Application Of Remote Sensing Technology Research Articles

A Study of Change in Agricultural and Forest Land in Gwalior City Using Satellite Imagery

The relative evaluation of land use and land cover for various uses such as forest, agriculture and water bodies etc. is the important issue in the semiarid region. Application of Remote Sensing technology for Land Use and Land Cover (LULC) change analysis has been carried out in semi-arid region of Madhya Pradesh, central part of India and found that the use of remote sensing along with Survey of India toposheets could be used appropriately for LULC mapping. The semi-arid regions are characterized by erratic rainfall and high rate of vegetation dynamics. The increasing biotic pressure together with increasing human demands exerts pressure on the available land resources all over the region. Therefore, in order to have best possible use of land, it is not only necessary to have the information on the existing LULC, but also to monitor the dynamic land use resulting because of increasing demands aroused from the growing population. Continuous overexploitation of natural resources like land, water, and forest has caused serious threat to the local population of the semi-arid region. This causes problems like little scope for soil moisture storage, high rate of soil erosion, declining groundwater level and shortage of drinking water

Drought Detection by Application of Remote Sensing Technology and Vegetation Phenology

Drought Detection by Application of Remote Sensing Technology and Vegetation Phenology

Application of Remote Sensing Technologies for Assessing Planted Forests Damaged by Insect Pests and Fungal Pathogens: a Review

In this review, we highlight recent developments and applications in remote sensing that can improve the accuracy and timeliness of health assessments in plantations managed for timber and pulp production. The detection and mapping of damage extent and severity caused by insect pests and fungal pathogens is a common requirement of foresters managing plantations. The objectives of these surveys can range from early detection for targeted intervention to more strategic aims of predicting stand susceptibility or evaluating the performance of management strategies. Recent developments in remote sensing technologies and big data modelling techniques can now provide spatially explicit, quantitative solutions for these management objectives that are more accurate than manual field-based assessments of tree damage or airborne visual mapping. Past studies have identified a large number of spectral, textural and structural metrics that have been used in models to classify specific tree crown damage symptoms. This process requires a detailed understanding of the chronology of crown symptoms for specific damaging agents and the spectral responses to these symptoms. Continuing increases in the spatial and spectral resolution of remote sensors enables crown-level damage classification. The development of data processing workflows that fuse spectral information with three-dimensional (3D) data acquired simultaneously from single or different remote platforms promote the opportunities to derive both structural and physiological crown-level attributes that relate to crown damage. The simultaneous acquisition of spectral and 3D point data will enable plantation foresters to derive several spatial products, including the assessment of tree health in a cost-effective manner.

Spectral characteristics and species identification of rhododendrons using a discriminative restricted Boltzmann machine

ABSTRACTRhododendrons are an important genus of alpine flowering plant used ornamentally worldwide. The purpose of this study is to improve the application of remote-sensing technology for investigating and monitoring mountain rhododendron germplasm. Research area is the Baili Rhododendron National Forest Park located in the karst region of Guizhou Province, China. Field spectrometry was used to acquire spectral data for 20 samples extracted from eight rhododendron species. A deep-learning algorithm from a discriminative restricted Boltzmann machine was used with the original spectral data from the different rhododendron species to obtain the optimal parameters for the model. Simultaneously, the data processing methodology from the discriminative restricted Boltzmann machine was used to recognize the original spectra, the noise smoothed spectra, and the first- and second-order spectral derivatives with accuracies of 88.54%, 88.54%, 93.75%, and 90.62%, respectively. The results show that the discriminative restricted Boltzmann machine is effective in recognizing spectral information for different rhododendron species. Changes in the first-order derivative gave the most accurate classification, but changes in the second-order derivative significantly reduced the sample training time. Changes in both derivatives therefore proved useful in recognizing and extracting particular features of the plant species. This research may therefore further support the use of hyperspectral remote-sensing imagery for investigating and monitoring germplasm, species classification, and physiological parameter inversions for rhododendrons from various mountain regions of China.

Application of Remote Sensing Technology for Calibration of Bay of Bengal Morphology Model

Prior to implication of mathematical model as a tool for any kind of policy making or decision making, the model must be calibrated against appropriate observed data. The two-dimensional sediment transport model of the Bay of Bengal usually being calibrated against suspended sediment concentration at different locations comparing the model results with field measurements to make the model performance to a satisfactory level. Since field data of sediment concentration is rarely available for model calibration, an attempt has been made in the present study, to generate sediment concentration data by using satellite images to calibrate the sediment transport model in a better way. Calibration of sediment transport model result against image generated sediment concentration at different locations of Bay of Bengal show acceptable agreement.

