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Geoplanning : Journal of Geomatics and Planning
Number of Followers: 4  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2355-6544
Published by Diponegoro University Homepage  [18 journals]
  • Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and

    • Authors: Nurwita Mustika Sari, R. Rokhmatuloh, Masita Dwi Mandini Manessa
      Pages: 75 - 84
      Abstract: The existence of vegetation in an area has an important role to maintain the carrying capacity of the environment and create a comfortable environment as a place to live. In an effort to create a sustainable environment, there are various pressures on vegetation that cause a decrease in vegetation area. Economic activity, population growth and other anthropogenic activities trigger the dynamics of vegetation cover in an area that causes land cover changes from vegetation to non-vegetation. Majalengka Regency as one of the areas with intensive regional physical development in line with the operation of BIJB Kertajati and the Cipali toll road became the study area in this research. This study aims to monitor the dynamics of vegetation cover with the proposed method namely the integration of the OBIA and Random Forest classifier using multi temporal Sentinel-2 satellite imagery. The results show that there is a decrease in the area of vegetation in the research area as much as 4,329.6 hectares to non-vegetation areas in the period 2016-2020. The vegetation area in 2020 is 84,716.07 hectares and non-vegetation area is 35,708 hectares. Thus, there has been a decrease in the percentage of vegetation area from 73.94% in 2016 to 70.35% in 2020, meanwhile for non-vegetation areas there has been an increase from 26.06% in 2016 to 29.65% in 2020.
      PubDate: 2021-12-30
      DOI: 10.14710/geoplanning.8.2.75-84
      Issue No: Vol. 8, No. 2 (2021)
  • Trends in The Adoption of New Geospatial Technologies for Spatial Planning
           and Land Management in 2021

    • Authors: Walter Timo de Vries
      Pages: 85 - 98
      Abstract: Changes in spatial planning and land management practices, regulations and operations have frequently relied on the uptake of innovations in geospatial technologies. This article reviews which ones the spatial planning and land management domains has effectively adopted and which new ones might potentially disrupt the domain in the near future of 2021 and beyond. Based on an extensive concept-centric trends synthesis and meta-review, the analysis demonstrates that whilst geospatial technologies are clearly gaining wider societal recognition and while private companies are indeed developing promising applications, its adoption in office work of public officials and public decision makers remains almost as limited as before. The potentially most disruptive technologies for the domain are however BIM, Block chain and Machine learning.
      PubDate: 2021-12-30
      DOI: 10.14710/geoplanning.8.2.85-98
      Issue No: Vol. 8, No. 2 (2021)
  • Development of Desertification Indicators for Desertification Monitoring
           from Landsat Images Using Python Programming

    • Authors: Lamyaa Gamal EL-Deen Taha, Manar A. Basheer, Amany Morsi Mohamed
      Pages: 99 - 114
      Abstract: Nowadays, desertification is one of the most serious environment socioeconomic issues and sand dune advances are a major threat that causes desertification. Wadi El-Rayan is one of the areas facing severe dune migration. Therefore, it's important to monitor desertification and study sand dune migration in this area. Image differencing for the years 2000 (Landsat ETM+) and 2019 (OLI images) and Bi-temporal layer stacking was performed. It was found that image differencing is a superior method to get changes of the study area compared to the visual method (Bi-temporal layer stacking). This research develops a quantitative technique for desertification assessment by developing indicators using Landsat images. Spatial distribution of the movement of sand dunes using some spectral indices (NDVI, BSI, LDI, and LST) was studied and a Python script was developed to calculate these indices. The results show that NDVI and BSI indices are the best indices in the identification and detection of vegetation. It was found that mobile sand dunes on the southern side of the lower Wadi El-Rayan Lake caused filling up of large part of the lower lake. The indices results show that sand movement decreased the size of the lower Wadi El-Rayan Lake and there are reclamation activities in the west of the lower lake. The results show that a good result could be achieved from the developed codes compared to ready-made software (ENVI 5).
      PubDate: 2021-12-30
      DOI: 10.14710/geoplanning.8.2.99-114
      Issue No: Vol. 8, No. 2 (2021)
  • Prospective Mapping of Land Cover and Land Use in The Classified Forest of
           The Upper Alibori Based on Satellite Imagery

    • Authors: Dramane Issiako, Ousséni Arouna, Karimou Soufiyanou, Ismaila Toko Imorou, Brice Tente
      Pages: 115 - 126
      Abstract: The dynamics of land cover and land use in the classified forest of the upper Alibori (FCAS) in relation to the disturbance of agro-pastoral activities is a major issue in the rational management of forest resources. The objective of this research is to simulate the evolutionary trend of land cover and land use in the FCAS by 2069 based on satellite images. Landsat images from 2009, 2014 and 2019 obtained from the earthexplorer-usgs archive were used. The methods used are diachronic mapping and spatial forecasting based on senarii. The MOLUSCE module available under QGIS remote sensing 2.18.2 is used to simulate the future evolution of land cover and land use in the FCAS. The land cover and use in the year 2069 is simulated using cellular automata based on the scenarios. The results show that natural land cover units have decreased while anthropogenic formations have increased between 2009 and 2014 and between 2014 and 2019. Under the "absence multi-criteria zoning (MZM)" scenario over a 50-year interval, land cover and use will be dominated by crop-fallow mosaics (88%). On the other hand, the scenario "implementation of a multicriteria zoning (MZE)", was issued with the aim of reversing the regressive trend of vegetation types by making a rational and sustainable management of resources.
      PubDate: 2021-12-30
      DOI: 10.14710/geoplanning.8.2.115-126
      Issue No: Vol. 8, No. 2 (2021)
  • The Spatial Model of Paddy Productivity Based on Environmental
           Vulnerability in Each Phase of Paddy Planting

    • Authors: Rahmatia Susanti, S. Supriatna, R. Rokhmatuloh, Masita Dwi Mandini Manessa, Aris Poniman, Yoniar Hufan Ramadhani
      Pages: 127 - 136
      Abstract: The national primary always growth and increase in line with the increase in population, such as the rise of rice consumption in Indonesia.  Paddy productivity influenced by the physical condition of the land and the declining of those factors can detected from the environmental vulnerability parameters. Purpose of this study was to compile a spatial model of paddy productivity based on environmental vulnerability in each planting phase using the remote sensing and GIS technology approaches. This spatial model is compiled based on the results of the application of two models, namely spatial model of paddy planting phase and paddy productivity. The spatial model of paddy planting phase obtained from the analysis of vegetation index from Sentinel-2A imagery using the random forest classification model. The variables for building the spatial model of the paddy planting phase are a combination of NDVI vegetation index, EVI, SAVI, NDWI, and time variables. The overall accuracy of the paddy planting phase model is 0.92 which divides the paddy planting phase into the initial phase of planting, vegetative phase, generative phase, and fallow phase. The paddy productivity model obtained from environmental vulnerability analysis with GIS using the linear regression method. The variables used are environmental vulnerability variables which consist of hazards from floods, droughts, landslides, and rainfall. Estimation of paddy productivity based on the influence of environmental vulnerability has the best accuracy done at the vegetative phase of 0.63 and the generative phase of 0.61 while in the initial phase of planting cannot be used because it has a weak relationship with an accuracy of 0.35.
      PubDate: 2021-12-30
      DOI: 10.14710/geoplanning.8.2.127-136
      Issue No: Vol. 8, No. 2 (2021)
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