scholarly journals Study on Coastline Changes of Xiamen City Based on Remote Sensing Images

2019 ◽  
Vol 136 ◽  
pp. 05003
Author(s):  
Yanfang Qin ◽  
Lin Ye ◽  
Siming Chen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Length Change ◽  
Vector Data ◽  
Remote Sensing Images ◽  
Change Rate ◽  
Xiamen City ◽  
Spatio Temporal

Based on the Landsat remote sensing data, this paper had monitored the coastline changes of Xiamen city in recent 20 years. By extracting the coastline vector data of 1999, 2005, 2011 and 2017 respectively, the spatio-temporal characteristics of coastline changes on coastline length, change rate and land change area were analyzed, and the main driving factors were analyzed combined with the land use changes in the coastal swing area. The results show that: the total length of Xiamen's coastline increased from 235.16 km to 264.98 km during 1999-2017, and the land area increased from 1558.84 km2 to 1594.29 km2. The most significant changes occurred in Xiang'an district and Huli district with the coastline length increased by 16.38% during 2011-2017 and 22.14% during 1999-2005 respectively, while the changes were not very conspicuous in other areas. According to the land use changes in the coastal areas, the coastline changes in Xiamen City were mainly related to the expansion of construction land and port constructions in Haicang district, Xiang'an district and Huli district, as well as the expansion of aquaculture in the Xiang'an district.

scholarly journals Research on Land Use Changes in Panjin City Basing on Remote Sensing Data

2018 ◽  
Vol 153 ◽  
pp. 062050
Author(s):  
Hua Ding ◽  
Ru Ren Li ◽  
Li Shuang Sun ◽  
Xin Wang ◽  
Yu Mei Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework

2019 ◽  
Vol 8 (10) ◽  
pp. 454 ◽  
Author(s):  
Junfeng Kang ◽  
Lei Fang ◽  
Shuang Li ◽  
Xiangrong Wang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Cellular Automata ◽  
Markov Model ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Remote Sensing Images ◽  
Mapreduce Framework ◽  
Construction Land ◽  
Model Based

The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.

scholarly journals Modeling soil erosion and deposition within a Mediterranean mountainous environment utilizing remote sensing and GIS – Wadi Tlata, Morocco

2008 ◽  
Vol 63 (1) ◽  
pp. 36-47 ◽  
Author(s):  
H. Chen ◽  
L. A. Lewis ◽  
A. El Garouani
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Loss ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Erosion And Deposition ◽  
Critical Areas ◽  
Time Period ◽  
Decision Making Processes ◽  
Olive Trees

Abstract. This article presents the results of the GIS-based analysis of four Landsat and Spot images covering a fifteen year period (1987, 1994, 2000, 2002). The purpose of the study was to establish a means of rapidly determining land cover and land use changes, as well as spatial patterns of erosion and deposition, in areas with relatively poor data bases and where soil loss results primarily from nonchannelized flows. The procedure selected involved the following: establishment of land use class distribution and size for each year of observation, static estimation of soil loss, calculation of net erosion and deposition, and prioritisation of critical areas. Thus, for the targeted 123 km2 Tlata catchment of northeastern Morocco, six main land use classes could be defined (highly degraded lands, annual cereal crops fields, mixed farmlands, olive trees, reforested areas, and natural protected forest). Analysis of remote sensing data allowed establishment of the areal distribution of each land use class for each year. Soil loss was estimated using a RUSLE module integrated in a GIS framework. These static areal estimates of soil loss were then fed into a sedimentation algorithm that models downslope movement of soil loss. From the resulting spatial (flow) movements, net erosion and deposition for each time period could be estimated. The results permit, at the least, an ordinal ranking of erosion and deposition within the basin. This supports decision-making processes on prioritization of areas where interventions are needed to ameliorate or prevent land degradation.

scholarly journals Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery

2019 ◽  
Author(s):  
Iswari Nur Hidayati ◽  
R Suharyadi ◽  
Projo Danoedoro
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Heterogeneity ◽  
Urban Ecology ◽  
Vegetation Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Ecological Index ◽  
Sensing Data

The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.

