An iterative algorithm for removing the effect of thin cloud cover from LANDSAT imagery

In this paper, we propose an iterative algorithm for removing the effect of thin cloud cover from LANDSAT imagery. It is seen that the noise in such images is multiplicative as well as additive. The recorded image is first processed to update different parameters of the image formation model to known values. Processing algorithm and knowledge of parameter values are developed by considering the physics of the situation. A low-pass filter is then applied to the processed image to remove the effect of the cloud. The filter is of a tapered shape, and its parameters are adjusted to minimize the estimation error.     

Mapping badland areas using LANDSAT TM/ETM satellite imagery and morphological data

Soil erosion is the most influential component of land degradation for its strong impacts on both natural and agricultural environments. In order to support effective intervention and recovery policies for eroded areas, monitoring techniques should take into account the space–time variability of the processes involved, and make use of assessed mapping methodologies as baseline criteria for studying the dynamics of landform development. When using multispectral data for mapping eroded areas, low spectral separability is a significant limit in areas with complex features, where soil materials are frequently remixed by surface runoff. Since multispectral satellite images are a valuable data source for multi-temporal analyses of erosion processes at the medium scale, we assessed how accurately a badland area can be identified from LANDSAT TM and ETM data. A protocol for an optimal mapping was built up by testing the performance of different supervised algorithms and input layers (spectral and morphological). Tests were carried out in a well-known badland area of Basilicata, Italy, with an extension of  8000 ha. Results obtained from the use of spectral bands (with and without thermal channel) and principal components returned an overall accuracy ranging from 53% (for classification on first three components) to 72% (for classification on all bands from TM), with low values for the kappa coefficient (0.30–0.50), showing that the spectral information alone are insufficient to accurately identify badland areas. In order to improve mapping, we found that the integration of slopes and aspects derived from a Digital Elevation Model (DEM) can overcome problems inherent to the low separability of spectral signatures. The use of morphological data was tested for different classification algorithms and integration approaches. In discriminating badlands, the better performing algorithm was MLC (Maximum Likelihood Classifier) and the best results were obtained by integrating all seven bands (including TIR) with slope and aspect maps as input within the classifier (A > 0.85 and K  0.75 for both the sensors). We selected such parameters because they play an important role in characterising badlands of study area from a morphological perspective but the proposed approach is also conceptually simple and can be easily exported to other areas. The obtained results support the hypothesis that the combined use of remote sensing imagery and auxiliary morphological data significantly improves the mapping of badlands over large areas with heterogeneous features, thus providing a useful methodology for long-term studies on soil erosion processes.     

差分主成分分析法在辽河三角洲景观变化中的应用

差分主成分分析法是应用遥感数据检测景观变化的一种新方法。为了提高检测精度,我们利用TM卫星遥感数据,改进了主成分分析法和图像差值法,提出了差分主成分分析法。并以辽河三角洲地区为例,对该方法进行了验证。研究结果表明：(1) 与传统的检测法-分类后比较法相比,差分主成分分析法具有较高的检测精度,总检测精度为0.89,Kappa指数为0.82;(2) 在1984~2000年的16年间,辽河三角洲地区有近22%的景观发生了变化,主要包括芦苇湿地的减少、水稻田的增加以及城镇用地的增加。     

运用LANDSAT ETM+和ASTER数据进行岩性分类

本文评价了运用ASTER和LANDSAT ETM+数据进行岩性制图的性能。分别利用ASTER数据不同波段区图像及其组合,以及ETM+数据进行岩性分类,并探讨了将ASTER和ETM+数据叠加在一起进行了岩性分类; 利用现有地质图对所有分类结果进行了定量评价。结果表明,ASTER数据不同波段的岩性识别能力不同,并且较ETM+数据能更准确地识别岩性。更重要的是,把ASTER与ETM+数据结合在一起进行岩性分类,可获得比用任一数据单独分类更高的分类精度,表明二者的光谱特征具有一定的互补性。     

Tidal estuary width convergence: Theory and form in North Australian estuaries

In order to better understand the relations between tidal estuary shape and geomorphic processes, the width profiles of 79 tidal channels from within 30 estuaries in northern Australia have been extracted from LANDSAT 5 imagery using GIS. Statistics describing the shape and width convergence of individual channels and entire estuaries (which can contain several channels) are analysed along with proxies for the tidal range and fluvial inputs of the estuaries in question. The width profiles of most individual channels can be reasonably approximated with an exponential curve, and this is also true of the width profiles of estuaries. However, the shape of this exponential width profile is strongly related to the mouth width of the system. Channels and estuaries with larger mouths generally exhibit a more pronounced ‘funnel shape’ than those with narrower mouths, reflecting the hydrodynamic importance of the distance over which the channel or estuarine width converges. At the estuarine scale, this ‘convergence length’ also tends to be higher in estuaries which have larger catchments relative to their size. No clear relation between the estuarine width convergence length and tidal range could be discerned within the Northern Australian estuaries although, when these data are combined with data from other studies, a weak relationship emerges. Copyright © 2010 John Wiley & Sons, Ltd.     

Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO-1Hyperion data

Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R²_{no age correction} = 0.55 vs R²_{age correction} = 0.73 for Landsat) and hyperspectral (R²_{no age correction} = 0.12 vs R²_{age correction} = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION.     

Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions

Biomass can indicate plant growth status, so it is an important index for plant growth monitoring. This paper focused on the methodology of estimating the winter wheat biomass based on hyperspectral field data, including the LANDSAT TM and EOS MODIS images. In order to develop the method of retrieving the wheat biomass from remote sensed data, routine field measurements were initiated during periods when the LANDSAT satellite passed over the study region. In the course of the experiment, five LANDSAT TM images were acquired respectively at early erecting stage, jointing stage, earring stage, flowering stage and grain-filling stage of the winter wheat, and the wheat biomass was measured at each stage. Based on the TM and MODIS images, spectral indices such as NDVI, RDVI, EVI, MSAVI, SIPI and NDWI were calculated. At the same time, the hyperspectral field data was used to compute the normalized difference in spectral indices, red-edge parameters, spectral absorption, and reflection feature parameters. Then the correlation coefficients between the wheat biomass and spectral parameters of the experiment sites were computed. According to the correlation coefficients, the optimal spectral parameters for estimating the wheat biomass were determined. The bestfitting method was employed to build the relationship models between the wheat biomass and the optimal spectral parameters. Finally, the models were used to estimate the wheat biomass based on the TM and MODIS data. The maximum RMSE of estimated biomass was 66.403 g/m².     

The Warrabin Trough,western Adavale Basin,Queensland

The Warrabin Trough in SW Queensland contains up to 3000 m of Devonian sedimentary rocks, and is a structural remnant of the formerly widespread Adavale Basin. Seismic data recently obtained by the Bureau of Mineral Resources enable reinterpretation of existing poorer quality seismic data, so that the structural framework and most of the trough margin can be mapped.

Sedimentary rocks in the trough are folded and are displaced by high‐angle reverse faults, which were probably active during the Late Carboniferous. LANDSAT studies provide additional information on the position of faults, which on seismic records appear to be mainly contained within the trough sequence. Differential compaction, together with slight post‐Cretaceous rejuvenation of some structures, may have given surface expression to some of these deep faults.

The western margin of the trough has been drilled, but only 775 m of the Devonian sequence was penetrated. The petroleum source‐rock and maturation levels do not appear encouraging on the basis of this drilling. However, the thick sequence of arenites, lutites and carbonates, interpreted to occur in the trough, offer considerable potential for hydrocarbon source‐rocks and reservoirs.     

Retrieving water surface temperature from archive LANDSAT thermal infrared data: Application of the mono-channel atmospheric correction algorithm over two freshwater reservoirs

Water surface temperature is a key element in characterizing the thermodynamics of waterbodies, and for irregularly-shaped inland reservoirs, LANDSAT thermal infrared images are the best alternative yet for the retrieval of this parameter. However, images must be corrected mainly for atmospheric effects in order to be fully exploitable. The objective of this study is to validate the mono-channel correction algorithm for single-band thermal infrared LANDSAT data as put forward by Jiménez-Muñoz et al. (2009). Two freshwater reservoirs in continental France were selected as study sites, and best use was made of all accessible image and field data. Results obtained are satisfactory and in accordance with the literature: r² values are above 0.90 and root-mean-square error values are comprised between 1 and 2 °C. Moreover, paired Wilcoxon signed rank tests showed a highly significant difference between field and uncorrected image data, a very highly significant difference between uncorrected and corrected image data, and no significant difference between field and corrected image data. The mono-channel algorithm is hence recommended for correcting archive LANDSAT single-band thermal infrared data for inland waterbody monitoring and study.     

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm,India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d^?1 for ET_SEBAL, i.e. ET_SEBAL having good accuracy with respect to observed ET_Lysimeter. Results were also validated using R-RMSE test, which also proved that ET_SEBAL data are having good accuracy with respect to observed ET_Lysimeter as R-RMSE of crop-growing period is 0.19 mm d^?1. MAE (0.19), NRMSE (0.21) and r² (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET_24 h) estimation. The temporal study of the ET_24 h values analysed has revealed that the highest ET_24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.