An Improved GPU-Based Parallel Computing Method for Landscape Index Calculation in Urban Area

2021 ◽  
Vol 87 (2) ◽  
pp. 125-132
Author(s):  
Mengjun Kang ◽  
Yunlong Ma ◽  
Qingyun Du ◽  
Min Weng
Keyword(s):  
Remote Sensing ◽  
Parallel Computing ◽  
Urban Area ◽  
Large Scale ◽  
Urban Expansion ◽  
Landscape Index ◽  
The World ◽  
Considerable Pressure ◽  
Computing Method ◽  
Index Calculation

With the development of urbanization in the world, dealing with the problems caused by urban expansion is becoming more and more important. The data that need to be processed in urbanization studies have increased with the improvement of the spatial and temporal resolution of remote sensing satellites, exerting considerable pressure on traditional software used for landscape index computation. In this article, an improved landscape index-computing algorithm is proposed. Based on CUDA, a pixel-group parallelization strategy is adopted to optimize the algorithm. The results show that the proposed algorithm increases the efficiency by more than a factor of three as the amount of data to be processed increases to 50 million pixels, thus providing a new way to calculate large-scale landscape index values on personal computers to study urbanization.

scholarly journals Understanding Urban Growth in Beijing-Tianjin-Hebei Region over the Past 100 Years Using Old Maps and Landsat Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3264 ◽  
Author(s):  
Shuang Li ◽  
Zhongqiu Sun ◽  
Yafei Wang ◽  
Yuxia Wang
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Spatial Data ◽  
Urban Expansion ◽  
Remote Sensing Images ◽  
The Past ◽  
Old Maps ◽  
The World ◽  
The City

Studying urban expansion from a longer-term perspective is of great significance to obtain an in-depth understanding of the process of urbanization. Remote sensing data are mostly selected to investigate the long-term expansion of cities. In this study, we selected the world-class urban agglomeration of Beijing-Tianjin-Hebei (BTH) as the study area, and then discussed how to make full use of multi-source, multi-category, and multi-temporal spatial data (old maps and remote sensing images) to study long-term urbanization. Through this study, we addressed three questions: (1) How much has the urban area in BTH expanded in the past 100 years? (2) How did the urban area expand in the past century? (3) What factors or important historical events have changed the development of cities with different functions? By comprehensively using urban spatial data, such as old maps and remote sensing images, geo-referencing them, and extracting built-up area information, a long-term series of urban built-up areas in the BTH region can be obtained. Results show the following: (1) There was clear evidence of dramatic urban expansion in this area, and the total built-up area had increased by 55.585 times, from 126.181 km2 to 7013.832 km2. (2) Continuous outward expansion has always been the main trend, while the compactness of the built-up land within the city is constantly decreasing and the complexity of the city boundary is increasing. (3) Cities in BTH were mostly formed through the construction of city walls during the Ming and Qing dynasties, and the expansion process was mostly highly related to important political events, traffic development, and other factors. In summary, the BTH area, similarly to China and most regions of the world, has experienced rapid urbanization and the history of such ancient cities should be further preserved with the combined use of old maps.

scholarly journals A review of parallel computing for large-scale remote sensing image mosaicking

2015 ◽  
Vol 18 (2) ◽  
pp. 517-529 ◽  
Author(s):  
Lajiao Chen ◽  
Yan Ma ◽  
Peng Liu ◽  
Jingbo Wei ◽  
Wei Jie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Parallel Computing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Image Mosaicking

Study on Parallel Computing Method of 3-D Dynamic Analysis of Large-Scale Structure

2013 ◽  
Vol 444-445 ◽  
pp. 916-922
Author(s):  
Ri Qing Lan ◽  
Biao Feng
Keyword(s):  
Parallel Computing ◽  
Dynamic Analysis ◽  
Large Scale ◽  
Domain Decomposition Method ◽  
Time Integration ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Parallel Computer ◽  
Acceleration Method ◽  
Computing Method

In order to solve 3-D dynamic problem for large-scale structure, based on HDDM (Hierarchical Domain Decomposition Method) method, 3-D dynamic finite element parallel computing method of large-scale structure is studied. In this paper, BDD method and Newmark-β average acceleration method in time integration is used. Based on ADVENTRUE program, the corresponding linear implicit transient dynamic parallel program which is based on the MPI parallel environment and can be used in shared memory and distributed memory parallel computer is prepared. Several pre-conditioners are contrasted using some examples, and the results show that BDD preconditioner or BDD-DIAG preconditioner can be used in 3-D dynamic analysis for large-scale structure.