ANALISIS SPASIAL POLA PERUBAHAN PENGGUNAAN LAHAN PERTANIAN (STUDI KASUS NAGARI CUBADAK)

2019 ◽  
Vol 4 (2) ◽  
pp. 62-68
Author(s):  
Afrital Rezki, S.Pd., M.Si ◽  
Erna Juita ◽  
Dasrizal Dasrizal ◽  
Arie Zella Putra Ulni
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Survey Methods ◽  
Descriptive Analysis ◽  
Remote Sensing Data ◽  
Physical Factors ◽  
Sports Facilities ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Type Of Land Use

Perkembangan penggunaan tanah secara spasial di Nagari Cubadak dibatasi oleh faktor fisik yang didominasi oleh kemiringan landai dan agak sedikit curam. Penelitian ini dilakukan dengan tujuan untuk  mengetahui dan menganalisis Penggunaan tanah dan Pola perubahan penggunaan tanah untuk pertanian secara spasial di Nagari Cubadak. Penelitian ini menggunakan metode yang dilakukan adalah metode interpretasi citra penginderaan jauh, metode survey, dan analisis deskriptif berbasis keruangan. Interpretasi citra penginderaan jauh dilakukan untuk mengetahui informasi jenis penggunaan lahan khususnya pertanian aktual dan tahun-tahun terdahulu berdasarkan nilai digital yang terekam pada data penginderaan jauh. Dari penelitian ini dapat disimpulkan bahwa, Penggunaan tanah di Nagari Cubadak bisa diklasifikasikan delapan (8) jenis penggunaan lahan yakni; Bangunan Umum, Fasilitas Olahraga, Kolam, Makam, Perumahan, Sawah, Tanah Kosong, Tegalan dan Tempat Ibadah. Kemudian, pengurangan penggunaan tanah 1990–2000 yang paling banyak terdiri dari penggunaan tanah tegalan dengan 91 kavling, paling banyak berubah menjadi perumahan sebanyak 75 kavling, kemudian pengurangan sawah dengan 25 kavling, paling banyak berubah menjadi tegalan dengan 35 kavling dan kolam 20 kavling dengan pengurangan 52 kavling.The development of spatial land use in Nagari Cubadak limited by physical factors which are dominated by sloping slopes and slightly steep. This research was conducted with the aim to find out and analyze land use and the pattern of changes in land use for agriculture spatially in Nagari Cubadak. This study uses the method used is the method of interpretation of remote sensing images, survey methods, and spatial-based descriptive analysis. Interpretation of remote sensing imagery is done to find out information on the type of land use, especially actual and previous years based on digital values recorded on remote sensing data. From this study it can be concluded that, Land use in Nagari Cubadak can be classified as eight (8) types of land use namely; Public Buildings, Sports Facilities, Swimming, Graves, Housing, Paddy Fields, Empty Land, fields and places of worship. Then, the reduction in land use from 1990 to 2000 which mostly consisted of the use of upland land with 91 plots, at most turned into housing lots of 75 plots, then reduced fields with 25 plots, most changed to moor with 35 plots and pools of 20 plots with subtraction 52 lots.

scholarly journals Environmental and spatial risk factors for the larval habitats of Plasmodium knowlesi vectors in Sabah, Malaysian Borneo

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Isabel Byrne ◽  
Wilfredo Aure ◽  
Benny O. Manin ◽  
Indra Vythilingam ◽  
Heather M. Ferguson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Remote Sensing ◽  
Land Use ◽  
Plasmodium Knowlesi ◽  
Spatial Scales ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Larval Habitats ◽  
Sensing Data ◽  
Malaysian Borneo

AbstractLand-use changes, such as deforestation and agriculture, can influence mosquito vector populations and malaria transmission. These land-use changes have been linked to increased incidence in human cases of the zoonotic malaria Plasmodium knowlesi in Sabah, Malaysian Borneo. This study investigates whether these associations are partially driven by fine-scale land-use changes creating more favourable aquatic breeding habitats for P. knowlesi anopheline vectors. Using aerial remote sensing data, we developed a sampling frame representative of all land use types within a major focus of P. knowlesi transmission. From 2015 to 2016 monthly longitudinal surveys of larval habitats were collected in randomly selected areas stratified by land use type. Additional remote sensing data on environmental variables, land cover and landscape configuration were assembled for the study site. Risk factor analyses were performed over multiple spatial scales to determine associations between environmental and spatial variables and anopheline larval presence. Habitat fragmentation (300 m), aspect (350 m), distance to rubber plantations (100 m) and Culex larval presence were identified as risk factors for Anopheles breeding. Additionally, models were fit to determine the presence of potential larval habitats within the areas surveyed and used to generate a time-series of monthly predictive maps. These results indicate that land-use change and topography influence the suitability of larval habitats, and may partially explain the link between P. knowlesi incidence and deforestation. The predictive maps, and identification of the spatial scales at which risk factors are most influential may aid spatio-temporally targeted vector control interventions.

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