Landsat thematic mapper studies of land cover spatial variability related to hydrology

The Landsat 4 and 5 Thematic Mapper (TM) provides increased spatial, spectral, and radiometric capability relative to the Multispectral Scanner (MSS). Visual inspection of TM imagery confirms this. Land cover detail is evident that would be of use in watershed management and planning activities. Specific studies have been conducted in Georgia, West Virginia, Michigan and Maryland to compare MSS and TM for urbanizing watersheds, wetlands, and floodplain mapping situations. These studies show that only modest improvements in classification accuracy (Anderson Level I/II) have been achieved using existing classification approaches. An attempt to identify the visibly apparent interstate highways and secondary and residential streets in TM data via conventional approaches failed due to an inability to derive separable spectral signatures. The basis for a non-parametric approach to classification is presented in which roads are identified by locating linear local minima in the greenness transformed dimension. Preliminary results indicate that such a method provides more reliable road locations than MSS or TM used singly.     

Examining LiDAR – WorldView-3 data synergy to generate a detailed stand map in a mixed forest in the north-west of Turkey

Although stand delineation approach based on aerial photographs and field survey produces high accuracy maps, it is labour-intensive and time consuming. Furthermore, conventional forest stand maps may have some uncertainties that can hardly be verified due to the experiments and skills of photo-interpreters. Therefore, researchers have been seeking more objective and cost-effective methods for forest mapping. LiDAR (Light Detection and Ranging) data have a high potential to automatically delineate forest stands. Unlike optical sensors, LiDAR height data provides information about both the horizontal and vertical structural characteristics of forest stands. However, it deprives of spectral data that may be successfully used in separating tree species. In this study, we investigate the potential of LiDAR – WorldView-3 data synergy for the automatic generation of a detailed forest stand map which can be used for a tactical forest management plan. Firstly, image segmentation was applied to LiDAR data alone and LiDAR/WorldView-3 data set in order to obtain the most suitable image objects representing forest stands. Visual inspection of the segmentation results showed that image objects based on the LiDAR/WorldView-3 data set were more compatible with the reference forest stand boundaries. After the segmentation process, the LiDAR and LiDAR/WorldView-3 data sets were independently classified using object-based classification method. We tested two levels of classification. The first was a detailed classification with 14 classes considering reference stand types. The second was the rough classification with 9 classes where some stand types were combined. The mean, standard deviation and texture features of LiDAR metrics and spectral information were used in the classification. The accuracy assessment results of LiDAR data showed that the Overall Accuracy (OA) was calculated as 0.31 and 0.43, and the Kappa Index (KIA) was calculated as 0.26 and 0.32 for the detailed and rough classifications, respectively. For the LiDAR/WorldView-3 data set, the OA values were calculated as 0.50 and 0.61, and the KIA were calculated as 0.46 and 0.55 for the detailed and rough classifications, respectively. These results showed that the forest stand map derived from the LiDAR/WorldView-3 data synergy is more compatible with the reference forest stand map. In conclusion, it can be said that the forest stand maps produced in this study may provide strategic forest planning needs, but it is not sufficient for tactical forest management plans.     

Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nanjing city,China

Vegetation fractional coverage (VFC) is an important vegetation parameter affecting exchanges of carbon, water, energy between the atmosphere and surface. In this study, the applicability of tonal and texture measures calculated using an IKONOS_2 image in retrieving VFC of forests was investigated in the urban area of Nanjing city, China. Four spectral vegetation indices (VI) and six texture measures (TEX) were related to VFCs acquired from in situ measurements. Models for estimating VFC based on VIs or/and TEXs were established and validated for planted low broad-leaf forest plots (PLB), planted mature forest plots (PMF), natural broad-leaf forest plots (NBF), and all forest plots (ALLv), respectively. The results show that high spatial resolution remote sensing data is applicable to estimate VFC in urban areas, and TEXs may act as effective supplements of vegetation indices (VIs) for the retrieval of VFC. VIs are suitable for VFC estimation of mature forests (such as NBF and PMF) with high vegetation density, and TEXs can yield a more accurate estimate for planted forests (such as PLB and PMF) with regular spatial distribution if they are calculated with proper parameters, such as window size. The combination of VIs and TEXs improve the estimation of VFC if forest types are not previously differentiated. The results can be used as a reference for determining effective spectral or texture parameters in VFC estimation under similar environmental conditions according to vegetation maturity and regularity.     

