Journal of the Indian Society of Remote Sensing

In image interpretation, to make maximum use of the available information, some sort of enhancement is usually needed. In image enhancement, the aim is to manipulate the image to improve its quality. Image enhancement techniques emphasize upon the viewing of the image for extraction of information that may not have been so readily apparent in the original. Hera the software package for various enhancement techniques that has been developed at National bemote Sensing Agency (NRSA) is described. In order to illustrate the utility of the techniques, the enhanced imagery have been analysed for land use categories and linear features, and the corresponding thematic maps have also been shown in the results.

For a satellite sensor with only one or two thermal infrared channels, it is difficult to retrieve the surface emissivity from the received emissive signal. Empirical linear relationship between surface emissivity and red reflectance are already established for deriving emissivity, but the inner physical mechanism remains unclear. The optical constants of various minerals that cover the spectral range from 0.44 to 13.5 μm in conjunction with modern radiative transfer models were used to produce corresponding surface reflectance and emissivity spectra. Compared to the commonly used empirical linear relationship, a more accurate multiple linear relationship between Landsat TM5 emissivity and optical reflectances was derived using the simulated data, which indicated the necessity of replacing the empirical relationship with the new one for improving surface emissivity estimate in the single channel algorithm. The significant multiple linear relationship between broadband emissivity (BBE, 8–13.5 μm) and MODIS spectral albedos was also derived using the same data. This paper demonstrates that there is a physical linkage between surface emissive and reflective variables, and provides a theoretical perspective on estimating surface emissivity for sensors with only one or two thermal infrared channels.

Applications of unmanned aerial vehicles (UAVs) based remote sensing is increasing rapidly due to their advanced accessibility, capability for fast and easy deployment, capability for miniaturization of sensors and efficient collection of remotely-sensed data from relatively low altitudes. Recently, UAV data sets have been found to be quite useful for forest feature identification due to their relatively high spatial resolution. Several machine learning algorithms have been broadly used for remotely-sensed image classification. In remote sensing image classification, deep learning based methods can be considered quite effective techniques as they have achieved promising results. In this study, we have used deep learning based supervised image classification algorithm and images collected using UAV for classification of forest areas. The deep learning algorithm stacked Auto-encoder has been found to have tremendous potential regarding image classification and the assessment of forest coverage area. Our experimental results show that deep learning method provides better accuracy compared to other machine learning algorithms. Cross-validation showed that the overall accuracy of the deep learning method is about 93%. This study highlights the essential role that UAV observations and deep learning could play in the planning and management of forest areas which are often under the threat of deforestation and forest encroachment.

The contribution of snow and ice melt towards the water discharges of most of the Himalayan rivers is highly significant. It is, therefore, necessary to monitor the snow accumulation and depletion, and study the melting processes to help in efficient management of water resources. It is also important to compile a glacier inventory for the purpose. The snow bound areas in the Himalaya lie at high altitudes where the terrain is rugged and inaccessible. This renders the conventional methods of study not only difficult but hazardous as well. Remote sensing techniques, therefore, have a vital role to play in these studies for quick results with much less cost. Visual interpretation of Landsat imagery in Beas river basin and use of aerial photographs for glacier inventory in Baspa river basin have been cited as case studies. To perfect the methodology used in various remote sensing techniques, a pilot project approach has been suggested.

The present investigation was carried out to determine carbon sequestration potential of Solan Forest Division of Himachal Pradesh during 2006–2007. There are six land uses viz., Chir pine, Ban oak, Deodar, Other broadleaves, Culturable and Un-culturable, which are distributed in 538 compartments along altitudinal gradient from 900 to 2,100m. The study reveals that among various land uses, the Other broadleaved species will result in maximum expected carbon (19.88 Mt) which will be 28.81, 23.95, and 3.07 times higher than standing carbon in Ban oak, Deodar and Chir pine, respectively. The Solan Forest Division on the whole, has potential to sequester 17 times more carbon over standing carbon of 1.67 Mt, if forest species are extended to their corresponding altitudinal limits in the “land area available for planting” i.e., Uncultrable land area in the forest division however, to have an accurate estimate of the carbon sequestration potential of the area, other attributes that decides the establishment of plantation of different species such as slope, aspect, soil, climate, etc. need to be taken into consideration beside altitude.

