Journal of the Indian Society of Remote Sensing

Image segmentation to create representative objects by region growing image segmentation techniques such as multi resolution segmentation (MRS) is mostly done through interactive selection of scale parameters and is still a subject of great research interest in object-based image analysis. In this study, we developed an optimum scale parameter selector (OSPS) tool for objective determination of multiple optimal scales in an image by MRS using eCognition software. The ready to use OSPS tool consists of three modules and determines optimum scales in an image by combining intrasegment variance and intersegment spatial autocorrelation. The tool was tested using WorldView-2 and Resourcesat-2 LISS-IV Mx images having different spectral and spatial resolutions in two areas to find optimal objects for ground features such as water bodies, trees, buildings, road, agricultural fields and landslides. Quality of the objects created for these features using scale parameters obtained from the OSPS tool was evaluated quantitatively using segmentation goodness metrics. Results show that OSPS tool is able determine optimum scale parameters for creation of representative objects from high resolution satellite images by MRS method.

Prioritization of river basin in semi-arid to sub-humid tropical region is important in the context of natural resources conservation and management. In this paper, the Gomti River basin was prioritized for soil–water resource conservation based on analysis of morphometric parameters and land use–land cover with RS and GIS techniques. Gomti River is a monsoon and groundwater-fed river; it is a tributary of Ganga River. The basin has been divided into seventeen sub-basins. ASTER (30 m) Digital Elevation Model data and LANDSAT—8 (30 m) satellite imagery were used to analyze the morphometric parameters and to generate land-use–land-cover data for the basin through supervised image classification technique. The linear, areal, and relief aspects of each sub-basins were determined. Morphometric analysis revealed that the basin is a fifth-order drainage system with a dendritic to sub-dendritic drainage pattern. Drainage patterns indicate that sub-surface materials are homogeneous and lack structural control over topography. The circulatory ratio value indicates the basin is an elongated shape. Low values of morphometric parameters indicate good permeability, infiltration, and coarse drainage texture, low relief, and elongated shape indicated good groundwater potential in the basin. The final prioritization result shows that the sub-basin 12 is at the highest priority with the greatest shortage of groundwater potential, so immediate water conservation measures are needed. Contrary, sub-basin 3 is at low priority that indicates the highest groundwater potential. This study suggests the primary priority to improve the water resource management in semi-arid regions at the basin level.

ISRO is launching a LiDAR instrument (LLRI) onboard Chandrayan-I. The LLRI will collect topographic data of lunar surface. Flying at an altitude of 100km the LLRI will have a footprint of 100m on the moon surface. Time of travel measurement, which is fundamental for topographic coordinate computation, depends upon the shape of the return pulse. This shape in turn is a function of the characteristics of footprint, i. e. its geometry, reflectance and roughness. This paper uses a mathematical model to simulate the return waveform at the receiver for different conditions of said characteristics within the footprint. Mathematical equations are employed to generate footprints that vary in their characteristics in terms of reflectance, geometry and roughness. A footprint is divided into small bins so that each bin has uniform property. Energy distribution in transmitted pulse is considered Gaussian. Energy irradiated over footprint is approximated using the assumed distribution. For each bin the energy incident is computed and accordingly the quantum and distribution of reflected energy is determined. The final waveform is generated by integrating the energy returned from all the bins according to their time of arrival and spread. Some results are presented to show the performance of the developed system.

UAV technology when aided with the unique data acquisition strategies, preprocessing techniques and analytical abilities of an established domain of remote sensing provide more affordable, customized and user-friendly option of “UAV-Remote Sensing”. This extended branch of remote sensing flourishes in both the mapping and measurement, if implemented in the ordered fashion to ensure remote sensing grade data. The current study integrates the potential of UAV technology to the high-resolution data classification approach of object-based image analysis. Department of Civil Engineering, Indian Institute of Technology-Roorkee, India, is selected as study area. In the first part of the study, a detailed UAV survey followed by UAV data processing was carried out to capture the VHR orthorectified image of the selected study area. In the second step, a comparative assessment of nearest neighbor (NN) and rule-based classifications were performed. Orthorectified image was segmented using a multi-resolution segmentation. The overall accuracy for NN and rule-based classifier were 95.13% and 93.87%, respectively. Detailed assessment of user accuracy and producer accuracy described that tree, road, solar panel and waterbody were more accurately classified with NN classifier, whereas building, grass land, open land and vehicle were more accurately classified with rule-based classifier.

