Location of kimberlites using Landsat Thematic Mapper images and aerial photographs: the Redondao diatreme,Brazil

Abstract

Landsat Thematic Mapper images and aerial photographs were used in the detection of kimberlile-derived materials in the Redondao test site. In this area kimberlite-derived soils show a flora constituted mainly by grasses and shrubs, which differ from the surrounding savanna-park (cerrado) vegetation cover. Band-ratio images were able to distinguish kimberlite-derived materials by enhancing areas with different vegetation covers. However, the coarse spatial resolution of Landsat-TM images compared with the spatial variability of the study area, and the removal of topographic shadowing effects on ratio images blurred several landscape features. To increase discrimination, Landsat Thematic Mapper ratio images were merged with digitized aerial photographs through intensity, hue and saturation (IHS) colour transforms. The resulting merged colour composite highlighted the spatial and spectral features of the study area permitting an accurate definition of the kimberlite-derived materials within the Redondao diatreme.     

Remote sensing of coastal discharge sites using SPOT-simulation data

Abstract

The use of SPOT-simulation data to identify and delineate the structure of effluent plumes in a tidal estuary is considered. Results are presented to demonstrate the capability of the data to locate the effluent discharged from short coastal outfalls and a long sea outfall, respectively, in the Firth of Forth. In addition, coastal water movements are revealed and the processed images are interpreted in terms of correlations between suspended sediment distributions and estuarine bathymetry variations. The relationship between the present results and previous studies at the same site are discussed.     

Improving geological mapping in the Arabian Shield using Landsat Thematic Mapper imagery—Jabal Duhayyah area

Abstract

The Jabal Duhayyah area spans the contact between two tectonic zones of the southern Arabian Shield. By utilising principal components analysis of the Landsat-TM data, it was easy to locate this contact precisely as well as to improve and update available geological maps. Previously unmapped lithologics were distinguished and mapped during the processing of the images. Field-checking confirmed these findings and a new geological map was constructed.

Satellite imagery (e.g. Landsat) could be a valuable mapping tool in the well-exposed Arabian Shield, even where it is already mapped at the 1:100000 scale.     

Migratory bird habitat monitoring through remote sensing

Abstract

Unsupervised classification of Landsat-TM data was employed to identify habitats important for migratory birds in Costa Rica. The overall habitat classification accuracy was 70 per cent (Kappa correction). Mature forest could be identified with high accuracy (93 per cent) but Landsat-TM classification accuracy for major successional stages was low. Habitat availability and conversion rates from 1976 to 1986 were derived from multidate Landsat imagery supplemented with interpretation of historical air photos to document the specific types of habitat change. The major trend in habitat conversion between 1976 and 1984 was forest clearing followed by establishment of permanent pasture. Some of the pasture land was converted to perennial tree crops by 1986. The implication of habitat modification on groups and species of migrant land birds are discussed.     

Water quality mapping from Landsat digital data

Landsat multispectral scanner (MSS) data, and U-2 colour and colour infrared photographs were combined with in situ data for the assessment of water quality parameters within the San Francisco Bay-Delta. The water quality parameters of interest included turbidity and suspended solids. The U-2 photography and water quality samples were obtained simultaneously and coincidently with Landsat overpass. Regression models were developed between each of the water quality parameter measurements and Landsat digital data for 29 pre-selected sample sites. These regression models were then extended to the entire study area for mapping the water quality parameters of interest. The results included a series of colour-coded maps, each pertaining to one of the water quality parameters, and the statistical summaries. Areas of relatively high biological activity were clearly discernible on digitally enhanced Landsat MSS data.     

紫外吸收光谱法检测化学需氧量的方法

为提高化学需氧量检测的准确性和时效性,利用多波长紫外吸收光谱法与偏最小二乘回归相结合的算法预测水样中的化学需氧量,同时考虑了浊度对建模所用的吸光度的影响,对浊度的影响进行了补偿.通过实验分析表明:提出的方法对不同类型的污水水质检测均适用,平均相对误差在5%以内,且预测精度明显优于未经浊度补偿的偏最小二乘回归模型.     

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

Abstract

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

Wheat-area estimation using digital LANDSAT MSS data and aerial photographs

Abstract

A procedure to estimate wheat (Triticum aestivum L) area using a sampling technique based on aerial photographs and digital LANDSAT MSS data was developed. Aerial photographs covering 720km2 were visually analysed. Computer classification of LANDSAT MSS data acquired on 4 September 1979 was performed using unsupervised and supervised algorithms and the classification results were spatially filtered using a post-processing technique. To estimate wheal area, a regression approach was applied using different sample sizes and various sampling units. Based on four decision criteria proposed in this study, it was concluded that (i) as the size of the sampling unit decreased, the percentage of the sample area required to obtain a similar estimation performance also decreased, (ii) the lowest percentage of the area sampled for wheat estimation under established precision and accuracy criteria through regression estimation was 13-09 per cent using 10 km2 as the sampling unit and (iii) wheat-area estimation obtained by regression estimation was more precise and accurate than those obtained by a direct expansion method.     

