Development of Desertification Indicators for Desertification Monitoring from Landsat Images Using Python Programming

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).

Trends in The Adoption of New Geospatial Technologies for Spatial Planning and Land Management in 2021

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.

Prospective Mapping of Land Cover and Land Use in The Classified Forest of The Upper Alibori Based on Satellite Imagery

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.

Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery

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.

The Spatial Model of Paddy Productivity Based on Environmental Vulnerability in Each Phase of Paddy Planting

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.

Practical Methods of GIS for Archaeologists: Viewshed Analysis – The Kingdom of Pylos example

Visibility (or Viewshed) Analysis in archeology is a function given through GIS, in purpose to contribute in the field of archaeology and especially in landscape archeology, by reconstituting the visual panorama of a study area of the past. The concept of landscape archeology is a multidimensional research process that is not limited to archaeologists but places a special emphasis on a multidisciplinary approach. Mycenaean Messenia was the area of study and analysis of the visual panorama for two important reasons. First of all, it is a large area, which presents territories of varying heterogeneity in terms of morphology, while having a large sea front and an open observation horizon. Secondly, it is one of the continental regions of the Mycenaean period, which has evoked the largest number of residential facilities, structures and tombs, and also has been extensively studied by archaeologists since the 1920s. The main aim of this paper is to make an effort to identify archaeological information, through the bibliographic references of the archaeologists who studied the area, with the GIS visibility analysis. For that reason, the author tries for those residential locations that have been assigned a role or function of the site by archaeologists, such as an observation station, to be controlled in parallel and on the basis of new technologies (GIS and Viewshed Analysis) if this view is verified.

Slum Upgrading Spatial Model Based on Level of Vulnerability to Climate Change in Coastal Area of Semarang City

Slum settlement is one of the significant global problems which requires special concern in the discussion agenda of Sustainable Development Goals (SDGs) of 2016-2030. The Sustainable Development Summit held in New York in September 2015 formulated that one of SDGs goals is to build inclusive, safe, resilient, and sustainable cities and settlements. In Indonesia, the achievement of this goal is stated in National Medium-Term Development Plan 2015-2019, i.e. creating 0% urban slum settlement which is supported by policies expected to accommodate the achievement of national development targets. Semarang Mayor Decree No. 050/801/2014 concerning the Determination of the Location of Housing Environment and Slum Settlements in Semarang City has been issued as the basis to identify slum settlements scattered throughout Semarang city, in terms of location, physical condition, and social conditions. This study was conducted by case studies on slum settlements in Trimulyo Village and Mangkang Wetan Village, Semarang city, Central Java Province, Indonesia, to formulate a slum upgrading model based on the resilience level of coastal communities towards climate change. The analysis included identifying the characteristics of slum settlements, scoring analysis to determine the resilience level possessed by coastal communities, and analysis of pentagon assets used to formulate slum upgrading models. The results of the study showed that these two research areas had a moderate level of vulnerability, with several different characteristics of asset ownership, particularly those related to human and social assets. Increasing the quality of human resources and social relations in the community was more intensified in the environment and community in Trimulyo, while improving the physical quality of the environment through housing improvements was carried out in Mangkang Wetan.

Correlation Between Settlement Environmental Quality and Acute Respiratory Infection (ARI) Disease of Gayamsari Sub-District, Semarang

Settlement area with high population density and situated in unfeasible location cause the quality of settlement environment to be low. The low quality of settlement environment has the potential to cause Acute Respiratory Infection (ARI). This study aimed to explain the correlation of settlement environmental quality and incidence of ARI disease in Gayamsari Sub-District, Semarang. This research used a quantitative descriptive spatial approach by using remote sensing technique and Geographic Information System (GIS). The result showed that there is a high and significant correlation between settlement environmental quality and acute respiratory infection (ARI) Incidence, especially happened in Kaligawe and Tambakrejo village.

Temperature Acquisition System for Real Time Application of First Velocity Correction by EDM (Electronic Distance Measurement)

The first velocity correction is used to correct the measured distance affected by the velocity variation of the electromagnetic wave propagation in a medium. This correction depends on the refractive index of the propagation medium and reference refractive index. The influence of the temperature in the medium refractive index is critical; some estimates establish that variation 1°C causes 1ppm of error in distances. In the measuring processes with total stations, the temperature is usually collected at only one point, for example, in the position where the measuring instrument is setup. However, the wave propagates in a medium of non-constant temperature, where the extremes of the line can present variations and thus this measurement in only one point could be non-representative. In this context, it was developed a low-cost real-time temperature acquisition system. This system provides the temperature values in different locations allowing their monitoring through the time. Experiments realized during the geodetic monitoring of a dam, show variations up to 8°C among geodetic points on the dam and around it. An analysis was development to evaluate the influence of temperature variations on monitoring distances and geodetic coordinate of a 2d network with different approaches (temperature modeling). The results shows different values for distances (1.0 mm) and coordinates (0.5 mm) depending of the approach choose.

Modelling Precipitable Water Vapour (PWV) Over Nigeria from Ground-Based GNSS

Global Navigational Satellite System (GNSS) over the past and present time has shown a great potential in the retrieval of the distribution of water vapour in the atmosphere. Taking the advantage of the effect of the atmosphere on GNSS signal as they travel from the constellation of satellite to ground-based GNSS receivers such that information (water vapour content) about the atmosphere (mostly from the troposphere) can be derived is referred to as GNSS meteorology. This paper presents the spatiotemporal variability of Precipitable Water Vapour (PWV) retrieved from ground–based Global Navigation Satellite System (GNSS) stations over Nigeria for the years 2012 to 2013. In this paper, the GNSS data were processed using GAMIT (ver. 10.70). The GNSS PWV were grouped into daily and monthly averages; the variability of the daily and monthly GNSS PWV were compared and validated with the daily and monthly PWV from National Centre for Environmental Prediction (NCEP) and monthly Rainfall data for the study years respectively. The results revealed that the spatiotemporal variability of PWV across Nigeria is a function of geographic location and seasons. The result shows that there is temporal correlation between GNSS PWV, NCEP PWV and rainfall events. The research also affirms that GNSS PWV could be used to improve weather forecasting/monitoring as well as climate monitoring.