An Exploratory Application of Remote Sensing Technologies and Statistical Analysis to Provide Rapid and Cost Effective Inundation Predictions for the Tonle Sap Lake Floodplain System

An Exploratory Application of Remote Sensing Technologies and Statistical Analysis to Provide Rapid and Cost Effective Inundation Predictions for the Tonle Sap Lake Floodplain System

Application of Remote Sensing Technology to the Ecology Quality Evaluation of Opencast Mine Area

Application of Remote Sensing Technology to the Ecology Quality Evaluation of Opencast Mine Area

Restoration of shadows in multispectral imagery using surface reflectance relationships with nearby similar areas

The presence of shadows in optical satellite images limits the application of remote-sensing technology. It is important to restore shadow radiance information for improving information extraction from remote-sensing images. Several shadow-restoration methods have been developed using complex statistical relationships between shadowed areas and their nearby sunlit areas. In this study, a simple shadow-restoration approach was proposed based on the surface reflectance equality relationship (RER) under the assumption that the surface reflectance of a feature in the shadowed area is equal to that of the same feature in the nearby sunlit area. This approach reduces the number of parameters, thus reducing the error propagated by the uncertainties of extra parameters. The new RER method was tested with three multispectral images with different shadow features. By comparing RER with the widely used mean and variance transformation, the RER was shown to be capable of restoring the image colours, texture, tone, and brightness of the shadowed areas to a visually satisfactory level. Quantitative analysis suggests that RER can help to restore the reflectance of shadow features accurately and has robust performance for a variety of land-surface types. Moreover, RER can be effectively used to restore the spectral shape information of shadow features, which is particularly important when applying RER to the restoration of multispectral imagery for the purpose of image classification.

Application of Remote Sensing Technologies to detect the vegetation changes during past two decades in Islamabad, Pakistan

Islamabad was built in 1960 as a capital of Pakistan. It is a city famous for its beauty, greenery, and beautiful trees. The total area of Islamabad is 906 km2. With increasing population, a lot of development projects have been initiated within the city, which had a huge impact on the vegetation. Awareness of urban vegetation, environmental quality and knowledge is really important for conservation of natural resource, management and improvement of ecosystem of urban resources. The impact of these development activities on vegetation was assessed for the last twenty years within Islamabad using remote sensing techniques. For this purpose, satellite images of Landsat were acquired for 1992, 2000, and 2013. Supervised classification and NDVI analysis were done to calculate the area under vegetation and other classes of all the images. Analysis of these satellite images revealed that vegetation was very dense in the past, but with the passage of time the vegetation loss became significantly prominent. Sector wise analysis was done to find out the most vulnerable area with respect to vegetation loss. The included H-10, G-13, G-5, and D-13. Although, some initiatives are being introduced to control vegetation area, but they do not match the pace of vegetation loss. Necessary measures need to be taken to maintain and improve vegetation to the desired level.

Application of Remote Sensing Technologies to Map the Structural Geology of Central Region of Kenya

Advancements of digital image processes (DIP) and availability of multispectral and hyperspectral remote sensing data have greatly benefited mineral investigation, structure geology mapping, fault pattern, and landslide studies: site-specific landslide assessment and landslide quantification. The main objective of this research was to map the geology of the central region of Kenya using remote-sensing techniques to aid rainfall-induced landslide quantification. The study area is prone to landslides geological hazards and, therefore, it was necessary to investigate geological characteristics in terms of structural pattern, faults, and river channels in a highly rugged mountainous terrain. The methodology included application of PCA, band rationing, intensity hue saturation (IHS) transformation, ICA, false color composites (FCC), filtering applications, and thresholding, and performing knowledge-based classification on Landsat ETM + imagery. PCA factor loading facilitated the choice of bands with the most geological information for band rationing and FCC combination. Band ratios (3/2, 5/1, 5/4, and 7/3) had enhanced contrast on geological features and were the input variables in a knowledge-based geological classification. This was compared to a knowledge-based classification using PCs 2, 5, and IC1, where the band ratio classification performed better at representing geology and matched FCC [IC1, PC5, saturation band of IHS (5,7,3)]. Fault and lineament extraction was achieved by filtering and thresholding of pan-band8 and ratio 5/1 and overlaid on the geology map. However, the best visualization of lineaments and geology was in the FCC [IC1, PC5, saturation band of IHS (5,7,3)], where volcanic extrusions, igneous, sedimentary rocks (eolian and organic), and fluvial deposits were well discriminated.

Application of Remote Sensing Technology in Mine Environment Monitoring

Mine environment problem caused by the exploitation of mineral resources has become a key factor which affects normal production of mine and safety of ecological environment for human settlement. For better protection and management of mine environment, this article has introduced the important role of remote sensing technology in pollution monitoring of mine environment, geological disaster monitoring and monitoring of mining activities.

Application of Remote Sensing Technology in Investigating Tourism Resources

As a significant technological means of collecting global statistics and its changing information, remote sensing technology plays a critically important role in the investigation of territorial resources. Therefore, using remote sensing technology to interpret the tourism resources of Danxia landform can make up for many shortages of traditional methods so as to do less for more. In this article, the tourism resources of Danxia landform in Jiangxi province are extracted by remote sensing technology and its average area error is reduced to 11% through the statistics data. It can be concluded that it is feasible to investigate the tourism resources of Danxia landform by means of computer automatic identification and classification.

