Reclamative grouping of ravines using Cartosat-1 PAN stereo data

Information on the depth and bed width of ravines (network of gullies) at large scales is critical for their reclamation and management. Hitherto such information has been generated from aerial photographs and space borne stereo images with medium to coarse ‘z’ – axis resolution. The present study, aims at demonstrating the potential of Cartosat ?1 (an Indian Earth observations satellite) stereo images with 2.5 m spatial resolution in deriving morphometric information on ravines for their reclamative grouping. The study area is a part of Jhansi and Hamirpur districts of Uttar Pradesh, northern India. The approach involves acquiring precise ground control points using Differential GPS (DGPS), triangulation, DEM extraction and generation of ortho image as well as anaglyphs for stereo viewing. The depth and bed width of ravines were measured in the field for validation. A comparison with field observations reveal that the bed width of ravines and depth can be measured successfully with Carto-1 stereo data. The anaglyph data was used to delineate various categories of ravines based on their depth and bed width. Results indicate that the Cartosat-1 stereo images are quite suitable for delineation of three categories of ravines, namely shallow (<3 m deep and <18 m bed width), medium deep (3–9 m deep and >18 m bed width) and deep (>9 m deep) which are important for their reclamation.     

Evaluation and Comparison of Multi Resolution DEM Derived Through Cartosat-1 Stereo Pair – A Case Study of Damanganga Basin

The study evaluates and compares Digital Elevation Model (DEM) data of various grid spacing derived using high resolution Cartosat 1 stereo data for hydrologic applications. DEM is essential in modeling different environmental processes which depend on surface elevation. The accuracy of derived DEM varies with grid spacing and source. The CartoDEM is the photogrammetric DEM derived from stereo pairs. Damanganga basin lying in the Western Ghats was analysed using 11 Carto stereo pairs. The process of triangulation resulted in RMSE of 0.42. DEM was extracted at 10 m, 20 m, 30 m, 40 m, 50 m and 90 m grid spacing and compared with ASTER GDEM (30 m) and SRTM DEM (90 m). DEM accuracy was checked with Root Mean Square Error (RMSE) statistic for random points generated in different elevation zones. Extracted stream networks were compared based on Correctness Index and Figure of Merit index, calculated for all the Digital Elevation Models at varying cell sizes. In order to further evaluate the DEM’s, a simple flood simulation with no water movement and no consideration of real time precipitation data was carried out and relationship between heights of flood stage and inundation area for each Digital Elevation Model was also established.     

Estimation of Above Ground Biomass Using Texture Metrics Derived from IRS Cartosat-1 Panchromatic Data in Evergreen Forests of Western Ghats,India

Assessment of above ground forest biomass (AGB) is essential in carbon modelling studies to provide mitigation strategies as demonstrated by reducing emissions from deforestation and forest degradation. Several researchers have demonstrated the use of remote sensing data in spatial AGB estimation, in terms of spectral and radar backscatter based approaches at a landscape scale with several known limitations. However, these methods lacked the predictive ability at high biomass ranges due to saturation. The current study addresses the problem of saturation at high biomass ranges using canopy textural metric from high resolution optical data. Fourier transform based textural ordination (FOTO) technique, which involves deriving radial spectrum information via 2D fast Fourier transform and ordination through principal component analysis was used for characterizing the textural properties of forest canopies. In the current study, plot level estimated AGB from 15 (1 ha) plots was used to relate with texture derived information from very high resolution datasets (viz., IKONOS and Cartosat-1). In addition to the estimation of high biomass ranges, one of the prime objective of the current study is to understand the effects of spatial resolution on deriving textural-AGB relationship from 2.5 m IRS Cartosat data (Cartosat-A, viewing angle = ?5°) to that of IKONOS imagery with near nadir view. Further, since texture is impacted by several illumination geometry issues, the effect of viewing geometry on the relationship was evaluated using Cartosat-F (Viewing angle = 26°) imagery. The results show that the FOTO method using stereo Cartosat (A and F) images at 2.5 m resolution are able to perform well in characterizing high AGB values since the texture-biomass relationship is only subjected to 18 % relative error to that of 15 % in case of IKONOS and could aid in reduction of uncertainty in AGB estimation at a large landscape levels.     

