scholarly journals The spatial distribution of sago palm landscape Sentani watershed in Jayapura District, Papua Province, Indonesia

2021 ◽  
Vol 22 (9) ◽  
Author(s):  
PETRUS ABRAHAM DIMARA ◽  
RIS HADI PURWANTO ◽  
SIGIT SUNARTA
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Field Survey ◽  
Steep Slope ◽  
Landsat 8 ◽  
Soil Erodibility ◽  
Slope Gradient ◽  
Sago Palm ◽  
Optimal Distance ◽  
Mediterranean Soil

Abstract. Dimara PA, Purwanto RH, Sunarta S, Wardhana W. 2021. The spatial distribution of sago palm landscape Sentani watershed in Jayapura District, Papua Province, Indonesia. Biodiversitas 22: 3811-3820. Sago palm is one of the starch sources used as local food in Papua, therefore this research aims to identify the supporting environment for the plant to grow by utilizing spatial data. The methods used were Spatial Analysis and Field Survey, where the first employed satellite imagery of Quickbird in 2012 and Landsat 8 in 2020 to distinguish between sago and non-sago palm landscape. In the process, five parameters were used, consisting of covering land elevation, slope gradient, soil type, rainfall as well as the optimal distance from the river and lake. The result showed the sago palm landscape in Sentani Watershed lies in the elevation of 0-450 m asl, while its largest habitat which lies between 0-100 m asl covering an area of 4,385.63 is found in a flat slope covering an area of 2,941.99 ha and in a very steep slope that spreads out over 41.92 ha. Generally, in Sentani Watershed, the plant grows in Mediterranean soil possessing thick solum with pH 5.0-7.0 and medium to great soil erodibility. Moreover, the largest habitat experiences a precipitation rate of 1,750 mm yr-1 covering a total of 6,846.24 ha, while the Doyo River has the largest sago palm landscape compared to other rivers.

scholarly journals The Face of the Banjarbaru City Wetlands in Last Four Decades

2019 ◽  
Vol 6 (2) ◽  
pp. 130
Author(s):  
Syam'ani Syam'ani ◽  
Abdi Fithria ◽  
Eva Prihatiningtyas
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Central Government ◽  
Reduction Rate ◽  
Schedule Algorithm ◽  
Landsat 8 ◽  
Linear Pattern ◽  
The Past ◽  
Object Based ◽  
The Face

The change of Banjarbaru city status into the central government of South Kalimantan Province, has the potential to increase the need for land. This directly affects wetlands conversion activities into other forms of land closure. This research aims to map the spatial distribution of wetlands, and the spatial distribution of wetlands conversion existing in Banjarbaru City in every decade over the last four decades, ie from the 1970s to the present. Wetlands spatial data are extracted from multitemporal satellite imagery, Landsat 5 in 1973, Landsat 5 in 1989, Landsat 5 in 1997, Landsat 5 in 2007, and Landsat 8 in 2016. The method used to extract wetlands is Object Based Image Analysis (OBIA), with Full Lambda-Schedule algorithm. The research results show that over the past last decades, the total area of Banjarbaru City's wetlands has been reduced continuously. The average total reduction rate is 534.53 hectares per decade or about 53.5 hectares per year, with a linear pattern over the past four decades.

scholarly journals Influence of Slope Gradient and Aspect on Soil Organic Carbon Content in the Region of Niš, Serbia

2021 ◽  
Vol 13 (15) ◽  
pp. 8332
Author(s):  
Snežana Jakšić ◽  
Jordana Ninkov ◽  
Stanko Milić ◽  
Jovica Vasin ◽  
Milorad Živanov ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Slope Aspect ◽  
Available Phosphorus ◽  
Organic Carbon Content ◽  
Slope Gradient ◽  
Phosphorus And Potassium ◽  
Uncultivated Land

Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

Mapping the Spatial Distribution Patterns of Personal Time Spent Based on Trip Purpose

