scholarly journals Spatial Distribution Patterns Analysis of Hotspot in Central Kalimantan using FIMRS MODIS Data

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Adisty Pratamasari ◽  
Ni Ketut Feny Permatasari ◽  
Tia Pramudiyasari ◽  
Masita Dwi Mandini Manessa ◽  
Supriatna Supriatna
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Correlation Analysis ◽  
Spatial Pattern ◽  
Forest Fires ◽  
Nearest Neighbor ◽  
Kernel Density ◽  
Distribution Patterns ◽  
Central Kalimantan ◽  
Noaa Avhrr

<p><em>One of the ways to observe the </em><em>hotspot created by </em><em>forest fires in Indonesia </em><em>is </em><em>through </em><em>Remote sensing imagery, such as MODIS, NOAA AVHRR, etc</em><em>. </em><em>Central Kalimantan is one of the areas in Indonesia with the highest hotspot data. In this research, MODIS FIRMS hotspot data in Central Kalimantan collected from 2017 – 2019, covering 13 districts: South Barito, East Barito, North Barito, Mount Mas, Kapuas, Katingan, Palangkaraya City, West Kotawaringin, East Kotawaringin, Lamandau, Murung Raya, Pulang Pisau, Seruyan, and Sukamara. That is four aspects that this research evaluated: 1) evaluating the spatial pattern using the Nearest Neighbor Analysis (NNA); 2) evaluate the hotspot density appearance using Kernel Density; and 3) correlation analysis between rainfall data and MODIS FIRMS. As a result, the hotspot in Central Kalimantan shows a clustered pattern. While the natural breaks KDE algorithm shows the most relevant result to represent the hotspot distribution. Finally, the hotspot is low correlated with rainfall; however, is see that most of the hotspot (~90%) appeared in low rainfall month (less than 3000 mm/month).</em></p><p><strong><em>Keywords</em></strong><em>: Forest fire, Hotspot, NNA, Kernel density, Central Kalimantan</em></p>

scholarly journals Geospatial Distribution of Public Secondary in Gombe Local Government Area, Gombe State of Nigeria

2019 ◽  
Vol 1 (1) ◽  
pp. 30-39
Author(s):  
Yila Caiaphas Makadi ◽  
Abecca Stephen Sati ◽  
Ismail Dankaka
Keyword(s):  
Spatial Distribution ◽  
Local Government ◽  
Spatial Pattern ◽  
Nearest Neighbor ◽  
Secondary Data ◽  
Local Government Area ◽  
Primary Data ◽  
Public Secondary School ◽  
Data Survey ◽  
Senior Secondary

The paper reviews research tradition of accessibility level and spatial distribution of student in public secondary school in gombe local government area, Gombe state. Primary and secondary data were used for the study. Primary data was collected using questionnaire and a hand-held GPS receiver to capture the coordinate points of schools and other relevant data. Secondary data include administrative map, population figures of both students and Teachers, Names and addresses of the secondary schools in the study area. The data were analyzed using geographic information techniques. From the data survey carried out, the result of the analysis showed the accessibility level and spatial distribution of school in Gombe were seventeen (17) public senior secondary and total number of students were nineteen thousand and eleven (19,011). The nearest neighbor analysis (NNA) for the spatial pattern of school were carried out based on each ward in study area which as ten (10) wards in each ward revealed two major spatial distributions. The spatial pattern of the Gombe LGA has Nearest Neighbour Ratio (NNR): 3.385087, Bolari East ward with NNR: 3.385087 and Shamaki wards NNR: 1.600148, which showed dispersed pattern, while Jekada Fari ward with NNR: 0.214890, Pantami ward with NNR: 0.226863, and Herwo Gana wards with NNR: 0.185239, were showed clustered pattern. The nearest neighbor index shows clustered pattern for all the wards in the local government area except Bolari East and Shamaki wards that has dispersed pattern of distribution. The implication of these two patterns means that accessibility is poor in the study area. Students travel than normal to overcome the function of distance.

scholarly journals The spatial distribution characteristics, influencing factors and development models of rural tourism in China

2021 ◽  
Vol 261 ◽  
pp. 03022
Author(s):  
Jia Lei ◽  
Yanhong Liu
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Nearest Neighbor ◽  
Buffer Zone ◽  
Structural Features ◽  
Yangtze River Delta ◽  
River Delta ◽  
Rural Tourism ◽  
Geographic Concentration ◽  
Development Models

