scholarly journals Estimation of Snow-Melt Run-Off During Pre-Monsoon Months in Beas Sub-Basin using Satellite Imagery (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 251-251
Author(s):  
K.P. Sharma ◽  
P.K. Garg
Keyword(s):  
Remote Sensing ◽  
Linear Trend ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Snow Melt ◽  
Monsoon Period ◽  
Similar Work ◽  
Run Off ◽  
Exponential Trend

The increasing demand for water, coupled with the construction of multi-purpose reservoirs to control and regulate snow-melt run-off, requires accurate strearm-flow forecast. For making an accurate prediction of spring run-off, information on the amount of snow accumulation in winter is necessary; this may be achieved through remote-sensing techniques in any inaccessible region.This paper outlines the snow-melt run-off study carried out in a part of Beas basin, India, using Landsat imagery for the years 1973, 1975, 1976, and 1977. The Beas basin lies between long. 76°56' to 77°52'E. and lat. 31°30' to 32°25'N., covering an area about 4900 km2, of which 1400 km2 is permanently covered by snow. The gradual melting of snow accumulated over the catchment area during the winter months is responsible for the perennial character of the Beas River.Photohydrological investigation of the part of the Beas basin up-stream of Barji was carried out and a study was made for the estimation of the snow-melt run-off during the pre-monsoon period in the sub-basin up-stream of Manali. For this purpose, the sub-basin has been divided into permanent and temporary snow-covered zones. The degree-day method and the melt due to rainfall on snow have been used to estimate snow-melt run-off. The routing of snow-melt, after accounting for losses as well as the run-off from the excess rainfall from the permanent and temporary snow-covered areas, has also been done taking the recession coefficient K as 0.90, and the excess rain from the non-snow-covered areas has been assumed to contribute directly to the run-off for that day. Run-off coefficients of 0.595 for rainfall on the snow-covered areas and 0.278 for rainfall on the non-snow-covered areas have been determined.Reference can be made to similar work in India and Pakistan to establish the relationship between the snow cover and the cumulative discharges for the months of March, April, and May of the years 1973, 1975, 1976, and 1977, and an exponential trend was observed with the help of Landsat Imagery. Furthermore, the snow-covered areas as determined from bands 5 and 7 of the Landsat imagery, for the same day, showed a linear trend.The analysis of the results shows that remote-sensing data used in conjunction with conventional methods are likely to improve the accuracy of the snow-melt forecasts in remote areas like the Himalayan catchments.

scholarly journals Estimation of Snow-Melt Run-Off During Pre-Monsoon Months in Beas Sub-Basin using Satellite Imagery (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 251
Author(s):  
K.P. Sharma ◽  
P.K. Garg
Keyword(s):  
Remote Sensing ◽  
Linear Trend ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Snow Melt ◽  
Monsoon Period ◽  
Similar Work ◽  
Run Off ◽  
Exponential Trend

The increasing demand for water, coupled with the construction of multi-purpose reservoirs to control and regulate snow-melt run-off, requires accurate strearm-flow forecast. For making an accurate prediction of spring run-off, information on the amount of snow accumulation in winter is necessary; this may be achieved through remote-sensing techniques in any inaccessible region. This paper outlines the snow-melt run-off study carried out in a part of Beas basin, India, using Landsat imagery for the years 1973, 1975, 1976, and 1977. The Beas basin lies between long. 76°56' to 77°52'E. and lat. 31°30' to 32°25'N., covering an area about 4900 km2, of which 1400 km2 is permanently covered by snow. The gradual melting of snow accumulated over the catchment area during the winter months is responsible for the perennial character of the Beas River. Photohydrological investigation of the part of the Beas basin up-stream of Barji was carried out and a study was made for the estimation of the snow-melt run-off during the pre-monsoon period in the sub-basin up-stream of Manali. For this purpose, the sub-basin has been divided into permanent and temporary snow-covered zones. The degree-day method and the melt due to rainfall on snow have been used to estimate snow-melt run-off. The routing of snow-melt, after accounting for losses as well as the run-off from the excess rainfall from the permanent and temporary snow-covered areas, has also been done taking the recession coefficient K as 0.90, and the excess rain from the non-snow-covered areas has been assumed to contribute directly to the run-off for that day. Run-off coefficients of 0.595 for rainfall on the snow-covered areas and 0.278 for rainfall on the non-snow-covered areas have been determined. Reference can be made to similar work in India and Pakistan to establish the relationship between the snow cover and the cumulative discharges for the months of March, April, and May of the years 1973, 1975, 1976, and 1977, and an exponential trend was observed with the help of Landsat Imagery. Furthermore, the snow-covered areas as determined from bands 5 and 7 of the Landsat imagery, for the same day, showed a linear trend. The analysis of the results shows that remote-sensing data used in conjunction with conventional methods are likely to improve the accuracy of the snow-melt forecasts in remote areas like the Himalayan catchments.