Excessive runoff and high soil erosion rate are the critical problems in the Himalayan terrain, mainly due to rugged topography and high intensity rains. Accurate quantification of runoff and erosion is thus of paramount importance for taking appropriate measures to sustain the soil productivity in the Himalayan watersheds. Distributed, process-based hydrological and erosion models are ideal for this purpose. However, model parameterization in the rugged, inaccessible and thus generally a data scarce Himalayan watershed is a major challenge. The present study primarily investigates the applicability of kinematic runoff and erosion model (KINEROS2) model in a Himalayan watershed besides exploring the potential of satellite remote sensing and GIS in spatially distributed runoff modeling. The KINEROS2 model, is an event-based, distributed, water and erosion process model. It discretizes the watershed into a mosaic of planes and channels based on topography. The runoff is estimated for each plane which eventually flows to adjacent channel and is then routed to estimate the total runoff at the watershed outlet. Remote sensing is primarily used for model parameterization, i.e., characterizing the individual planes and channels. Optimized digital elevation model and fine-scale land-use/land-cover information are generated using high-resolution panchromatic and multi-spectral optical and microwave satellite imagery. The resulting data on near-surface soil moisture from radar imagery (ENVISAT ASAR) calibrated the initial soil moisture in the model, whose performance is evaluated using root mean square error and Nash–Sutcliffe that reveals that KINEROS2 model works quite well in a small Himalayan watershed. The sensitivity analysis indicates that saturated soil hydraulic conductivity is the most sensitive parameter influencing the runoff compared to Manning’s coefficient and initial soil moisture. The model output is also used for validating the remote sensing and geographical information system (GIS) based hydrologic response units delineated in a previous research study. The study highlights that the coupling of remote sensing and GIS with process models, such as KINEROS2, can provide valuable information in planning sustainable watershed management practices in the Himalayan watersheds.

Một dạng hàm chức năng của hồ sơ quang phổ cây trồng được đề xuất bởi Badhwar đã được áp dụng cho giá trị Chỉ số thực vật khác biệt chuẩn hóa (NDVI) lúa mì theo từng quận, được chuẩn hoá tương đối bởi giá trị NDVI của các đặc trưng giả bất biến (đô thị và khu xây dựng), được lấy từ Cảm biến Khung rộng (WiFS) trên vệ tinh Địa lý Từ xa Ấn Độ (IRS) trong 17 ngày trong mùa vụ rabi 1999–2000. Độ phù hợp tổng thể của hồ sơ và ba tham số cơ bản, tức là, ngày xuất hiện cây trồng (To), và các tham số đặc trưng của cây trồng (a và P) được tìm thấy có ý nghĩa thống kê. Trong khi a tương ứng với tỷ lệ tăng trưởng hồ sơ, thì β tương ứng với tỷ lệ suy giảm hồ sơ. Một so sánh với các nghiên cứu trước đó ở Punjab sử dụng NOAA-AVHRR chỉ ra sự cải thiện trong mối quan hệ giữa NDVI đỉnh và năng suất lúa mì. Thời gian ước lượng xuất hiện quang phổ và NDVI đỉnh được derivate từ hồ sơ theo dõi hành vi được quan sát của thời gian trước khi ra hoa của cây trồng rút ngắn với việc trì hoãn việc gieo trồng.

The study aims to investigate the efficiency of Cellular Automata (CA) based models for simulation of urban growth in two Indian cities (Dehradun and Saharanpur) having different growth patterns. The transition rules in the CA model were defined using Multi-Criteria Evaluation technique. The model was calibrated by varying two parameters namely the neighbourhood (type and size) and model iterations. The model results were assessed using two measures, i.e., percent correct match and Moran’s Index. It was found that for Dehradun, which had a dispersed growth pattern, Von Neumann neighbourhood of small size produced the highest accuracy, in terms of pattern and location of simulated urban growth. For Saharanpur, which had a compact growth pattern, large neighbourhoods, produced the most optimum results, irrespective of the type of neighbourhood. For both study areas, large number of model iterations failed to increase the accuracy of urban growth assessment.