Improving Cartosat-1 DEM accuracy using synthetic stereo pair and triplet

Cartosat–1 is the first Indian Remote Sensing Satellite capable of providing along-track stereo images. Cartosat–1 provides forward stereo images with look angles +26° and −5° with respect to nadir for generating Digital Elevation Models (DEMs), Orthoimages and value added products for various applications. A pitch bias of −21° to the satellite resulted in giving reverse tilt mode stereo pair with look angles of +5° and −26° with respect to nadir. This paper compares DEMs generated using forward, reverse and other possible synthetic stereo pairs for two different types of topographies. Stereo triplet was used to generate DEM for Himalayan mountain topography to overcome the problem of occlusions.For flat to undulating topography it was shown that using Cartosat-1 synthetic stereo pair with look angles of −26° and +26° will produce improved version of DEM. Planimetric and height accuracy (Root Mean Square Error (RMSE)) of less than 2.5 m and 2.95 m respectively were obtained and qualitative analysis shows finer details in comparison with other DEMs. For rugged terrain and steep slopes of Himalayan mountain topography simple stereo pairs may not provide reliable accuracies in DEMs due to occlusions and shadows. Stereo triplet from Cartosat-1 was used to generate DEM for mountainous topography. This DEM shows better reconstruction of elevation model even at occluded region when compared with simple stereo pair based DEM. Planimetric and height accuracy (RMSE) of nearly 3 m were obtained and qualitative analysis shows reduction of outliers at occluded region.     

Evaluation of digital elevation models for delineation of hydrological response units in a Himalayan watershed

This study reports results from evaluation of the quality of digital elevation model (DEM) from four sources viz. topographic map (1:50,000), Shuttle Radar Topographic Mission (SRTM) (90 m), optical stereo pair from ASTER (15 m) and CARTOSAT (2.5 m) and their use in derivation of hydrological response units (HRUs) in Sitla Rao watershed (North India). The HRUs were derived using water storage capacity and slope to produce surface runoff zones. The DEMs were evaluated on elevation accuracy and representation of morphometric features. The DEM derived from optical stereo pairs (ASTER and CARTOSAT) provided higher vertical accuracies than the SRTM and topographic map-based DEM. The SRTM with a coarse resolution of 90 m provided vertical accuracy but better morphometry compared to topographic map. The HRU maps derived from the fine resolution DEM (ASTER and CARTOSAT) were more detailed but did not provide much advantage for hydrological studies at the scale of Sitla Rao watershed (5800 ha).     

Synergistic Application of Optical and Radar Data for Archaeological Exploration in the Talakadu Region,Karnataka

Talakadu is a well known historic place situated on bank of the river Cauvery in Mysore district of Karnataka. The place is close to concave side of a prominent meander where large amount of sand has accumulated. It is believed that after construction of a reservoir upstream, sand was exposed to wind action burying the structures of Ganga dynasty and other later kingdoms. A number of buried sites have been identified by archaeological excavations conducted so far. Presently the area forms sand dunes with thick plantation cover. Analysis of RADAR data (fine beam RADARSAT and ENVISAT ASAR) led to identifying a hitherto unknown buried channel through the Old Talakadu town adjoining the excavated archaeological sites. The study suggests that RADAR penetration through the plantation canopy seems to have occurred as observed by comparing with corresponding optical data of LISS-IV. Below the canopy, sand and shrubs on top of the channel (topographically low area) are acting as smooth surface providing dark tone on radar imagery. During field validation GPS was extensively used to navigate through the forest canopy and locate the buried channel, excavated archaeological sites as well as other anomalous patterns. Synergistic application of optical (RESOURCESAT-1 LISS-IV and CARTOSAT-1 & 2) and radar (fine beam RADARSAT and ENVISAT ASAR) data led to identifying remote sensing based guides for archaeological exploration. Integration of known archaeological sites with the identified anomalous patterns was done in GIS environment. This study adds on to the knowledge base of the site and compliments already known information and suggested new areas for further archaeological exploration.     