Volcano monitoring using remote sensing data

Abstract

The paper brings out the theoretical basis and utility of near-infrared band data sets obtained from Earth resources satellites, in the estimation of very high temperatures witnessed during volcanic eruptions. The Landsat Thematic Mapper (TM) and Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning System (LISS-II) data sets for the period 4 April 1991-4 August 1991 were used for studying the volcanic eruption at Barren Island (India). The effect of the sub-pixel size vent in the estimation of pixel-integrated temperature has been discussed. The volcanic vent temperature on 6 May 1991 was found to be 1084K. The availability of mid-IR bands (1-55-1-75 μm and 2.08-2.35μm spectral region) in Landsat-TM enabled bringing out the vent region in the false colour composite (FCC) generated using these and the near-IR (0.76-0.90μm) band. The very high or saturated values in mid-IR bands brought a good contrast between vent and its surroundings     

Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data

ABSTRACT

Rapid accurate estimation of the fractional cover of non-photosynthetic vegetation (f_NPV) is essential for monitoring desertification, managing grassland resources, assessing soil erosion and grassland fire risk, and preserving the grassland ecological environment. However, there have been very few studies using multispectral remote sensing images (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) images in this study) to estimate f_NPV in typical grassland areas in northern China. In this study, using field spectra obtained from ground measurements in May and October 2017 and corresponding f_NPV data, we calculated eight non-photosynthetic vegetation indices (NPVIs) from the simulated MODIS bands. We then determined the NPVIs that were suitable for the estimation of f_NPV. Based on the determined NPVIs, we established a remote sensing estimation model for f_NPV in typical grassland areas using MODIS image data. The spatial distribution of f_NPV in the studied area was also investigated. The results indicated that the determined NPVIs, including the dead fuel index (DFI), shortwave-infrared ratio (SWIR₃₂), normalized difference tillage index (NDTI), modified soil-adjusted crop residue index (MSACRI), and soil tillage index (STI), used bands 6 and 7 in the shortwave-infrared region of the MODIS data; the DFI had the best performance, with a coefficient of determination (R²) of 0.68 and root mean square error of leave-one-out cross-validation (RMSECV) of 0.1390. The models based on MODIS image data for the estimation of f_NPV using NPVIs had relatively good regression relations, and we determined that the DFI linear regression model was the best remote sensing model for monitoring f_NPV in typical grassland areas, with an estimation accuracy exceeding 73.00%. Additionally, our results indicated that the distribution of non-photosynthetic vegetation exhibited substantial spatial heterogeneity and that f_NPV gradually decreased from the north-eastern to south-western portions of the study area.     

Identification of overdeveloped zones of ground water and the location of rainwater harvesting structures using an integrated remote sensing based approach—a case study in part of the Anantapur district,Andhra Pradesh,India

Abstract

The Kuderu macro watershed lies in the middle reaches of Pennar river basin in Anantapur district, Andhra Pradesh, India. There is great need and demand for ground water for irrigation and drinking purposes, due lo frequent failure of monsoons and recurring drought. The majority of the irrigated wells are dried up and the ground water is extracted from the deeper aquifers, to meet the acute shortage of water. In this article, an attempt has been made lo make a quantitative estimate of ground water resource at the micro level using conventional techniques, hydrogeomorphological and hydrogeological mapping using remotely-sensed data. Multi-spectral and multi-date satellite data from IRS LISS-1 and Landsat-TM were utilised to assess the hydrogeological characteristics as well as ground water irrigated areas. The rainwater harvesting structures are recommended in the ground water overdeveloped villages to recharge the irrigated wells for further utilisation and management.     

Comparison of three satellite sensors at three spatial scales to predict larval mosquito presence in Connecticut wetlands

Satellite imagery can be used to identify suitable habitat for mosquitoes in areas inaccessible or lacking sufficient ground-based information about the environment but current applications are limited by the spatial and spectral resolution of the sensors. Here, models used to compare prediction of the presence of Anopheles punctipennis larvae in Connecticut wetlands were built using stepwise logistic regression and compared by Akaike's Information Criterion (AIC). Vegetation indices were extracted from three satellite sensor scenes (Hyperion, ASTER and Landsat-TM) at three scales (pixel, wetland perimeter, and wetland area). The best models were developed using ASTER (ROC = 0.80, p = 0.01, AIC 65.37) and Hyperion (ROC = 0.81, p < 0.01, AIC 66.40) at the wetland area level. The Disease Water Stress Index (DWSI), a measure of leaf water content, and Normalized Difference Vegetation Index (NDVI) were significant in many of the models. This comparison of satellite based models demonstrates higher spatial and spectral resolution of ASTER and Hyperion resulted in more parsimonious models than Landsat-TM models. The need for continued research and development into sensors with increased spatial and spectral resolution and the development of mosquito specific indices is discussed.     