As an essential support for rural revitalization and global tourism strategies, rural tourism has a lot to do in the dual-cycle development pattern. Based on the first and second batch of crucial rural tourism villages in the country, the nearest neighbor index, geographic concentration index, Gini coefficient, standard deviation ellipse, nuclear density and other methods are used to analyze their spatial distribution types, spatial balance, spatial pattern and evolution. Then, the factors influencing the distribution differences are analyzed by using the tools of geographic connection rate and buffer zone. From a configuration perspective, QCA is used to explore the development model of crucial rural tourism villages. The results show that: ①China’s rural tourism crucial villages along the Hu Huanyong line show a condensed distribution characteristic of “sparsely located in the southeast and sparsely northwest”. The distribution within the provinces is weakly concentrated and the spatial imbalance is prominent. Morover, the spatial pattern is evident from the Northeast to the Southwest and shows the insense evolution trend. ②The crucial rural tourism villages are spatially formed with the dual-core radiation and multi-regional structural features centered on the Beijing-Tianjin-Hebei, Yangtze River Delta, and supplemented by several sub-high-density areas such as Guanzhong, Sichuan, Chongqing, Guizhou, and Pearl River Delta, and finally formed the radiation pattern of urban agglomerations-central cities-crucial rural tourism villages. ③Natural geography, source market, transportation environment, economic level, and endowment of tourism resources are important factors that affect the spatial distribution of crucial rural tourism villages. ④There are three main development models of crucial rural tourism villages in China: source market-driven, traffic-oriented and scenic-radiant.

Small-scale spatial patterns of two soil lichens in semi-arid Mediterranean steppe

2003 ◽  
Vol 35 (1) ◽  
pp. 71-81 ◽  
Author(s):  
Fernando T. Maestre
Keyword(s):  
Spatial Distribution ◽  
Correlation Analysis ◽  
Spatial Pattern ◽  
Soil Surface ◽  
Arid Ecosystems ◽  
Small Scale ◽  
Significant Relationships ◽  
Arid Steppe ◽  
The Individual ◽  
Semi Arid

AbstractBiological crusts are a common feature of the soil surface in arid and semi-arid ecosystems, where they play a major role in ecosystem functioning. In recent years, there has been an increasing interest in the ecophysiology, floristics, and dynamics of crust-forming lichens but little is known about the effect of vascular plants on their small-scale spatial distribution. To increase our understanding about the interactions between crust-forming lichens and vegetation in semi-arid areas, the spatial pattern and interaction of two soil lichens, Cladonia convoluta and Squamarina cartilaginea, at two microsites in semi-arid Stipa tenacissima steppe of south-eastern Spain are evaluated. The aim of this study was to determine if the microsite provided by Stipa tussocks promoted changes in the individual patterns and in the spatial covariation of these soil lichens. Spatial analysis by distance index (SADIE) coupled with correlation analysis was used to explore the individual patterns and the spatial relationships between the two species. SADIE detected a significant clumped pattern in the spatial distribution of both species, but Stipa tussocks promoted changes only in the spatial pattern of Cladonia. Correlation analysis revealed the presence of significant relationships between the two species, particularly close to Stipa tussocks. The results show that the microenvironment provided by Stipa is able to modify the small-scale spatial pattern of soil lichens in semi-arid steppe, and suggest the presence of facilitation between Stipa and Cladonia.

Construction of a New Spatial Pattern of Industries under the Industrial Transfer in Xi'an Metropolitan Area

2012 ◽  
Vol 174-177 ◽  
pp. 2243-2247
Author(s):  
Yong Pan ◽  
Xiao Jian Chen
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Metropolitan Area ◽  
Distribution Patterns ◽  
Analysis Method ◽  
Industrial Transfer ◽  
Main Component Analysis ◽  
Countermeasures And Suggestions ◽  
Surrounding Areas ◽  
Main Component

Under the development background of Xi’an international metropolitan area, the industries spatial distribution patterns facing with Xi’an and its surrounding areas change greatly. The authors explore the current industries distribution characters in Xi’an based on the industrial spatial transfer perspective and empirical analysis. Then the authors discuss the potential holding spaces for industrial transfer based on the main component analysis method. Finally, the new pattern and some countermeasures and suggestions are put forward.

scholarly journals FRACTAL DIMENSIONS FOR RADIOISOTOPE POLLUTION PATTERNS BY NUCLEAR POWER PLANT ACCIDENTS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 919-925
Author(s):  
K. Saito ◽  
S. Ogawa
Keyword(s):  
Spatial Distribution ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Fractal Analysis ◽  
Nuclear Power ◽  
Fractal Dimensions ◽  
Distribution Patterns ◽  
Nuclear Power Plant Accident ◽  
Spatial Distribution Patterns ◽  
Noaa Avhrr

The radioisotope pollution shows two types of patterns: dry and wet deposits for nuclear power plant accidents. Two surface pollution patterns were analysed by fractal. In Fukushima nuclear power plant accident, surface pollution by wet deposits was estimated to occur. However, actually it was no rain and white crystals were observed on the surface. Then, fractal analysis was carried out for the spatial distribution patterns of radio isotopes on the surface to judge the types of deposits. As a reference, Chernobyl nuclear power plant accident was checked for the spatial distribution patterns of radioisotopes on the surface. The objective patterns by fractal analysis were the surface pollution maps in Fukushima and Chernobyl, Abukuma river watershed map, and NOAA/AVHRR. The calculation of fractal dimensions was carried out with the box counting for binarized images. Fractal analysis results suggested the next conclusions. The radioisotope pollution in Fukushima might occur in both dry and wet deposits. The dry deposit might make the pollution pattern similar to the watershed, while the wet deposit might make the pollution pattern similar to cloud images. Moreover, most radioisotope contaminants might flow on the road in the forest valley and deposit on forest with and without rainfall in Fukushima.