A strategy for mineral and energy resource independence

Data acquired by Landsats 1, 2, and 3, are beginning to provide the information on which an improved mineral and energy resource exploration strategy can be based. Landsat 4 is expected to augment this capability with its higher resolution (30 m) and additional spectral bands in the Thematic Mapper (TM) designed specifically to discriminate clay minerals associated with mineral alteration. In addition, a new global magnetic anomaly map, derived from the recent Magsat mission, has recently been compiled by the National Aeronautics and Space Administration (NASA), the U.S. Geological Survey (USGS), and others. Preliminary, extremely small-scale renditions of this map indicate that global coverage is nearly complete and that the map will improve upon a previous one derived from Polar Orbiting Geophysical Observatory (POGO) data. Digital processing of the Landsat image data and Magsat geophysical data can be used to create three-dimensional stereoscopic models for which Landsat images provide surface reference to deep structural anomalies.Comparative studies of national Landsat lineament maps, Magsat stereoscopic models, and metallogenic information derived from the Computerized Resources Information Bank (CRIB) inventory of U.S. mineral resources, provide a way of identifying and selecting exploration areas that have mineral resource potential. Landsat images and computer-compatible tapes can provide new and better mosaics and also provide the capability for a closer look at promising sites.     

Experiment for mapping land cover and it’s change in southeastern Sri Lanka utilizing 250 m resolution MODIS imageries

Utilizing freely available MODIS NDVI and Natural color imageries of 250 m spatial resolution produced by NASA, an experiment was made to map land-cover and its change with an emphasis on vegetation cover in southeastern Sri Lanka, which plays a vital role for control of green house gas. For the change detection purpose, 1987 land cover map made by present authors from Landsat MSS image and extensive ground truth survey data was used as the base map. The result of the experiment shows that MODIS data are useful to make a land cover map of 250 m spatial resolution for tropical areas with high cloud coverage like Sri Lanka. It was found that the forest cover decrease amounted as large as 21% in 19 years time span in southeastern Sri Lanka, the prominent forest region of the country. On the other hand homestead/vegetation and mixed vegetation/scrub dominant categories increased by 13.7% and 7.1%, respectively. These changes are considered due to a large clearance of forest areas for agriculture and building houses to accommodate increasing inhabitants.     

Preliminary evaluation of the landsat-4 thematic mapper data for mineral exploration

Landsat-4 Thematic Mapper (TM) data recorded over an arid terrain were analyzed to determine the applicability of using of TM data for identifying and mapping hydrothermally altered, potentially mineralized rocks. Clays, micas, and other minerals bearing the OH anion in specific crystal lattice positions have absorption bands in the 2.2-μm region (TM channel 7, TM7) and commonly lack features in the 1.6-μm region (TM5). Channel ratios TM5/TM7, TM5/TM4, and TM3/TM1 were combined into a color-ratio-composite (CRC) image and used to distinguish hydrothermally altered rocks, unaltered rocks, and vegetation. These distinctions are made possible by using the TM5 and TM7, channels which are not available in the Landsat multispectral scanner (MSS). Digital masking was used to eliminate ambiguities due to water and shadows. However, some ambiguities in identification resulted between altered volcanic rocks and unaltered sedimentary deposits that contained clays, carbonates, and gypsum, and between altered volcanic rocks and volcanic tuffs diagenetically altered to zeolites. However, compared to MSS data, TM data should greatly improve the ability to map hydrothermally altered rocks in arid terrains.     

Object-based rice mapping using time-series and phenological data

Remote sensing techniques are often used in mapping rice, but high quality time-series remote sensing data are difficult to obtain due to the cloudy weather of rice growing areas and long satellite revisit interval. As such, rice mapping is usually based on mono-temporal Landsat TM/ETM+ data, which have large uncertainties due to the spectral similarity of different vegetation types. Moreover, conventional pixel-based classification method is unable to meet the required accuracy for rice mapping. Therefore, this study proposes a new strategy for mapping rice in cloud-prone areas using fused data of Landsat-8 OLI time-series and phenological parameters, based on the object-based method. We determine the critical growth stages of paddy rice from observed phenological data and MODIS-NDVI time-series data. The spatial and temporal adaptive reflectance fusion model (STARFM) is used to blend the MODIS and Landsat data to obtain a multi-temporal Landsat-like dataset for classification. Finally, an object-oriented algorithm is used to extract rice paddies from the Landsat-like, time-series dataset. The validation experiments show that the proposed method can provide high accuracy rice mapping, with an overall accuracy of 92.38% and a kappa coefficient of 0.85.     