scholarly journals Snow dunes and glazed surfaces in Antarctica: new field and remote-sensing data

2002 ◽  
Vol 34 ◽  
pp. 81-88 ◽  
Author(s):  
Massimo Frezzotti ◽  
Stefano Gandolfi ◽  
Floriana La Marca ◽  
Stefano Urbini
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accumulation Rate ◽  
Spectral Response ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Remotely Sensed Data ◽  
Surface Mass ◽  
Glazed Surface ◽  
Transverse Dunes

AbstractAs part of the International Trans-Antarctic Scientific Expedition project, the Italian Antarctic Programme undertook two traverses from the Terra Nova station to Talos Dome and to Dome C. Along the traverses, the party carried out several tasks (drilling, glaciological and geophysical exploration). The difference in spectral response between glazed surfaces and snow makes it simple to identify these areas on visible/near-infrared satellite images. Integration of field observation and remotely sensed data allows the description of different mega-morphologic features: wide glazed surfaces, sastrugi glazed surface fields, transverse dunes and megadunes. Topography global positioning system, ground penetrating radar and detailed snow-surface surveys have been carried out, providing new information about the formation and evolution of mega-morphologic features. The extensive presence, (up to 30%) of glazed surface caused by a long hiatus in accumulation, with an accumulation rate of nil or slightly negative, has a significant impact on the surface mass balance of a wide area of the interior part of East Antarctica. The aeolian processes creating these features have important implications for the selection of optimum sites for ice coring, because orographic variations of even a few metres per kilometre have a significant impact on the snow-accumulation process. Remote-sensing surveys of aeolian macro-morphology provide a proven, high-quality method for detailed mapping of the interior of the ice sheet’s prevalent wind direction and could provide a relative indication of wind intensity.

scholarly journals Forest areas remote monitoring within the limits of transport objects impact

2021 ◽  
pp. 109-118
Author(s):  
Dmytro Liashenko ◽  
◽  
Dmytro Pavliuk ◽  
Vadym Belenok ◽  
Vitalii Babii ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Remote Monitoring ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Transport Infrastructure ◽  
Nature Management ◽  
Collecting Methods ◽  
Remote Sensing Methods

The article studies the issues of using remote sensing data for the tasks of ensuring sustainable nature management in the territories within the influence of transport infrastructure objects. Peculiarities of remote monitoring for tasks of transport networks design and in the process of their operation are determined. The paper analyzes the development of modern remote sensing methods (satellite imagery, the use of mobile sensors installed on cars or aircraft). A brief overview of spatial data collecting methods for the tasks of managing the development of territories within the influence of transport infrastructure (roads, railways, etc.) has made. The article considers the experience of using remote sensing technologies to monitor changes in the parameters of forest cover in the Transcarpathian region (Ukraine) in areas near to highways, by use Landsat imagery.

scholarly journals Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 940
Author(s):  
Rocío Ballesteros ◽  
Miguel A. Moreno ◽  
Fellype Barroso ◽  
Laura González-Gómez ◽  
José F. Ortega
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Canopy Cover ◽  
Remote Sensing Data ◽  
Multilinear Regression ◽  
Area Index ◽  
Canopy Development

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

Snow accumulation in forests from ground and remote-sensing data

2004 ◽  
Vol 18 (10) ◽  
pp. 1941-1955 ◽  
Author(s):  
Angela Lundberg ◽  
Yuichiro Nakai ◽  
Hans Thunehed ◽  
Sven Halldin
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Sensing Data

scholarly journals AN OBJECT-BASED APPROACH FOR MONITORING THE EVOLUTION OF LANDSLIDE-DAMMED LAKES AND DETECTING TRIGGERING LANDSLIDES IN TAIWAN

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 103-108 ◽  
Author(s):  
Z. Dabiri ◽  
D. Hölbling ◽  
L. Abad ◽  
G. Prasicek ◽  
A.-L. Argentin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landsat Imagery ◽  
Contextual Information ◽  
Regional Scale ◽  
Landslide Dam ◽  
Remote Sensing Data ◽  
Time Range ◽  
Channel System ◽  
Object Based ◽  
Elevation Model

