainly distributed in areas filled with water or near water. Therefore, short vegetation usually submerged during high tide tended to be poorly predicted by SMA models. These results strongly suggest that tide water has a great influence on SMA modelling, especially for short vegetation.     

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.     

Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research

Atmospheric correction is an important preprocessing step required in many remote sensing applications. The authors are engaged in the project 'Human Dimensions of Amazonia: Forest Regeneration and Landscape Structure' in NASA/INPE's Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) programme. This project requires use of corrected Landsat TM data since research foci integrate ground-based data and TM to: (1) measure and model biomass; (2) classify multiple stages of secondary succession; (3) model land cover/land use changes; and (4) derive spectral signatures consistent across different study areas. The 30+ scenes of TM data are historic and lack detailed atmospheric data needed by physically-based atmospheric correction models such as 6S (Second Simulation of the Satellite Signal in the Solar Spectrum). Imagebased DOS models are based on image measurements and explored in this article for application to LBA study areas. Two methods using theoretical spectral radiance and image acquisition date respectively were used to convert TM DN values to at-satellite radiance. Three image-based models were employed using each method to convert at-satellite radiance to surface reflectance. Analyses of these six different image-based models were conducted. The Improved Imagebased DOS was the best technique for correcting atmospheric effects in this LBA research with results similar to those obtained from physically-based approaches.     

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.     

Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data

Various methods have been developed during the past three decades to improve the classification accuracy in burned area mapping using satellite data captured by different sensors. In this article, we compare ten such classification approaches using Landsat Thematic Mapper (TM) imagery on three Mediterranean test sites by evaluating the classification accuracy using (i) a traditional pixel-based approach, (ii) the concept of the Pareto boundary of efficient solution and (iii) linear regression analysis. Additionally, we make a discrimination of errors depending on their distribution and causal factor. The classification approaches compared resulted in not statistically significant differences in the accuracy of the burned area maps. Differences between the methods were also observed when considering the accuracy along the edges of the burned patches; however, again these were not statistically significant. The findings of our study in a Mediterranean environment clearly demonstrate that, for the selection of the most suitable classification approach, other factors could be given more weight, such as computational resources, imagery characteristics, availability of ancillary data, available software and the analyst's experience. Maybe the most important finding of our work is that the variance imposed by the methods is less than the variance imposed by factors differentiated locally in the three study sites since the between-group variance of the overall accuracy is higher than that of the within groups.     

An evaluation of Landsat TM spectral data and SAR-derived textural information for lithological discrimination in the Red Sea Hills,Sudan

The effectiveness of spectral and textural information in the identification of surface rock types in an arid region, the Red Sea Hills of Sudan, is evaluated using spectral information from the six Landsat TM optical bands and textural features derived from Shuttle Imaging Radar-C (SIR-C) C-band HH polarization data. An initial classification is derived from Landsat TM data alone using three classification algorithms, Gaussian maximum likelihood, a multi-layer feed-forward neural network and a Kohonen self-organizing feature map (SOM), to generate lithological maps, with classification accuracy being measured using a confusion matrix approach. The feed-forward neural net produced the highest overall classification accuracy of 57 per cent and was, therefore, selected for the second experiment, in which texture measures from SIR-C C-band HH-polarized synthetic aperture radar (SAR) data are added to selected TM spectral features. Four methods of measuring texture are employed, based on the Fourier power spectrum, grey level co-occurrence matrix (GLCM), multi-fractal measures, and the multiplicative autoregressive random field (MAR) model. The use of textural information together with a subset of the TM spectral features leads to an increase in classification accuracy to almost 70 per cent. Both the MAR model and the GLCM matrix approach perform better than Fourier and multi-fractal based methods of texture characterization.     

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.     

Monitoring geomorphologic changes using Landsat TM and ETM+ data in the Hendijan River delta,southwest Iran

Beach and delta areas are dynamic physical features with changes occurring at many spatial and temporal scales due to both general and catastrophic events. Geomorphic changes such as temporal and periodic changes in riverbeds and coasts are common events in all deltaic areas. The Hendijan river basin is located in the southwest of Iran, close to the city of the Hendijan and many villages and rural settlements. Changes in various geomorphic features, such as riverbed and shoreline migration, Sebkhas, alluvial terraces, meanders and old, dry rivers over 48 years of time, were detected and identified using Landsat TM and ETM satellite data and topographic maps. Simple bands subtraction, principal component analysis (PCA) and fuzzy logic were used to identify regions that have undergone land cover change. Results of this study show that the Hendijan River channel has migrated several times over the last 48 years. Several meanders and ox‐bow lakes remain as a result of migration. The shoreline has migrated over 4 km into the Persian Gulf. The resulting maps can be used in an integrated coastal zone information system as it has been proposed for the Heddijan delta.