scholarly journals Identification of Land Criticality with the Application of Deep Learning in West Lahat District Using Sentinel-2A Imagery

2021 ◽  
Vol 936 (1) ◽  
pp. 012009
Author(s):  
Atika Izzaty ◽  
Bangun Muljo Sukojo
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Near Infrared ◽  
Human Life ◽  
Director General ◽  
Vegetation Density ◽  
Land Productivity ◽  
Productivity Level ◽  
Sentinel 2A ◽  
Critical Class

Abstract Land is an important factor in human life. In addition of land use that continue to increase every year. Land use is an element of meeting needs. This situation often makes the condition of the land around it questionable the content and level of land productivity. Land whose productivity level is lost can cause critical land to occur. Coupled with the occurrence of uncontrolled development, land productivity has decreased. By using the application of remote sensing, it is able to monitor land conditions, one of which is by using Sentinel-2A data. Sentinel-2A image data was selected to identify the condition or distribution of critical land and critical land parameters that has the most influence on criticality level of the land with Sentinel-2A imagery with a spatial resolution of 10 meters for Red, Green, Blue, and Near-Infrared canals to perform NDVI classification processing. closely related to vegetation. Based on the Regulation of the Director General of Watershed Management and Social Forestry Number: P.4/V-SET/2013 concerning the Technical Guidelines for the Preparation of Spatial Data for Critical Lands, there are 5 parameters for determining the criticality of the processed land as indicators, including the level of erosion distribution, productivity land, land management, slope, and vegetation density. Based on the results of the study, the researchers found that the distribution of critical land in Lahat Regency was 19 hectares or 0.56%, the critical class was 36,090 hectares or 10.1%, the critical potential class was 142,140 hectares or 42.1%, the class which was slightly critical is 156,860 hectares or 46.5%, and non critical class is 3 hectares or 0.074%. for very critical class. These results can be seen with the parameter that most affects the criticality of the land is vegetation density.

scholarly journals ANALISIS KERAPATAN VEGETASI UNTUK AREA PEMUKIMAN DENGAN MEMANFAATAN CITRA SATELIT LANDSAT DI KOTA TASIKMALAYA

2019 ◽  
Vol 8 (2) ◽  
pp. 78
Author(s):  
Miftah Kurnia Hayu ◽  
Riki Ridwana
Keyword(s):  
Land Use ◽  
Global Warming ◽  
Human Activity ◽  
Rural Areas ◽  
Urban Areas ◽  
Vegetation Index ◽  
Human Life ◽  
Vegetation Density ◽  
Urban And Rural Areas ◽  
High Level

Global warming is an important issue to discuss because it is very impactful to human life, one of the factors influencing increase global warming is decreasing the green vegetation continuously that exist in both urban and rural areas. Especially in urban areas because it is the center of human activity. The high level of human activity centered in the city leads to an increase in the need for land use which will lead to reduced vegetation density levels. Utilization of remote sensing images can be used to determine the density of vegetation in an area. Vegetation density analysis can be done by means of digital imagery intrepetation using the transformation of NDVI (Normalized Different Vegetation Index). The purpose of this research is to know the land use through the calculation of vegetation index of residential area in Tasikmalaya city.

scholarly journals Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning

2021 ◽  
Vol 10 (5) ◽  
pp. 2821-2835
Author(s):  
Sri Yulianto Joko Prasetyo ◽  
Bistok Hasiholan Simanjuntak ◽  
Kristoko Dwi Hartomo ◽  
Wiwin Sulistyo
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Surface ◽  
Vegetation Indices ◽  
Spatial Prediction ◽  
Machine Learning Algorithms ◽  
Vegetation Density ◽  
Sentinel 2A ◽  
High Degree ◽  
Sentinel 2

This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

2020 ◽  
Vol 5 (1) ◽  
pp. 414
Author(s):  
Amsar Yunan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Flood Hazard ◽  
Parameter Analysis ◽  
Thematic Maps ◽  
Aceh Province ◽  
Object Area ◽  
Use Factors ◽  
Hazard Levels ◽  
Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually.  Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

A GIS-Based Irregular Cellular Automata Model of Land-Use Change

2007 ◽  
Vol 34 (4) ◽  
pp. 708-724 ◽  
Author(s):  
Daniel Stevens ◽  
Suzana Dragićević
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Spatial Data ◽  
Cellular Automata Model ◽  
Population Growth Rates ◽  
Asynchronous Development ◽  
Canadian City ◽  
Ca Model ◽  
Developing Area