<p><strong>Abstract.</strong> In August 2009, Typhoon Morakot caused a record-breaking rainfall in Taiwan. The heavy rainfall triggered thousands of landslides, in particular in the central-southern part of the island. Large landslides can block rivers and can lead to the formation of landslide-dammed lakes. Cascading hazards like floods and debris flows after landslide dam breaches pose a high risk for people and infrastructure downstream. Thus, better knowledge about landslides that significantly impact the channel system and about the resulting landslide-dammed lakes are key elements for assessing the direct and indirect hazards caused by the moving mass. The main objectives of this study are 1) to develop an object-based image analysis (OBIA) approach for semi-automated detection of landslides that caused the formation of landslide-dammed lakes and 2) to monitor the evolution of landslide-dammed lakes based on Landsat imagery. For landslide and lake mapping, primarily spectral indices and contextual information were used. By integrating morphological and hydrological parameters derived from a digital elevation model (DEM) into the OBIA framework, we automatically identified landslide-dammed lakes, and the landslides that likely caused the formation of those lakes, due to the input of large amounts of debris into the channel system. The proposed approach can be adapted to other remote sensing platforms and can be used to monitor the evolution of landslide-dammed lakes and triggering landslides at regional scale after typhoon and heavy rainstorm events within an efficient time range after suitable remote sensing data has been provided.</p>

scholarly journals The effects of dating uncertainties on net accumulation estimates from firn cores

2015 ◽  
Vol 61 (225) ◽  
pp. 163-172 ◽  
Author(s):  
Summer Rupper ◽  
William F. Christensen ◽  
Barry R. Bickmore ◽  
Landon Burgener ◽  
Lora S. Koenig ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ice Sheet ◽  
Remote Sensing Data ◽  
Statistical Simulation ◽  
Snow Accumulation ◽  
Model Output ◽  
West Antarctica ◽  
Surface Mass ◽  
Core Data ◽  
Firn Core

AbstractThe mean, trend and variability of net snow accumulation in firn cores are often used to validate model output, develop remote-sensing algorithms and quantify ice-sheet surface mass balance. Thus, accurately defining uncertainties associated with these in situ measurements is critical. In this study, we apply statistical simulation methods to quantify the uncertainty in firn-core accumulation data due to the uncertainty in depth–age scales. The methods are applied to a suite of firn cores from central West Antarctica. The results show that uncertainty in depth–age scales can give rise to spurious trends in accumulation that are the same order of magnitude as accumulation trends reported in West Antarctica. The depth–age scale uncertainties also significantly increase the apparent interannual accumulation variability, so these uncertainties must first be accounted for before using firn-core data to assess such processes as small-spatial-scale variability. Better quantification of error in accumulation will improve our ability to meaningfully compare firn-core data across different regions of the ice sheet, and provide appropriate targets for calibration and/or validation of model output and remote-sensing data.

scholarly journals Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data

2021 ◽  
Vol 13 (20) ◽  
pp. 4057
Author(s):  
Liya Zhao ◽  
Qi Yang ◽  
Qiang Zhao ◽  
Jingwei Wu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Electrical Conductance ◽  
Remote Sensing Data ◽  
Groundwater Table ◽  
Irrigation District ◽  
Cropping Patterns ◽  
Sensing Data

Salinization in arid or semiarid regions with water logging limits cropland yield, threatening food security. The highest level of farmland salinization, that is, abandoned salinized farmland, is a tradeoff between inadequate drainage facilities and sustainable farming. The evolution of abandoned salinized farmlands is closely related to the development of cropping systems. However, detecting abandoned salinized farmland using time-series remote sensing data has not been investigated well by previous studies. In this study, a novel approach was proposed to detect the dynamics of abandoned salinized farmland using time-series multispectral and thermal imagery. Thirty-two years of temporal Landsat imagery (from 1988 to 2019) was used to assess the evolution of salinization in Hetao, a two-thousand-year-old irrigation district in northern China. As intermediate variables of the proposed method, the crop-specific planting area was retrieved via its unique temporal vegetation index (VI) pattern, in which the shape-model-fitting technology and the K-means cluster algorithm were used. The desert area was stripped from the clustered non-vegetative area using its distinct features in the thermal band. Subsequently, the abandoned salinized farmland was distinguished from the urban area by the threshold-based saline index (SI). In addition, a regression model between electrical conductance (EC) and SI was established, and the spatial saline degree was evaluated by the SI map in uncropped and unfrozen seasons. The results show that the cropland has constantly been expanding in recent decades (from 4.7 × 105 ha to 7.1 × 105 ha), while the planting area of maize and sunflower has grown and the area of wheat has decreased. Significant desalinization progress was observed in Hetao, where both the area of salt-affected land (salt-free area increased approximately 4 × 105 ha) and the abandoned salinized farmland decreased (reduced from 0.45 × 105 ha to 0.19 × 105 ha). This could be mainly attributed to three reasons: the popularization of water-saving irrigation technology, the construction of artificial drainage facilities, and a shift in cropping patterns. The decrease in irrigation and the increase in drainage have deepened the groundwater table in Hetao, which weakens the salt collection capacity of the abandoned salinized farmland. The results demonstrate the promising possibility of reutilizing abandoned salinized farmland via a leaching campaign where the groundwater table is sufficiently deep to stop salinization.