Remote sensing salt marsh biomass and stress detection

A hand-held radiometer was used to gather spectral radiance data simulating bands 3, 4 and 5 of the Landsat-D Thematic Mapper. Variations in biomass of the salt marsh plant $\text{Spartina alterniflora}$ were highly correlated to changes in spectral radiance expressed as the vegetation index or the infrared index. Negative stresses like increased soil salinity and increased concentrations of copper or zinc yielded reductions in biomass which were detected spectrally. Positive stresses like freshwater and sewage effluent additions produced an increase in biomass which also were detected using spectral data. The demonstrated detection of biomass from spectral data was expanded spatially and temporally to estimate net primary productivity of a salt marsh. Remote sensing estimates of production ranged from 5 to 20% of estimates from harvest data. Future applications of this biomass estimation technique, employing data gathered from satellite platforms and from the ground, are discussed for salt marsh systems.     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

Landsat image data quality studies

The July 1982 launch of Landsat-4 was immediately followed by a two-year comprehensive set of detailed investigations sponsored by the National Aeronautics and Space Administration (NASA) at Goddard Space Flight Center (GSFC). The Landsat Image Data Quality Analysis (LIDQA) research plans for these investigations were specified prior to launch, so that minimum time would be lost in assessing the performance of the long-awaited Thematic Mapper (TM) sensor that Landsat-4 carried in addition to a fourth Multispectral Scanner (MSS). The LIDQA investigations have been substantially completed, and have shown that the TM is a very good spaceborne multispectral radiometer, and has met or exceeded most of its design goals. TM's new short-wave infrared (SWIR) spectral capability yielded improved mineral and plant discrimination compared to the MSS, as anticipated by ground-based and airborne TM simulations. Moreover, the improved spatial resolution and geometric accuracy of Landsat-4 and the TM have resulted in satellite image maps exceeding 1:100,000 U.S. map accuracy standards. Finally, based on an information entropy measure, principal component analysis, and classification results, TM data has been shown to approach its theoretical limit in information content per pixel, exceeding the MSS by at least a factor of two.     

Estimating canopy cover in drylands with Landsat MSS data

The relationship between canopy cover and spectral characteristics of the corresponding areas was studied in a semi-arid savannah environment in Kordofan, The Sudan.The canopy cover was measured in 32 test plots through air photo interpretation. Achieved values were correlated with multitemporal Landsat MSS raw data and manipulated data.The highest correlation coefficients in general were obtained between crown cover and spectral data recorded during the dry season.The inverse relationship between amount of woody vegetation and nIR reflectance (MSS 6, MSS 7) was striking. This implied that other factors than a healthy foliage characterized the spectral responses.Destructive measurements of woody biomass were carried out to establish a relationship between woody wet weight and crown diameter for future biomass studies.     

Sensor data for forest policy information

NASA-Ames Research Center has investigated the role and performance capabilities of the Landsat Multi-Spectral Scanner (MSS) for forest policy analysis for the past four years in cooperation with the California Department of Forestry. A thorough series of studies, from a statewide land cover map to smaller, highly detailed studies including collateral data, have been conducted with a view to comprehensive forest policy needs. The strengths and limitations of MSS data have been evaluated. Some observations about the information needed from new satellite sensors such as the Thematic Mapper are discussed against this background.     

Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex,south Iran

Spectral transformation methods, including correlation coefficient (CC) and Optimum Index Factor (OIF), band ratio (BR) and principal component analysis (PCA) were applied to ASTER and Landsat TM bands for lithological mapping of Soghan ophiolitic complex in south of Iran. The results indicated that the methods used evidently showed superior outputs for detecting lithological units in ophiolitic complexes. CC and OIF methods were used to establish enhanced Red–Green–Blue (RGB) color combination bands for discriminating lithological units. A specialized band ratio (4/1, 4/5, 4/7 in RGB) was developed using ASTER bands to differentiate lithological units in ophiolitic complexes. The band ratio effectively detected serpentinite dunite as host rock of chromite ore deposits from surrounding lithological units in the study area. Principal component images derived from first three bands of ASTER and Landsat TM produced well results for lithological mapping applications. ASTER bands contain improved spectral characteristics and higher spatial resolution for detecting serpentinite dunite in ophiolitic complexes. The developed approach used in this study offers great potential for lithological mapping using ASTER and Landsat TM bands, which contributes in economic geology for prospecting chromite ore deposits associated with ophiolitic complexes.     