Remote Sensing and Unmanned Aerial System Technology for Monitoring and Quantifying Forest Fire Impacts

Fire is a regular occurrence throughout the world’s forested landscapes and affects millions of hectares annually. A variety of remote sensing applications have been developed to quantify wildfire impacts in forests with varying success. Remote sensing technology applications for wildfires have typically involved quantifying burn severity, fuel levels, and forest resource recovery following burn. Wildfire remote sensing applications have recently included active or realtime technology applications for mapping burn impacts and wildfire detection and monitoring. A review of traditional remote sensing applications are presented in this paper to quantify fire impacts, tracking vegetation recovery, establishing fuel conditions, and fire detection and monitoring in forested landscapes; then recent examples of active and real-time remote sensing techniques were examined to monitor fire ignition and behavior, followed by an assessment of potential future applications. The application of unmanned aerial systems (UAS’s) with both spectral and thermal sensors may hold great promise for future remote sensing applications related to forest fires.

Research Advance in Remote Sensing to Land Desertification Monitoring

This Land desertification has been a worldwide ecological and environmental problem. It is the significance for effective remedy of land desertification to monitor the desertification ,know well the present situation ,intensity as well as dynamic variation rules of the desertification. In recent years, remote sensing has become an important technology to monitor land desertification.Firstly, we summarize the research progress in monitoring land desertification using remote sensing data acquisition.Then, we discuss about themethods to extract information of land desertification from remote sensing image, which includes artificial visual interpretation, supervised classification, unsupervised classification, hierarchical decision tree classification, neural network classification and spectral mixture analysis£¬and also we comprehensively compare the strength and weaknesses of each method. Finally, We point out the problems in the remote sensing technology application to land desertification monitoring and put forward the development prospects in the application of remote sensing to monitoring land desertification.

Application of Remote Sensing Technology on Tobacco Planting in Fuquan City

Tobacco planting in Guizhou existed many problems such as exceed planting area, irregularity in quality, imprecise in crop estimate and kinds of disaster monitoring etc. In this article, to solve these problems some new schemes were given. The satellite remote sensing technology was applied on tobacco planting monitoring in Fuquan city of Guizhou province for the first time combining with ERDAS, GPS and ARCGIS in this paper. It put forward a specific implementation plan. And this plan could provide technical helps very well in planting area statistics, growth monitoring and yield estimation of tobacco in Fuquan city.

Application of Remote Sensing in the Research of Soil Erosion

Since remote sensing technology introduced into China, has made considerable progress in the dynamic monitoring of soil and water loss research. Through the application of remote sensing technology in soil erosion monitoring in our country and other countries, analyze the existing problems in the remote sensing technology application, then make prospects for the application and development.

Application of Remote Sensing Technology in the Analysis of Thermal Environment in Dongying City

Urban thermal environment, as an important reflection of urban ecological environment, is affected strongly by urban land change, especially the loss of green land. By using remote sensing and GIS, two terms of remote images of Dongying city are studied to disclose vegetation coverage and land surface temperature that can reflect urban thermal environment.

An Application of Remote Sensing Technology to Select Environmentally Suitable Areas for Shrimps Farming in Jazan Region, Kingdom of Saudi Arabia

An Application of Remote Sensing Technology to Select Environmentally Suitable Areas for Shrimps Farming in Jazan Region, Kingdom of Saudi Arabia

Towards Application of Remote Sensing Technology in Geothermal Prospecting in Xilingol in Eastern Inner Mongolia, NE China

Geothermal energy is a renewable energy source most abundantly associated with volcanism. In this paper, we report the progress in the application of remote sensing technology for prospecting the geothermal energy potential in Xilingol, a Cenozoic volcanic area in the eastern Inner Mongolia, NE China. Landsat TM images are processed in combination of existing geological and geographical data in the interpretation of faults and volcanic craters. Land surface temperature (LST) has been retrieved with a single-channel algorithm. Through the analysis of geological data and the distribution of the LST, seven prospective geothermal areas have been identified for further study.

A Review on the Principles and Basic Methods of Remote Sensing Spectral Characteristic Parameters

The principles and methods of remote sensing spectral characteristic parameters including derivative spectrum,red edge parameter,spectrum absorption characteristics and spectrum reflectance characteristics,national and international development of hyperspectral remote sensing and its application in extracting biophysical and biochemical information of vegetation,and the feasibility of vegetation indices application are reviewed.The potentiality of further application of remote sensing technology in extracting vegetation information in order to promote remote sensing was put forward.Remote sensing spectral parameter provides a powerful tool for extracting vegetation biophysical and biochemical information.