A Remote Sensing and GIS Integrated Study on Assessment of Change in Land Use and Loss of Agricultural Land due to Urbanization – A Case Study of Nashik City

2021 ◽  
Vol 9 (8) ◽  
pp. 3055-3063
Author(s):  
Dipti Bakare
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Rapid Change ◽  
Remote Sensing And Gis ◽  
Suitability Analysis ◽  
The World

Abstract: Urbanization may be a process having a serious impact ashore use characteristics. Basically, as an impression of urbanization, the world is observed with rapid change within the land use character of agricultural land. Generally, the agricultural land is employed for various development activities like industrial establishments, residential colonies and other urban infrastructure during the method of urbanization. it's necessary to possess a periodical assessment of land use change for the developing populated area , which helps to make a decision the longer term expansion strategies for the world. Nashik city is located in the state of Maharashtra in the western part of India. It is one of the most dynamic cities of India with a rapid growth rate due to migration from various parts of Maharashtra. The Nashik city is presently spread over an area of 264.15 sq. km. with a periodical increase in municipal corporation boundary during the last few decades. As a result of urbanization and expansion of municipal corporation limits, the city has undergone drastic changes in land use character. In this study, land-use change is quantified for the existing six zones of Nashik city during the last 30 years using remote sensing and GIS. The study has analysed the relationship between urban expansion and the loss of agricultural land because of an increase in a built-up area and other land use. The study present excellent scenario for land use change during the year 1991, 2001, 2011 and 2020. This can surely guide the development strategies for the study area of Nashik. Also the study can be extended for conducting a suitability analysis to assess future change of land use based on various criteria. Keywords: Land use, Remote sensing, GIS, Supervised classification, Urbanization, Agricultural land loss

scholarly journals Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids

2019 ◽  
Vol 11 (12) ◽  
pp. 1500 ◽  
Author(s):  
Ning Yang ◽  
Diyou Liu ◽  
Quanlong Feng ◽  
Quan Xiong ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Parallel Computing ◽  
Random Forest ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Remote Sensing Images ◽  
Medium Resolution ◽  
Crop Mapping

Large-scale crop mapping provides important information in agricultural applications. However, it is a challenging task due to the inconsistent availability of remote sensing data caused by the irregular time series and limited coverage of the images, together with the low spatial resolution of the classification results. In this study, we proposed a new efficient method based on grids to address the inconsistent availability of the high-medium resolution images for large-scale crop classification. First, we proposed a method to block the remote sensing data into grids to solve the problem of temporal inconsistency. Then, a parallel computing technique was introduced to improve the calculation efficiency on the grid scale. Experiments were designed to evaluate the applicability of this method for different high-medium spatial resolution remote sensing images and different machine learning algorithms and to compare the results with the widely used nonparallel method. The computational experiments showed that the proposed method was successful at identifying large-scale crop distribution using common high-medium resolution remote sensing images (GF-1 WFV images and Sentinel-2) and common machine learning classifiers (the random forest algorithm and support vector machine). Finally, we mapped the croplands in Heilongjiang Province in 2015, 2016, 2017, which used a random forest classifier with the time series GF-1 WFV images spectral features, the enhanced vegetation index (EVI) and normalized difference water index (NDWI). Ultimately, the accuracy was assessed using a confusion matrix. The results showed that the classification accuracy reached 88%, 82%, and 85% in 2015, 2016, and 2017, respectively. In addition, with the help of parallel computing, the calculation speed was significantly improved by at least seven-fold. This indicates that using the grid framework to block the data for classification is feasible for crop mapping in large areas and has great application potential in the future.

scholarly journals Spatial change detections and inventory of wetlands in Yenagoa Urban Area: Bayelsa State, Nigeria

2021 ◽  
Vol 6 (6) ◽  
pp. 230-240
Author(s):  
Eze Promise I ◽  
Elemuwa IC ◽  
Lawrence Hart
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Area ◽  
Urban Expansion ◽  
Rate Of Change ◽  
Percentage Change ◽  
Annual Rate ◽  
Land Use Land Cover ◽  
Total Percentage