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

Validation of Indian National DEM from Cartosat-1 Data

CartoDEM is an Indian National DEM generated from Cartosat-1 stereo data. Cartosat-1, launched in May, 2005, is an along track (aft ?5°, Fore +26°) stereo with 2.5 m GSD, give base-height ratio of 0.63 with 27 km swath. The operational procedure of DEM generation comprises stereo strip triangulation of 500?×?27 km segment with 10 m posting along with 2.5 m resolution ortho image and free—access posting of 30 m has been made available (bhuvan.nrsc.gov.in). A multi approach evaluation of CartoDEM comprising (a) absolute accuracy with respect to ground control points for two sites namely Jagatsinghpur -flat and Dharamshala- hilly; second site i.e. Alwar-plain and hilly with high resolution aerial DEM, (b) relative difference between SRTM and ASTERDEM (c) absolute accuracy with ICESat GLAS for two sites namely Jagatsinghpur-plain and Netravathi river, Western Ghats-hilly (d) relative comparison of drainage delineation with respect to ASTERDEM is reported here. The absolute height accuracy in flat terrain was 4.7 m with horizontal accuracy of 7.3 m, while in hilly terrain it was 7 m height with a horizontal accuracy of 14 m. While comparison with ICESat GLAS data absolute height difference of plain and hilly was 5.2 m and 7.9 m respectively. When compared to SRTM over Indian landmass, 90 % of pixels reported were within ±8 m difference. The drainage delineation shows better accuracy and clear demarcation of catchment ridgeline and more reliable flow-path prediction in comparison with ASTER. The results qualify Indian DEM for using it operationally which is equivalent and better than the other publicly available DEMs like SRTM and ASTERDEM.     

Characterization of ASTER GDEM elevation data over vegetated area compared with lidar data

Current researches based on areal or spaceborne stereo images with very high resolutions (<1 m) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is the state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 m) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that the accurate coregistration between ASTER GDEM and national elevation dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-second and further improved to 0.6 if only homogenous vegetated areas were considered.     

Multispectral Remote Sensing Data Analysis and Application for Detecting Moats Around Medieval Settlements in South India

This work explores the potential of multispectral imagery in identifying dried and buried moats, and possibly any adjacent fortifications of medieval sites in South India. Vegetation marks in the form of geometrical patterns have been one of the key signatures indicating archaeological sites. To explore this three of well known sites from Karnataka in south India–Belur, Halebidu and Somanathapura–were chosen as their historical accounts mention that they were townships which had circumscribing artefacts such as fort/wall or moats that at present are not easily detected from conventional exploration. These three sites belong to Hoysala dynasty, a period when a systematic town planning was followed based on cultural aspects such as the religion or faith followed by the inhabitants of respective sites. Traces of specific configuration of moats can be detected around each of them. The present work investigates the possibility of identifying these artefacts on space imageries through spatial and spectral distinction along with synoptic views and use of appropriate image processing and analysis techniques.     

Identification of archaeological buried remains based on the normalized difference vegetation index (NDVI) from Quickbird satellite data

In this study, Quickbird normalized difference vegetation index (NDVI) data were used in order to assess their capability in the field of archaeological prospection. The investigations were performed for a test case (Jure Vetere in the south of Italy) that is characterized by the presence of dense vegetation mainly composed by herbaceous plants. The results showed the high capability of QuickBird NDVI to enhance the typical surface anomalies linked to the presence of archaeological buried remains. The detected anomalies were confirmed by independent investigations based on geophysical prospections performed in 2005.     