Land-use inventory and mapping in a mountainous area: the Ardèche experiment†

Abstract

The paper is a review and discussion of the work done on the Ardèche test site by using TM data. The Ardèche is a mountainous and strongly dissected area with very small fields, and multitemporal processing is essential in order to reach sufficient classification accuracy. This necessitated the following developments: high-quality image-registration integrating altitude information, image-based radiometric calibration, spatial zoning before classification, and post-classification sorting. Monotemporal results are shown for the complete area together with multi-temporal results for a part of the area. A multiband multi-temporal segmentation method is presented as preparation for segment-wise classification.     

Assessing the feasibility of sub-pixel burned area mapping in miombo woodlands of northern Mozambique using MODIS imagery

The goal of this study was to evaluate the feasibility of sub-pixel burned area detection in the miombo woodlands of northern Mozambique, using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). Multitemporal Landsat-7 ETM+ data were acquired to produce a high spatial resolution map of areas burned between mid-August and late September 2000, and a field campaign was conducted in early November 2000 to gather ground truth data. Mapping of burned areas was performed with an ensemble of classification trees and yielded a kappa value of 0.896. This map was subsequently degraded to a spatial resolution of 500 m, to produce an estimate of burned area fraction, at the MODIS pixel size. Correlation analysis between the sub-pixel burned area fraction map and the MODIS reflective channels 1-7 yielded low but statistically significant correlations for all channels. The better correlations were obtained for MODIS channels 2 (0.86 µm), 5 (1.24 µm) and 6 (1.64 µm). A regression tree was constructed to predict sub-pixel burned area fraction as a function of those MODIS channels. The resulting tree has nine terminal nodes and an overall root mean square error of 0.252. The regression tree analysis confirmed that MODIS channels 2, 5, and 6 are the best predictors of burned area fraction. It may be possible to improve these results considering, as an alternative to individual channels, some appropriate spectral indices used to enhance the burnt scar signal, and by including MODIS thermal data in the analysis. It may also be possible to improve the accuracy of sub-pixel burned area fraction using MODIS imagery by allowing the regression tree to automatically create linear combinations of individual channels, and by using ensembles of trees.     

Growth profile based crop yield models: A case study of large area wheat yield modelling and its extendibility using atmospheric corrected NOAA AVHRR data

Wheat growth profile based yield models for 12 districts of Punjab State and 16 districts of Haryana State have been developed using the normalised difference vegetation index (NDVI) derived from NOAA-11 AVHRR data of the 1993-94 cropping season. Atmospheric normalisation of AVHRR data was performed prior to deriving district-level area weighted average NDVI (AWANDVI). The invariant growth profile model suggested by Badhwar was fitted and spectral emergence date, maximum vegetative vigour, peak day value of profile, growth rate and senescence rate, area under the curve, etc. were derived. These parameters were related to the reported district-level wheat yields using multiple regression analysis. A field study was also conducted using a handheld spectro-radiometer at the research station of Punjab Agricultural University (PAU), Ludhiana. From this field experimental data, wheat growth profile parameters were derived which were compared with satellite based parameters. Inversion of the models was carried out to evaluate the results by comparing the reported and predicted wheat yields. The results indicate highly significant fitting of the NDVI profile to the Badhwar model as indicated by multiple linear correlation coefficients and Fisher test. A significant relationship between district-level wheat yields and fractional area under the curve was also observed. The overall correlation of 0.82 for Punjab and Haryana states was obtained between reported yield and growth profile derived parameters. Atmospheric normalisation resulted in improvement of prediction model statistics ( R increased from 0.42 to 0.86). Evaluation of the models indicated that 10 out of 16 districts of Haryana State and 9 out of 12 districts of Punjab State showed relative deviations within 10% between reported and model predicted wheat yields.     

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).     

GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

ContextIn software industry, project managers usually rely on their previous experience to estimate the number men/hours required for each software project. The accuracy of such estimates is a key factor for the efficient application of human resources. Machine learning techniques such as radial basis function (RBF) neural networks, multi-layer perceptron (MLP) neural networks, support vector regression (SVR), bagging predictors and regression-based trees have recently been applied for estimating software development effort. Some works have demonstrated that the level of accuracy in software effort estimates strongly depends on the values of the parameters of these methods. In addition, it has been shown that the selection of the input features may also have an important influence on estimation accuracy.ObjectiveThis paper proposes and investigates the use of a genetic algorithm method for simultaneously (1) select an optimal input feature subset and (2) optimize the parameters of machine learning methods, aiming at a higher accuracy level for the software effort estimates.MethodSimulations are carried out using six benchmark data sets of software projects, namely, Desharnais, NASA, COCOMO, Albrecht, Kemerer and Koten and Gray. The results are compared to those obtained by methods proposed in the literature using neural networks, support vector machines, multiple additive regression trees, bagging, and Bayesian statistical models.ResultsIn all data sets, the simulations have shown that the proposed GA-based method was able to improve the performance of the machine learning methods. The simulations have also demonstrated that the proposed method outperforms some recent methods reported in the recent literature for software effort estimation. Furthermore, the use of GA for feature selection considerably reduced the number of input features for five of the data sets used in our analysis.ConclusionsThe combination of input features selection and parameters optimization of machine learning methods improves the accuracy of software development effort. In addition, this reduces model complexity, which may help understanding the relevance of each input feature. Therefore, some input parameters can be ignored without loss of accuracy in the estimations.     

Estimating grassland biomass and leaf area index using ground and satellite data

We compared estimates of regional biomass and LAI for a tallgrass prairie site derived from ground data versus estimates derived from satellite data. Linear regression models were estimated to predict LAI and biomass from Landsat-TM data for imagery acquired on three dates spanning the growing season of 1987 using co-registered TM data and ground measurements of LAl and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topographic position were included as indicator variables in the models to acccount for variance in biomass and LAI not captured in the TM data. Our results show important differences in the relationships between Kauth-Thomas greenness (from TM), LAI, biomass and the various terrain variables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substantial differences were observed in June. In a number of cases, the regression models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas encompassing heterogeneous landcover the inclusion of categorical terrain data in calibration procedures is a useful technique.     

Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN,SVR and random forest regression algorithms

In a small case study of mixed hardwood Hyrcanian forests of Iran, three non-parametric methods, namely k-nearest neighbour (k-NN), support vector machine regression (SVR) and tree regression based on random forest (RF), were used in plot-level estimation of volume/ha, basal area/ha and stems/ha using field inventory and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Relevant pre-processing and processing steps were applied to the ASTER data for geometric and atmospheric correction and for enhancing quantitative forest parameters. After collecting terrestrial information on trees in the 101 samples, the volume, basal area and tree number per hectare were calculated in each plot. In the k-NN implementation using different distance measures and k, the cross-validation method was used to find the best distance measure and optimal k. In SVR, the best regularized parameters of four kernel types were obtained using leave-one-out cross-validation. RF was implemented using a bootstrap learning method with regularized parameters for decision tree model and stopping. The validity of performances was examined using unused test samples by absolute and relative root mean square error (RMSE) and bias metrics. In volume/ha estimation, the results showed that all the three algorithms had similar performances. However, SVR and RF produced better results than k-NN with relative RMSE values of 28.54, 25.86 and 26.86 (m³ ha^–1), respectively, using k-NN, SVR and RF algorithms, but RF could generate unbiased estimation. In basal area/ha and stems/ha estimation, the implementation results of RF showed that RF was slightly superior in relative RMSE (18.39, 20.64) to SVR (19.35, 22.09) and k-NN (20.20, 21.53), but k-NN could generate unbiased estimation compared with the other two algorithms used.     

Relaxation vegetation index in non-linear modelling of ground plant cover by satellite remote-sensing data

Abstract

In order to obtain a model equation for the calculation of percentage plant cover by multi-spectral radiances remotely-sensed by satellites, a regression procedure is used to connect space remote-sensing data to ground plant cover measurement. A traditional linear regression model using the normalized difference vegetation index (NDVI) is examined by remote-sensing data of the SPOT satellite and ground measurement of LCTA project for a test site at Hohenfels. Germany. A relaxation vegetation index (RVI) is proposed in a non-linear regression modelling to replace the NDVI in linear regression modelling to get a better calculation of percentage plant cover. The definition of the RVI is

where X _i is raw remote-sensing data in channel i. Using the RVI, the correlation coefficient between calculated and observed percentage plant cover for a test scene in 1989 reaches 0·9 while for the NDVI it is only 0·7; the coefficient of multiple determination R ² reaches 0·8 for the RVI while it is only 0·5 for the NDVI. Numerical testing shows that the ability of using the RVI to predict percentage plant cover by space remote-sensing data for the same scene or the scene in other years is much stronger than the NDVI.