INDUSTRIAL IMPACTS ON VEGETATION IN THE MONCHEGORSK REGION FROM MULTISPECTRAL SPACE IMAGERY

Results are presented of analysis of Landsat MSS imagery for the purpose of assessing damage to northern taiga and tundra vegetation caused by emissions generated by nonferrous metallurgy on northwest Russia's Kola Peninsula. Unlike earlier studies, the present project attempts to provide spatially comprehensive coverage of vegetation impacts, according to a standardized methodology for their assessment. A reduction in the number of feature classes identifiable upon a change from visual interpretation to automated classification based on spectral brightness values made it necessary to test alternative classification procedures (based on brightness ratios and the normalized vegetation index).     

TEXTURE OF FOREST COMMUNITIES ON AERIAL PHOTOGRAPHS

The authors discuss a method by which the image characteristics of forest vegetation can be used to determine various valuational characteristics of forest stands through the combination of air photo interpretation and ground surveys at selected training sites. Construction of curves showing changes in image texture and tone occurring at different stages in the growth cycle of a pine forest community are used to estimate the age of a forest stand, and through known relationships between age and other stand characteristics incorporated into yield tables, to approximate such valuational characteristics as mean diameter, mean height, and stand volume. Translated from: Distantsionnyye issledovaniya rel'yefa Sibiri, A. L. Yanshin and V. N. Sharanov, eds. Novosibirsk, Nauka, 1985, pp. 73-78.     

Space based disaster management of 2008 Kosi floods,North Bihar,India

One of the most important elements in flood disaster management is the availability of timely information for taking decisions and actions by the authorities. During the August 18, 2008 Kosi floods which impacted India and Nepal and affected more than three million people, aero-space technology proved to be a critical input for providing vital information on flood inundation. The satellite based flood inundation maps were extensively used for identifying marooned villages, submerged roads and railway tracks and carrying out the relief and rescue operations by the state agencies. Decision Support Centre (DSC) at National Remote Sensing Centre (NRSC) kept a constant watch on the flood situation. More than 200 flood inundation maps, using about 30 satellite datasets were generated and provided in near real time mode to the state agencies during August to October, 2008. DSC efforts were primarily focused in providing an overall picture of the flood situation in a short span of time to the state agencies. The present paper discusses about the operational use of remote sensing technology for near real time flood mapping, monitoring of Kosi floods and the satellite based observations made for the Kosi river breach.     

APPLICATIONS OF TRANSFORMED IMAGES IN GEOGRAPHIC ANALYSIS

A final installment in a series of papers on transformed cartographic images in geogaphy (see Mapping Sciences and Remote Sensing, 1993, No. 4, pp. 306-319 and 1994, No. 1, pp. 33-85) focuses on their applications in more effectively depicting a variety of spatially distributed phenomena. Most applications are demonstrated using images compiled via a method developed by the authors. Specific transformed images used to illustrate the range of existing applications include those scaled according to population size, GNP, various illness rates, density of ocean vessels, oil pollution in the ocean, and fisheries and offshore oil and gas deposits.     

Generation of Vegetation Fraction and Surface Albedo Products Over India from Ocean Colour Monitor (OCM) Data Onboard Oceansat-2

The use of Local Area Coverage (LAC) data from Ocean Color Monitor (OCM) sensor of Oceansat-2 with its high radiometric resolution (12 bits/pixel) and 2-day repeat cycle for rapid monitoring of vegetation growth and estimating surface albedo for the Indian region is demonstrated in this study. For the vegetation monitoring, normalized difference vegetation index (NDVI) and vegetation fraction (VF) products were estimated by maximum value composite approach fortnightly and were resampled to 1 km. The surface albedo products were realized by converting narrow-band eight-band spectral reflectance OCM data to a) visible (300–700 nm) and b) broad band (300–3,000 nm) data. For validation, the derived products were compared with respective MODIS global products and found to be in good agreement.     

Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ⁰). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R²) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area.     

Comparison of Vegetation Indices from Two Ground Based Sensors

The best and commonly used ground-based sensor to monitor crop growth, ASD FieldSpecPro Spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA) is a passive sensor, which can be used under adequate light condition. However, now-a-days active sensors such as GreenSeeker? (GS) handheld crop response (Trimble Agriculture division, USA) are used for monitoring crop growth and are flexible in terms of timeliness and illumination conditions besides being cheaper than the ASD. Before its wide use, the suitability and accuracy of GS should be assessed by comparing the NDVI measured by this instrument with that by ASD, under diverse wheat growing conditions of India. Keeping this in view, the present experiment was undertaken with the following objectives: (1) to find out the temporal variation of NDVI measured both by ASD and GS treatments, (2) to find out relationship between the NDVI measured through ASD and GS and, (3) to evaluate the suitability of GS for NDVI measurements. It was observed that the numerical value of NDVI as measured by GS was always significantly (P < 0.05) lower than that measured by ASD for all the experiments under study. The NDVI-ASD and NDVI-GS were significantly positively correlated (P < 0.01) with the correlation coefficients being +0.94, +0.88 and +0.87 for irrigation and nitrogen experiment, irrigation and cultivars experiment, and tillage, residue and nitrogen experiments, respectively. Further, the regression equation developed between the NDVI-ASD and NDVI-GS: [NDVI-GS = 1.070 × (NDVI-ASD ? 0.292] can be successfully used to compute the NDVI of ASD from that computed by GS.     

ECOSYSTEM MONITORING AND MAPPING IN A NATURE PRESERVE

Two specialists on the forest and wetland ecosystems of Siberia describe principles and measures employed in the establishment of a regular program of ecological mapping at Russia's Central Siberian Biosphere Preserve, as well as efforts to standardize remote-sensing-based monitoring efforts through improved procedures for selection of reference plots for ground truth determination. Issues addressed include the identification of principal ecological factors serving as landscape classification criteria, the need for better methods of interpreting an entire range of forest-wetland communities on remote sensing imagery, innovations in data gathering procedures in the field, selection of natural models [representative tracts] for monitoring in view of the technical infeasibility of monitoring the entire preserve, and selection of reference plots within each model for ground truth. Translated by Edward Torrey, Alexandria, VA 22308 from: Geografiya i prirodnyye resursy, 1996, No. 2, pp. 36-43.     

Feature Extraction in Remote Sensing High-Dimensional Image Data

High-dimensional image data open new possibilities in remote sensing digital image classification, particularly when dealing with classes that are spectrally very similar. The main problem refers to the estimation of a large number of classifier's parameters. One possible solution to this problem consists in reducing the dimensionality of the original data without a significant loss of information. In this letter, a new approach to reduce data dimensionality is proposed. In the proposed methodology, each pixel's curve of spectral response is initially segmented, and the digital numbers (DNs) at each segment are replaced by a smaller number of statistics. In this letter, the proposed statistics are the mean and variance of the segment's DNs, which are supposed to carry information about the segment's position and shape, respectively. Tests were performed by using Airborne Visible/Infrared Imaging Spectrometer hyperspectral image data. The experiments have shown that this methodology is capable of providing very acceptable results, in addition of being computationally efficient