Yenegoa Town has in recent years witnessed rapid City growth and Urban development and much of these developments are unplanned and unregulated. This has seriously impacted on wetlands in several locations of the town as persistent Wetlands reclamations are being witnessed in study area. This prompted the need for the study which is aimed to map wetlands location in Yenagoa’s urban area using GIS and Remote Sensing approach. The study analyzes land use/land cover changes (LULC) using LANDSAT(5) TM, LANDSAT(5) ETM and LANDSAT(7) OLI satellite imageries of 1990, 2000, 2010 and 2020 respectively. Through this study, the pattern of urban expansion for Thirty years were been studied. The satellite imageries covering the area were acquired and analyzed using ArcGIS 10.1 and ENVI 5.0 software. The supervised image classification method was adopted and the classification results were validated using the Kappa Index of Agreement (KIA) yielding an accuracy of 0.69m for year 1990, 0.62m for year 2000, 0.58m for year 2010 and 0.73m for 2020. A total area of 13,741.4 hectares was delineated in the study area which is identified as Yenagoa’s urban area. After processing the imageries, four land use/land cover (LULC) classes where considered, and the results shows that Built-up area continuously increased in land area from 1990 -2020 with total percentage change of 273.31% (4,178.7ha) and total annual rate of change of 25.33. Vegetation have total percentage change of 38.55% (974.34Ha) and total annual rate of change of 3.85, wetland cover loss with total percentage Change of 61.96% (-51,44.99ha) and total annual rate of change of -6.19ha, and the water body have loss of total percentage of -2.16% (-8.05Ha) and total annual rate of change of -0.22ha wetland at Yenegwe loss by Total %change of -29.918% ( -197.95ha), and wetland at Igbogene loss by total percentage change of -36.028% (-358.7ha). The research findings also revealed that the wetlands in Anyama, Swali, Kpansia and Opolo Towns were completely lost from the third Epoch of 2010, this may be as a result of persistence reclamation of wetland in this parts of the study area. The Markov Chain predicted model were utilized for predicting the likely changes in land use land cover for a period of thirty years. The predicted results also indicates that wetland size of 32.47,%, 30.68% and 28.99% may likely be lost by the year 2030, 2040 and 2050 respectively in study area if no action is taking by concerned authorities to forestall the factors responsible for the lost in wetland. The study justified the dynamics of remote sensing and GIS techniques in modeling wetlands changees over these periods, wise use of wetland resources and improvement of institutional arrangement were recommended so that wetland policies can be fully integrated into the planning process across all disciplines.

scholarly journals Land Use Change Detection and Urban Sprawl Monitoring in Metropolitan Area of Jakarta (Jabodetabek) from 2001 to 2015

2019 ◽  
Author(s):  
Ismail Fata Robbany ◽  
Arash Gharghi ◽  
Karl-Peter Traub
Keyword(s):  
Urban Area ◽  
Metropolitan Area ◽  
Urban Sprawl ◽  
Urban Areas ◽  
Agricultural Land ◽  
Central City ◽  
Urban Expansion ◽  
South East Asia ◽  
The World ◽  
Increasing Demand

Being 13th largest city in the world makes Jakarta as a fascinating city in South East Asia. Its surrounding regions are included in a particular metropolitan area called “Jabodetabek”. Population growth in this metropolitan area about 10 million only in 15 years from 2000 to 2015. Consequently, loss of vegetation and agricultural land, less water resources, increasing demand for housing and transportation infrastructure as the effect of this ever-growing population take place. This phenomenon can be detected using Landsat satellites images. The settlement or urban area in Jabodetabek shows a huge increase in percentage from 2001 to 2015, so much that the urban area is the dominant land cover and reaches up to 61 percent of Jabodetabek in year 2015. Moreover settlement density in Jabodetabek (ring zones 25 to 45 km from central city) shows an increase of more than 20% urban areas in year 2015. Furthermore, the result of compactness reveals that this urban expansion in Jabodetabek was spread out from 2001 to 2008 and became more compacted by 2015.

scholarly journals A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6442 ◽  
Author(s):  
Panagiotis Barmpoutis ◽  
Periklis Papaioannou ◽  
Kosmas Dimitropoulos ◽  
Nikos Grammalidis
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Forest Fires ◽  
Large Scale ◽  
Fire Detection ◽  
Future Research ◽  
Optical Remote Sensing ◽  
Effective Prevention ◽  
Detection Systems ◽  
The World

The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.

scholarly journals Evaluation of Potential Geographic Distribution for Large-Scale Photovoltaic System in Suburbs of China

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Masashi Kawase ◽  
Keiichi Okajima ◽  
Yohji Uchiyama
Keyword(s):  
Information System ◽  
Urban Area ◽  
Emission Reduction ◽  
Large Scale ◽  
Photovoltaic System ◽  
Solar Irradiation ◽  
Pv System ◽  
Pv Systems ◽  
The World ◽  
Potential Geographic Distribution

Since China is the largest CO2emitting country in the world, photovoltaic (PV) systems are expected to be widely installed to reduce CO2emission. In general, available area for PV installation depends on urban area due to differences in land use and slope. Amount of electricity generated by a PV system also depends on urban area because of differences in solar irradiation and ambient temperature. The aim of this study is to evaluate the installation of large-scale PV systems in suburbs of China, taking these differences into consideration. We have used a geographic information system (GIS) to evaluate amounts of installation capacity of large-scale PV systems, electricity generated, and CO2emission reduction by the installation capacity of large-scale PV systems in suburbs of Liaoning, Shanghai, Anhui, and Guangdong. In Liaoning, the amount of CO2emission reduction by the installation capacity of large-scale PV systems was estimated to be the largest, 3,058 kt-CO2/yr, due to its larger amount of the installation capacity, 2439.4 MW, than the amount of the installation capacity in other regions.

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