2018 ◽  
Vol 9 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Ko Ko Lwin ◽  
Yoshihide Sekimoto
Keyword(s):  
Spatial Distribution ◽  
Survey Data ◽  
Spatial Data ◽  
Distribution Patterns ◽  
Spatial Data Mining ◽  
Urban Transport ◽  
Social Characteristics ◽  
Public Facility ◽  
Spatial Distribution Patterns ◽  
Trip Purpose

Understanding the spatial distribution patterns of the time spent by people based on their trip purpose and other social characteristics is important for sustainable urban transport planning, public facility management, socio-economic development, and other types of policy planning. Although personal trip survey data includes travel behavior and other social characteristics, many are lacking in detail regarding the spatial distribution patterns of individual movements based on time spent, typically due to privacy issues and difficulties in converting non-spatial survey data into a spatial format. In this article, geospatially-enabled personal trip data (Geospatial Big Data), converted from traditional paper-based survey data, are subjected to a spatial data mining process in order to examine the detailed spatial distribution patterns of time spent by the public based on various trip purposes and other social characteristics, using the Tokyo metropolitan area as a case study.

scholarly journals Approach to Urban Environmental Justice Using Exploratory Spatial Data Analysis. The Case of Valencia’s Monumental Trees

2020 ◽  
Vol 12 (18) ◽  
pp. 7760
Author(s):  
Alfonso Gallego-Valadés ◽  
Francisco Ródenas-Rigla ◽  
Jorge Garcés-Ferrer
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Environmental Justice ◽  
Spatial Data ◽  
Urban Trees ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
The Public ◽  
Object Of Study ◽  
The City

Environmental justice has been a relevant object of analysis in recent decades. The generation of patterns in the spatial distribution of urban trees has been a widely addressed issue in the literature. However, the spatial distribution of monumental trees still constitutes an unknown object of study. The aim of this paper was to analyse the spatial distribution of the monumental-tree heritage in the city of Valencia, using Exploratory Spatial Data Analysis (ESDA) methods, in relation to different population groups and to discuss some implications in terms of environmental justice, from the public-policy perspective. The results show that monumental trees are spatially concentrated in high-income neighbourhoods, and this fact represents an indicator of environmental inequality. This diagnosis can provide support for decision-making on this matter.

scholarly journals Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia

2020 ◽  
Vol 9 (11) ◽  
pp. 663
Author(s):  
Sanjiwana Arjasakusuma ◽  
Sandiaga Swahyu Kusuma ◽  
Raihan Rafif ◽  
Siti Saringatin ◽  
Pramaditya Wicaksono
Keyword(s):  
Spatial Data ◽  
Vegetation Index ◽  
Google Earth ◽  
Paddy Fields ◽  
Series Data ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Seamless Integration ◽  
Synthetic Aperture Radar Data ◽  
Central Java

The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

scholarly journals Spatial distribution of pedestrian-motor vehicle collisions before and after pedestrian countdown signal installation in Toronto, Canada

2017 ◽  
Vol 25 (2) ◽  
pp. 110-115 ◽  
Author(s):  
Linda Rothman ◽  
Marie-Soleil Cloutier ◽  
Alison K Macpherson ◽  
Sarah A Richmond ◽  
Andrew William Howard
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Motor Vehicle ◽  
Spatial Data Analysis ◽  
Motor Vehicle Collisions ◽  
Vehicle Collisions ◽  
Point Patterns ◽  
Kernel Density Estimates ◽  
Before And After