scholarly journals REMOTE SENSING-BASED DETECTION AND SPATIAL PATTERN ANALYSIS FOR GEO-ECOLOGICAL NICHE MODELING OF TILLANDSIA SPP. IN THE ATACAMA, CHILE

2016 ◽  
Vol XLI-B2 ◽  
pp. 251-256
Author(s):  
N. Wolf ◽  
A. Siegmund ◽  
C. del Río ◽  
P. Osses ◽  
J. L. García
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Vegetation Cover ◽  
Ecological Niche Modeling ◽  
Distribution Patterns ◽  
Spatial Pattern Analysis ◽  
Gis Analysis ◽  
Stratocumulus Clouds ◽  
Coastal Fog ◽  
Topographic Constraints

In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called ‘fog oases’ dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of <i>Tillandsia spp.</i> in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of <i>Tillandsia spp.</i> Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

scholarly journals HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN

2020 ◽  
Vol 16 (2) ◽  
pp. 143
Author(s):  
Khalifah Insan Nur Rahmi ◽  
Sayidah Sulma ◽  
Indah Prasasti
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
Near Infrared ◽  
The Other ◽  
Landsat 8 ◽  
Forest Land ◽  
Field Surveys ◽  
Infrared Wavelength ◽  
Central Kalimantan ◽  
Middle Infrared

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%.

scholarly journals Spatial patterns of a tropical tree species growing under an eucalyptus plantation in South-East Brazil

2010 ◽  
Vol 70 (2) ◽  
pp. 271-277 ◽  
Author(s):  
P. Higuchi ◽  
AC Silva ◽  
JNC Louzada ◽  
ELM Machado
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Basal Area ◽  
Tree Height ◽  
Distribution Patterns ◽  
Eucalyptus Plantation ◽  
Source Distance ◽  
Se Brazil ◽  
Population Competition ◽  
Linear Regressions

The objectives of this study were to evaluate the influence of propagules source and the implication of tree size class on the spatial pattern of Xylopia brasiliensis Spreng. individuals growing under the canopy of an experimental plantation of eucalyptus. To this end, all individuals of Xylopia brasiliensis with diameter at soil height (dsh) > 1 cm were mapped in the understory of a 3.16 ha Eucalyptus spp. and Corymbia spp. plantation, located in the municipality of Lavras, SE Brazil. The largest nearby mature tree of X. brasiliensis was considered as the propagules source. Linear regressions were used to assess the influence of the distance of propagules source on the population parameters (density, basal area and height). The spatial pattern of trees was assessed through the Ripley K function. The overall pattern showed that the propagules source distance had strong influence over spatial distribution of trees, mainly the small ones, indicating that the closer the distance from the propagules source, the higher the tree density and the lower the mean tree height. The population showed different spatial distribution patterns according to the spatial scale and diameter class considered. While small trees tended to be aggregated up to around 80 m, the largest individuals were randomly distributed in the area. A plausible explanation for observed patterns might be limited seed rain and intra-population competition.

DISTRIBUSI SPASIAL INDUSTRI DI KABUPATEN PADANG PARIAMAN

Horizon ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 133-143
Author(s):  
Sendi Sendi
Keyword(s):  
Spatial Distribution ◽  
Nearest Neighbor ◽  
Descriptive Analysis ◽  
Sampling Technique ◽  
Population Data ◽  
Distribution Patterns ◽  
Random Pattern ◽  
Industrial Growth ◽  
Analysis Techniques ◽  
Goods And Services

This study aims to obtain data, manage, analyze, discuss the spatial distribution of industries in Padang Pariaman District, seen from: 1) Spatial Distribution of Large and Medium Industries, 2) Spatial Patterns, and 3) Industrial Impacts on Society. This type of research is included in the descriptive study. The population in this study are all large and medium industries in Padang Pariaman District. The sample of this research is all large and medium industries in the Padang Pariaman District taken by sampling technique that is total sampling where the number of samples is equal to the population. Data analysis techniques using the nearest neighbor analysis and descriptive analysis. The results of this study found that: 1) in Padang Pariaman District there were 57 large and medium industries spread across 9 Subdistricts of 17 Subdistricts. The distribution of large and medium industries is most found in Batang Anai Sub District with 31 industries. Based on industry classification, it is divided into 4 types, namely: industry based on the number of workers and capital, industry based on goods produced, industry based on type, and industry based on general activities carried out, 2) distribution patterns of large and medium industries in Padang Pariaman District more scattered cluster (cluster). For the pattern of distribution of large industries in Padang Pariaman District is more unevenly distributed (random pattern), whereas the tendency of the pattern of medium industry distribution in Padang Pariaman District is more spread in clusters, 3) Viewed from industrial growth in Padang Pariaman District from 2016 -2018 continues to increase so that the need for labor also increases. All of that greatly affects the welfare of society and the value of goods and services produced by the economic system in Padang Pariaman District.

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