DEM Extraction in Urban Areas Using High-Resolution TerraSAR-X Imagery

Three-dimensional (3D) spatial information is crucial for improving the quality of human life through urban planning and management, and it is widely utilized due to its rapid, periodic and inexpensive acquisition. In this context, extraction of digital surface and elevation models (DSM and DEM) is a significant research topic for space-borne optical and synthetic aperture radar (SAR) remote sensing. The DSMs include visible features on the earth’s surface such as vegetation, forest and elevated man-made objects, while DEMs contain only the bare ground. In this paper, using TerraSAR-X (TSX) high resolution Spotlight (HS) images, high-resolution interferometric DEM generation in a part of Istanbul urban area is aimed. This is not an easy task because of SAR imaging problems in complex geometry of urban settlements. The interferometric processing steps for DSM generation were discussed including critical parameters and thresholds to improve the quality of the final product and a 3 m gridded DSM was generated. The DSM-DEM conversion was performed by filtering and the quality of generated DEM was verified against a reference DEM from stereo photogrammetry with 3 m original grid spacing. The achieved root mean square error of height differences (RMSZ) varies from 7.09 to 8.11 m, depending on the terrain slope. The differential DEM, illustrates the height differences between generated DEM and the reference DEM, was generated to show the correlation between height differences and the coherence map. Finally, a perspective view of test area was created draping extracted DEM and a high-resolution IKONOS panchromatic image.     

基于控制点约束影像的密集匹配及其在考古发掘中的应用

针对大场景考古挖掘现场的三维重建情况,选取半全局匹配策略作为研究基础,设计了一种基于附加控制点约束的半全局匹配算法进行序列影像密集匹配。将稀疏匹配中的特征提取和匹配技术用于提取初始特征,并由这些初始特征转化的同名特征点,生成视差空间影像中的视差控制点,以此作为一种可靠的约束,提高其密集匹配的精度。同时,采用影像分块的策略,将原始大核线影像分成若干对小核线影像,进行密集匹配,以达到提高计算效率,改善计算结果的目的。实验证明,该方法可实现大面积考古挖掘现场的快速三维重建,并且能够在进行文物形态三维重建的同时获取挖掘现场文物分布的正射影像平面图,为准确记录挖掘现场文物分布位置及考古发掘、调查及遗址保护规划编制等提供科学依据。     

What governs the presence of residual vegetation in boreal wildfires?

Wildfires are frequent boreal forest disturbances in Canada, and emulating their patterns with forest harvesting has emerged as a common forest management goal. Wildfires contain many patches of residual vegetation of various size, shape, and composition; understanding their characteristics provides insights for improved emulation criteria. We studied the occurrence of residual vegetation within eleven boreal wildfire events in a natural setting; fires ignited by lightning, no suppression efforts, and no prior anthropogenic disturbances. Relative importance of the measurable geo-environmental factors and their marginal effects on residual presence are studied using Random Forests. These factors included distance from natural firebreaks (wetland, bedrock and non-vegetated areas, and water), land cover, and topographic variables (elevation, slope, and ruggedness index). We present results at spatial resolutions ranging from four to 64 m while emphasizing four and 32 m since they mimic IKONOS- and Landsat-type images. Natural firebreak features, especially the proximity to wetlands, are among the most important variables that explain the likelihood residual occurrence. The majority of residual vegetation areas are concentrated within 100 m of wetlands. Topographic variables, typically important in rugged terrain, are less important in explaining the presence of residuals within our study fires.     

A Method to Determine Appropriate Spatial Resolution for Hard Image Classification

This study analyzed the relationship between the spatial resolution and the hard classification effect based on pixel-based image classification, and then discussed how to determine appropriate spatial resolution. Thematic maps of winter wheat derived from 250 m MODIS image, 19.5 m China-Brazil Earth Resources Satellite (CBERS) image, and 2.44 m QuickBird image were used to examine the classification effect as a case study. It indicated that the “Pareto Boundaries” and the “within-class variability” could be used to determine the coarsest and the highest resolution for hard classification, respectively. The methods proposed in this study should be useful to guide how to select appropriate spatial resolution for land cover mapping.     

Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka,Southern India using hyperspectral remote sensing data

Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33–1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.     

Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal,South Africa

The objective of this study was to identify an appropriate spatial resolution for discriminating forest vegetation at subspecies level. WorldView-2 imagery was progressively resampled to coarser spatial resolutions. At a compartment level, 30 × 30-m subsets were generated across forest compartments to represent the five forest subspecies investigated in this study. From the centre of each subset, the spatial resolution of the original WorldView-2 image was resampled from 6 to 34-m, with increments of 4-m. The variance was then calculated at every resampled spatial resolution using each of the eight WorldView-2 bands. Based on the sampling theorem, the 3-m spatial resolution provided an appropriate resolution for all subspecies investigated. The WorldView-2 image was subsequently classified using the partial least squares linear discriminant analysis algorithm and the appropriate spatial resolution. An overall classification accuracy of 90% was established with an allocation disagreement of 9 and a quantity disagreement of 1.     

Rule-based classification of high-resolution imagery over urban areas in New York City

This paper describes the integration of results from different feature extraction algorithms using spectral and spatial attributes to detect specific urban features. Methodology includes segmentation of IKONOS data, computing attributes for creating image objects and classifying the objects with fuzzy logic and rule-based algorithms. Previous research reported low class accuracies for two specific classes – dark and grey roofs. A modified per-field approach was employed to extract urban features. New rule-sets were used on image objects having similar or near-similar spectral and spatial characteristics. Different algorithms using spectral and spatial attributes were developed to extract specific urban features from a time-series of Multi-Spectral Scanner (MSS) (4 m × 4 m) IKONOS data. The modified approach resulted in a remarkable improvement in the accuracy of classes that registered low spectral seperability and therefore low accuracy. The spectral and spatial based classification model may be useful in mapping heterogeneous and spectrally similar urban features.     

An Analysis of the Marginal Value of Hyperspectral Features of the Mixed Pixel of Lotus Leaf and Water Body

A linear regression and curve fitting method is used to study the mixed pixel of lotus leaf and water body under the experimental conditions. The marginal value of hyperspectral features of vegetation is discussed. Through an analysis of the regression result and fitted equation, a conclusion is reached as follows: Until the area ratio of the lotus leaf is 5.9 %, the vegetation reflection features cannot be observed even factually there are some lotus leaves in the mixed pixel; when it is 10.7 %, the reflection spectrum curve saturates and the water body reflection features cannot been detected. The result indicates that although the area ratio of the lotus changes the reflection features may not change. This research will help to understand the mixed pixel and the liner spectrum unmixing technique further.     

Estimation of damage to agriculture biomass due to Hudhud cyclone and carbon stock assessment in cyclone affected areas using Landsat-8

Developing countries are vulnerable to tropical cyclones due to climatic variability and the frequency and magnitude of some extreme weather and disaster events is likely to increase. Cities and towns situated along the coastal belt of Visakhapatnam district experienced severe damage because of Hudhub cyclone (12 October 2014). The main objective of this research was to identify and quantify the damage to agriculture and vegetation caused by Hudhud cyclone. In this study, landsat-8 satellite data-sets acquired before and after the cyclone have been used; image processing techniques have been carried out to assess the changes of pre- and post-cyclone condition. Economic loss of agriculture crops has been assessed using crop production loss per hectare and total economic loss for agriculture crops in the study area was calculated. Classification results and land use land cover change analysis show that 13.25% of agriculture-Kharif and 31.1% of vegetation was damaged. Crop Biomass was estimated with aid of crop conversion factor for pre- and post-cyclone conditions. Total ‘Above ground biomass’ of the agriculture crop area was estimated at 31.57 t/ha and total loss of biomass was assessed to be 4.2 t/ha. Carbon stock was found to be varying from 0.3 to 8.3 t.C/ha in specific agriculture crops. From the results, it was concluded that Hudhud has done significant damage to the rural and urban areas of Visakhapatnam. The outcome of this study can be used by decision-makers for the release of post disaster relief fund to affected areas.