BackgroundPedestrian countdown signals (PCS) have been installed in many cities over the last 15 years. Few studies have evaluated the effectiveness of PCS on pedestrian motor vehicle collisions (PMVC). This exploratory study compared the spatial patterns of collisions pre and post PCS installation at PCS intersections and intersections or roadways without PCS in Toronto, and examined differences by age.MethodsPCS were installed at the majority of Toronto intersections from 2007 to 2009. Spatial patterns were compared between 4 years of police-reported PMVC prior to PCS installation to 4 years post installation at 1864 intersections. The spatial distribution of PMVC was estimated using kernel density estimates and simple point patterns examined changes in spatial patterns overall and stratified by age. Areas of higher or lower point density pre to post installation were identified.ResultsThere were 14 911 PMVC included in the analysis. There was an overall reduction in PMVC post PCS installation at both PCS locations and non-PCS locations, with a greater reduction at non-PCS locations (22% vs 1%). There was an increase in PMVC involving adults (5%) and older adults (9%) at PCS locations after installation, with increased adult PMVC concentrated downtown, and older adult increases occurring throughout the city following no spatial pattern. There was a reduction in children’s PMVC at both PCS and non-PCS locations, with greater reductions at non-PCS locations (35% vs 48%).ConclusionsResults suggest that the effects of PCS on PMVC may vary by age and location, illustrating the usefulness of exploratory spatial data analysis approaches in road safety. The age and location effects need to be understood in order to consistently improve pedestrian mobility and safety using PCS.

scholarly journals Modelling and quantifying the spatial distribution of post-wildfire ash loads

2016 ◽  
Vol 25 (2) ◽  
pp. 249 ◽  
Author(s):  
Chris J. Chafer ◽  
Cristina Santín ◽  
Stefan H. Doerr
Keyword(s):  
Spatial Distribution ◽  
Polynomial Regression ◽  
Spatial Scales ◽  
Fire Severity ◽  
Spectral Ratio ◽  
Landsat 8 ◽  
Applied Model ◽  
Sampling Locations ◽  
Before And After

Ash is generated in every wildfire, but its eco-hydro-geomorphic effects remain poorly understood and quantified, especially at large spatial scales. Here we present a new method that allows modelling the spatial distribution of ash loads in the post-fire landscape, based on a severe wildfire that burnt ~13 600 ha of a forested water supply catchment in October 2013 (2013 Hall Road Fire, 100 km south-west of Sydney, Australia). Employing an existing spectral ratio-based index, we developed a new spectral index using Landsat 8 satellite imagery: the normalised wildfire ash index (NWAI). Before- and after-fire images were normalised and a differenced wildfire ash image (dNWAI) computed. The relationship between dNWAI and ash loads (t ha−1) quantified in situ at nine sampling locations burnt under a range of fire severities was determined using a polynomial regression (R2 = 0.98). A spatially applied model was computed within a geographic information system (GIS) to illustrate the spatial distribution of ash across the area burnt and to estimate ash loads in the five subcatchments affected by the wildfire. Approximately 181 000 tonnes of ash was produced by the wildfire, with specific loads increasing with fire severity. This new tool to model wildfire ash distribution can inform decisions about post-fire land management in future wildfires in the region. It can also be adapted for its application in other fire-prone environments.

scholarly journals A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Xian-xia Zhang ◽  
Zhi-qiang Fu ◽  
Wei-lu Shan ◽  
Bing Wang ◽  
Tao Zou
Keyword(s):  
Spatial Distribution ◽  
Distributed Systems ◽  
Spatial Data ◽  
Sensor Placement ◽  
Spatiotemporal Data ◽  
Industrial Processes ◽  
Crucial Issue ◽  
Data Set ◽  
Spatially Distributed ◽  
Optimal Sensor Location

Many industrial processes are inherently distributed in space and time and are called spatially distributed dynamical systems (SDDSs). Sensor placement affects capturing the spatial distribution and then becomes crucial issue to model or control an SDDS. In this study, a new data-driven based sensor placement method is developed. SVR algorithm is innovatively used to extract the characteristics of spatial distribution from a spatiotemporal data set. The support vectors learned by SVR represent the crucial spatial data structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number. A systematic sensor placement design scheme in three steps (data collection, SVR learning, and sensor locating) is developed for an easy implementation. Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy controlled spatially distributed systems.

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