scholarly journals Remote Sensing of Vegetation Recovery in Grasslands After th 1988 Fires

1992 ◽  
Vol 16 ◽  
pp. 188-190
Author(s):  
Evelyn Merrill ◽  
Ron Marrs
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Landsat Imagery ◽  
Snow Accumulation ◽  
Vegetation Recovery ◽  
Electromagnetic Spectrum ◽  
Traditional Methods ◽  
Burned Areas ◽  
Soil Features ◽  
Vegetative Biomass

Traditional methods for measurement of vegetative biomass can be time-consuming and labor­intensive, especially across large areas. Yet such estimates are necessary to investigate the effects of large scale disturbances on ecosystem components and processes. One alternative to traditional methods for monitoring rangeland vegetation is to use satellite imagery. Because foliage of plants differentially absorbs and reflects energy within the electromagnetic spectrum, remote sensing of spectral data can be used to quantify the amount of vegetative biomass present in an area (Tucker and Sellers 1986). In 1987 we found that Landsat Multispectral Scanner (MSS) imagery could be used to quantify green herbaceous phytomass (GHP) on ungulate summer range in the northeastern portion of Yellowstone National Park. Estimates of GHP in the study area were well within values reported for the habitat types sampled (Mueggler and Steward 1980). Annual variation in GHP was related to winter snow accumulation probably due to the timing of snow melt (Merrill et al. 1988). Additionally, we found that GHP explained a significant amount of the variation in the per capita growth rate of elk population from 1972 to 1987 (Merrill and Boyce 1991). The extensive fires that occurred in the Park in the summer of 1988 provided an opportunity to determine whether remote sensing could be used to monitor grassland vegetation recovery in the Park and to explore the effects of the 1988 fires on ungulate populations using models we developed in 1987. Previous studies have used Landsat imagery to monitor succession of seral stages after fire in pine (Jakubauskas et al. 1990), but no studies to our knowledge have used this approach to quantify herbaceous recovery in grasslands. The objectives during this study period were: (1) to develop and validate a model for predicting GHP in sagebrush-grassland communities using 1989-91 Landsat TM spectral information and field data on GHP; and (2) to describe broad-scale vegetation recovery in burned areas and physiographic and soil features which influence the recovery.

scholarly journals Utilization of Remote Sensing Technology for Carbon Offset Identification in Malaysian Forests

2021 ◽  
Author(s):  
Hamdan Omar ◽  
Thirupathi Rao Narayanamoorthy ◽  
Norsheilla Mohd Johan Chuah ◽  
Nur Atikah Abu Bakar ◽  
Muhamad Afizzul Misman
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Tropical Forests ◽  
Optical Sensors ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Tropical Country ◽  
Sensing Technology ◽  
Carbon Offset ◽  
Carbon Dioxide Co2

Rapid growth of Malaysia’s economy recently is often associated with various environmental disturbances, which have been contributing to depletion of forest resources and thus climate change. The need for more spaces for numerous land developments has made the existing forests suffer from deforestation. This chapter presents an overview and demonstrates how remote sensing data is used to map and quantify changes of tropical forests in Malaysia. The analysis dealt with image processing that produce seamless mosaics of optical satellite data over Malaysia, within 15 years period, with 5-year intervals. The challenges were about the production of cloud-free images over a tropical country that always covered by clouds. These datasets were used to identify eligible areas for carbon offset in land use, land use change and forestry (LULUCF) sector in Malaysia. Altogether 580 scenes of Landsat imagery were processed to complete the observation period and came out with a seamless, wall to wall images over Malaysia from year 2005 to 2020. Forests have been identified from the image classification and then classified into three major types, which are dry-inland forest, peat swamp and mangroves. Post-classification change detection technique was used to determine areas that have been undergoing conversions from forests to other land uses. Forest areas were found to have declined from about 19.3 Mil. ha (in 2005) to 18.2 Mil. ha in year 2020. Causes of deforestation have been identified and the amount of carbon dioxide (CO2) that has been emitted due to the deforestation activity has been determined in this study. The total deforested area between years 2005 and 2020 was at 1,087,030 ha with rate of deforestation of about 72,469 ha yr.−1 (or 0.37% yr.−1). This has contributed to the total CO2 emission of 689.26 Mil. Mg CO2, with an annual rate of 45.95 Mil. Mg CO2 yr.−1. The study found that the use of a series satellite images from optical sensors are the most appropriate sensors to be used for monitoring of deforestation over the Malaysia region, although cloud covers are the major issue for optical imagery datasets.

Sign in / Sign up

Export Citation Format

Share Document