This study proposes an alternative cellular automata (CA) model, which relaxes the traditional CA regular square grid and synchronous growth, and is designed for representations of land-use change in rural-urban fringe settings. The model uses high-resolution spatial data in the form of irregularly sized and shaped land parcels, and incorporates synchronous and asynchronous development in order to model more realistically land-use change at the land parcel scale. The model allows urban planners and other stakeholders to evaluate how different subdivision designs will influence development under varying population growth rates and buyer preferences. A model prototype has been developed in a common desktop GIS and applied to a rapidly developing area of a midsized Canadian city.

scholarly journals Current Wildland Fire Patterns and Challenges in Europe: A Synthesis of National Perspectives

2021 ◽  
Vol 14 ◽  
pp. 117862212110281
Author(s):  
Nieves Fernandez-Anez ◽  
Andrey Krasovskiy ◽  
Mortimer Müller ◽  
Harald Vacik ◽  
Jan Baetens ◽  
...  
Keyword(s):  
Land Use ◽  
Large Scale ◽  
Wildland Fire ◽  
Human Life ◽  
Local Analysis ◽  
Wildland Fires ◽  
Fire Statistics ◽  
Quantitative Evidence ◽  
Wide Range ◽  
Fire Patterns

Changes in climate, land use, and land management impact the occurrence and severity of wildland fires in many parts of the world. This is particularly evident in Europe, where ongoing changes in land use have strongly modified fire patterns over the last decades. Although satellite data by the European Forest Fire Information System provide large-scale wildland fire statistics across European countries, there is still a crucial need to collect and summarize in-depth local analysis and understanding of the wildland fire condition and associated challenges across Europe. This article aims to provide a general overview of the current wildland fire patterns and challenges as perceived by national representatives, supplemented by national fire statistics (2009–2018) across Europe. For each of the 31 countries included, we present a perspective authored by scientists or practitioners from each respective country, representing a wide range of disciplines and cultural backgrounds. The authors were selected from members of the COST Action “Fire and the Earth System: Science & Society” funded by the European Commission with the aim to share knowledge and improve communication about wildland fire. Where relevant, a brief overview of key studies, particular wildland fire challenges a country is facing, and an overview of notable recent fire events are also presented. Key perceived challenges included (1) the lack of consistent and detailed records for wildland fire events, within and across countries, (2) an increase in wildland fires that pose a risk to properties and human life due to high population densities and sprawl into forested regions, and (3) the view that, irrespective of changes in management, climate change is likely to increase the frequency and impact of wildland fires in the coming decades. Addressing challenge (1) will not only be valuable in advancing national and pan-European wildland fire management strategies, but also in evaluating perceptions (2) and (3) against more robust quantitative evidence.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

scholarly journals Mapping Land use/land cover using a combination of Radar Sentinel-1A and Sentinel-2A optical images

2021 ◽  
Vol 652 (1) ◽  
pp. 012021
Author(s):  
T T H Nguyen ◽  
T N Q Chau ◽  
T A Pham ◽  
T X P Tran ◽  
T H Phan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Optical Images ◽  
Sentinel 2A

scholarly journals Mapping Sub-Metre 3D Land-Sea Coral Reefscapes Using Superspectral WorldView-3 Satellite Stereoimagery

Oceans ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 315-329
Author(s):  
Antoine Collin ◽  
Mark Andel ◽  
David Lecchini ◽  
Joachim Claudet
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Sustainable Management ◽  
Near Infrared ◽  
Value Added ◽  
Land Use Land Cover ◽  
Fine Scale ◽  
Mid Infrared ◽  
Stereo Imagery ◽  
Sea Use

Shallow coral reefs ensure a wide portfolio of ecosystem services, from fish provisioning to tourism, that support more than 500 million people worldwide. The protection and sustainable management of these pivotal ecosystems require fine-scale but large-extent mapping of their 3D composition. The sub-metre spaceborne imagery can neatly produce such an expected product using multispectral stereo-imagery. We built the first 3D land-sea coral reefscape mapping using the 0.3 m superspectral WorldView-3 stereo-imagery. An array of 13 land use/land cover and sea use/sea cover habitats were classified using sea-, ground- and air-truth data. The satellite-derived topography and bathymetry reached vertical accuracies of 1.11 and 0.89 m, respectively. The value added of the eight mid-infrared (MIR) channels specific to the WorldView-3 was quantified using the classification overall accuracy (OA). With no topobathymetry, the best combination included the eight-band optical (visible + near-infrared) and the MIR8, which boosted the basic blue-green-red OA by 9.58%. The classes that most benefited from this MIR information were the land use “roof” and land cover “soil” classes. The addition of the satellite-derived topobathymetry to the optical+MIR1 produced the best full combination, increasing the basic OA by 9.73%, and reinforcing the “roof” and “soil” distinction.

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