The present study describes the ground based bistatic scatterometer measurements of ladyfinger crop at its various growth stages in the specular direction with the azimuthal angle ( $$ \phi = 0 $$ ) for the angular incidence angle ranging from 20° to 60° at the interval of 10° at HH and VV polarization. An outdoor ladyfinger crop bed of an area 4 × 4 m2 was specially prepared for the ground based bistatic scatterometer measurements. The crop growth variables like vegetation water content, leaf area index, fresh biomass, and plant height were also measured at the time of each bistatic scatterometer measurement. The specular bistatic scattering coefficients were found to be decreasing with the crop growth variables up to the maturity stage and then after it increased slightly. The linear regression analysis was carried out between specular bistatic scattering coefficient and crop growth variables at all the incidence angles for HH and VV polarization to select the optimum angle of incidence and polarization for the estimation of crop growth variables. The potential of subtractive clustering based adaptive neuro-fuzzy inference system was applied for the estimation of crop growth variables. The estimated values for different crop growth variables were found almost close to the observed values.

The current study attempts to extract the phenological variables of alluvial plain Shorea robusta-dominated tropical moist deciduous forest of Northeast India using threshold-based method. A total of 230 Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 time series normalized difference vegetation index (NDVI) datasets (year 2006–2015) has been used. Time series NDVI datasets were fitted to an adaptive Savitzky–Golay filter for smoothing. Important phenological matrices, namely start of season (SOS), end of season (EOS), length of season (LOS), peak of season (POS), seasonal amplitude, seasonally integrals (large and small integral), were evaluated. SOS varied from 106 to 120 day of year (DOY) (average of 110 ± 17.6), and EOS varied from 425 to 441 DOY (average of 431 ± 14.33). POS reaches in the month of September and October (average 262 ± 15). In the current study, a mean amplitude of 0.35, lower value of small integral (4.70 ± 0.34) and higher value of large integral (16.24 ± 0.25) signify that the studied forest is highly productive ecosystem, with semievergreen or moist deciduous canopy. Strong linear relationship of NDVI with temperature and rainfall was witnessed, particularly with a 1–2 month time lag. Also, NDVI-temperature correlation was found stronger than NDVI-precipitation correlation, suggesting that the area being a humid subtropical region, temperature plays a greater role in the timing of phenological events than rainfall and can act as a crucial factor for growth of the species under the climate changing scenario.

Analysis of the ecosystem service value (ESV) associated with the observed land use changes is critically important for environmental assessment and sustainable urban development. This research evaluate the effects of land use on ESV in Chengdu based on remotely sensed data and GIS, assessment model, transfer and flows model, sensitivity and variable ratio function. The results show that the ESV is mainly composed of forest land and cultivated land. ESV substantially and continuously decreased by 92 × 108 Chinese Yuan (CNY) or about 45 % in Chengdu during the study period and ESV from urban areas in the city center and its surroundings were much lower than those from the natural landscape in the northern and western Longmenshan mountainous region. Land use changes were the major causes of the observed transfer and flows in ESV. The flows of ESV both positive and negative were associated with observed land use changes, but overall land use changes had a negative impact on ESV. The results further indicate that the coefficient change relative to the ESV was inelastic, and the relative sensitivity index was as follows: forest land > cultivated land > water areas, but changes in water area had the largest influence and amplification effect on the ESV. This research offers important insights for use in planning fast urbanizing regions and can assist to achieve more sustainable land use and policy making.