Soil moisture mapping over the Chinese Loess Plateau using ENVISAT/ASAR data

In this paper, we used the available algorithm for soil moisture estimation over LOPEX05 (the Loess Plateau land surface process Experiment (2005)) area. The available algorithm used ENVISAT/ASAR AP mode VV polarization observational data at a low incidence angle and ground measured soil moistures. The ground measurements were performed in the summer of the 2005 during the LOPEX05 field campaign. The validated results indicate that an average difference between the soil moistures estimated from the microwave remote sensing and ground measurements is less than 0.02 cm³/cm³, with a RMS error of 2.0%, and a maximum less than 0.04 cm³/cm³. The algorithm was applied to the surface soil moisture mapping later. The results show that this algorithm is suitable for monitoring soil moisture information of the agricultural fields over the Chinese Loess Plateau, when ground land cover situation and the resolution of imagery data are taken into account. However, we also find that there are large differences over the steep slope region, the edge of mesa. The results are not acceptable for surface soil moisture estimation in these regions. Thus, the surface soil moisture retrieval in the steep slope region of the Loess Plateau need to be further investigated in the future.     

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

Early evaluation of thematic mapper data for coastal process studies

Two sets of TM data taken over the ocean off the coast of the Southeastern U.S. Bight were studied for the applicability of TM data to marine environments. First, the results of applying TM and TMS data to determine chlorophyll concentration in the ocean are presented. Chlorophyll quantification in the range of 0.5 to 2.0 mg/M³ was achieved by taking the ratio of TM band-1/band-2. Second, the results of applying TM band-6 data to monitor sea surface temperature are described. A comparison of TM data with AVHRR data shows TM readings coincide with AVHRR data within a scatter of 0.5°C in most of areas studied. Lastly, the results of a technique to map the water depths of coral reefs in the Great Bahama Bank are demonstrated. Depths from 0 to 20 meters, were delineated using TM band-1. The classification accuracy and origins of anomalous depth points are discussed.     

Discrimination of rock types and main rock-forming components in Bulgarian territories through spectral reflectance characteristics

Remote sensing studies on the spectral reflectance of natural formations – rocks, products of different physical and chemical processes in the Earth’s crust and its surface – are reported. Based on spectral reflectance characteristics (SRC) of selected representative rocks with different structure, mineral composition and colour characteristics – sedimentary (psephite-conglomerate) and metamorphic (kyanite schist) – and by applying statistical and deterministic methods the rock types and the main rock-forming components were discriminated. The spectral data were obtained in laboratory using a multichannel spectrometer developed in STIL-BAS, which delivers data in the spectral range 480–810 nm.The a priori mineralogical information about the specimens studied, the actual SRC data, affine and perspective transformations of the colour coordinates of SRC were all utilized to perform a primary classification of the main constituents of the specimens. Cluster analysis was applied and its results were used to design the necessary grouping variables as required for the implementation of linear discriminant analysis. The discriminant functions were constructed from the SRC values for a selected set of wavelengths and of transformed data. Classification of the main ingredients and the rock types were obtained. The proposed novel approach to the spectral discrimination of main rock-forming components is believed relevant for predictive classification of other specimens of rocks through discriminant functions.     

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.     

基于GPS的广州地区电离层闪烁谱分析

利用广州站GPS电离层闪烁监测仪2014和2015年的观测数据,从150例典型闪烁事件中提取出4820例闪烁谱和闪烁谱指数,分析发现广州地区闪烁对应单幂律谱和双幂律谱两大典型闪烁谱.这些闪烁谱可以细分为5类,初步分析了这5类闪烁谱的特征和产生原因.通过统计分析发现,谱指数分布在0.63~9.57,平均谱指数为3.86,78.5%以上的谱指数分布在0~6,83.4%中弱闪烁强度的谱指数值分布在0~7,89.27%的强闪烁强度的谱指数分布在5~10,谱指数随闪烁强度的增强有上升趋势.      

Red edge measurements for remotely sensing plant chlorophyll content

The feasibility of using the wavelength of the maximum slope of the red edge of leaf reflectance spectra (λ_re) as an indication of plant chlorophyll status was examined in the laboratory for single leaves of several species. λ_re for each sample was determined by derivative reflectance spectroscopy. A high positive correlation was found between λ_re and leaf chlorophyll content for all species, although there were some differences in the quantitative nature of the relationship for plants of different types. The position of the red edge was found to be unaffected by simulated change in ground cover, but multiple leaf layers produced a shift in its position. Appropriate spectral measurements and processing for obtaining useful information from the red edge are discussed, and the potential of the red edge in relation to other spectral measurements is considered.