A novel approach to mapping land conversion using Google Earth with an application to East Africa

Effective conservation planning relies on the accurate identification of anthropogenic land cover. However, accessing localized information can be difficult or impossible in developing countries. Additionally, global medium-resolution land use land cover datasets may be insufficient for conservation planning purposes at the scale of a country or smaller. We thus introduce a new tool, GE Grids, to bridge this gap. This tool creates an interactive user-specified binary grid laid over Google Earth's high-resolution imagery. Using GE Grids, we manually identified anthropogenic land conversion across East Africa and compared this against available land cover datasets. Nearly 30% of East Africa is converted to anthropogenic land cover. The two highest-resolution comparative datasets have the greatest agreement with our own at the regional extent, despite having as low as 44% agreement at the country level. We achieved 83% consistency among users. GE Grids is intended to complement existing remote sensing datasets at local scales

Virtual Globes for UAV-based data integration: Sputnik GIS and Google Earth™ applications

“This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Digital Earth on 03 May 2018, available online: https://www.tandfonline.com/doi/abs/10.1080/17538947.2018.1470205"The integration of local measurements and monitoring via global-scale Earth observations has become a new challenge in digital Earth science. The increasing accessibility and ease of use of virtual globes (VGs) represent primary advantages of this integration, and the digital Earth scientific community has adopted this technology as one of the main methods for disseminating the results of scientific studies. In this study, the best VG software for the dissemination and analysis of high-resolution UAV (Unmanned Aerial Vehicle) data is identified for global and continuous geographic scope support. The VGs Google Earth and Sputnik Geographic Information System (GIS) are selected and compared for this purpose. Google Earth is a free platform and one of the most widely used VGs, and one of its best features its ability to provide users with quality visual results. The proprietary software Sputnik GIS more closely approximates the analytical capacity of a traditional GIS and provides outstanding advantages, such as DEM overlapping and visualization for its disseminationThis work was supported by Xunta de Galicia under the Grant “Financial aid for the consolidation and structure of competitive units of investigation in the universities of the University Galician System (2016-18)” (Ref. ED431B 2016/030 and Ref. ED341D R2016/023). The authors also acknowledge support provided by “Realización de vuelos virtuales en las parcelas del proyecto Green deserts LIFE09 / ENV/ES / 000447”S

EVALUATION OF OPTIMAL ECOLOGICAL TOURISM ROUTES OBTAINED VIA GOOGLE EARTH SOFTWARE IN AN ENVIRONMENTAL PROTECTION AREA

Ecological tourism stands out as an economic activity that can be reconciled with nature conservation. Such activities have been carried out in several conservation units in Brazil. However, for satisfactory implementation, it is necessary to effectively plan the activities to be conducted, places to be visited, and paths to reach those places. In this context, we aimed to assess whether Google Earth software can assist in the expansion of the plan to use these areas for ecotourism, especially the routes that may be taken by tourists within the conservation unit. For this purpose, tourist interest sites were defined in the State Environmental Protection Area of Rio Pandeiros in the northern region of the state of Minas Gerais. The shortest routes between the pairs of locations obtained using Google Earth and QGIS software were evaluated. Additionally, it was necessary to perform vectorization and classification of all roads in the area. The lengths of the paths obtained were compared using a method identity test. The results showed that for most routes, Google Earth showed longer paths than QGIS. It can be concluded that for the purpose of ecotourism, precise planning should consider the vectorization of roads in areas with many rural roads

Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping

In the classification of images for land cover and land use mapping, several methods can be applied, however, they can present different results in relation to field truth. Therefore, the objective of this work was to test techniques for classifying high spatial digital images obtained from the Google Earth Pro® platform. The images refer to a section of the Federal University of Goias, campus Samambaia Goiania - GO, Brazil. Classification tests were performed on the images obtained, using two classifiers per region and two classifiers per pixel, both available free of charge, in the Spring software of the National Institute for Space Research (INPE / Brazil). For the analysis of the quality of the classifications, the results were compared to a survey by direct method, in this case the topographic one, seeking to identify which classifier came closest to the field truth. The classification that presented the best performance and class separability was the Bhattacharya, with Global Accuracy of 0.85. Bhattacharya was also the classifier that came closest in terms of measured areas, by the topographic survey, with the areas of the “zinc roofing” use class, analyzed and calculated

SURFACE WATER DYNAMICS OF INLAND WATER BODIES OF INDIA USING GOOGLE EARTH ENGINE

Dynamics, distribution and quality of water has a direct impact on environment and its dependent human activities. Regular monitoring of these hydrological processes help in understanding water cycle and better management policy making. Recent increase in remote sensing satellites offer multiple observations with high spatial and temporal resolution, thus calling for extensive use of high end computational resources. Google Earth Engine(GEE) is an open Application Programing Interface (API), which offers free computational resources and satellite data on cloud computational platform minimising the users need for computational resources and data availability. Five year Landsat-8 imagery (2013–18) from GEE database has been used to study the surface water extent of large inland water bodies (surface area greater than 6000 ha) of India. We have used a pixel based classification system to delineate water and non-water pixels. A knowledge based Decision Tree (DT) model has been employed to cluster the classes according to Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) distribution. We report an anomalous departure from the 5-year trend line suggesting that the maximum decrease of water extent was found in year 2015–2016. Analysis of the decay pattern of reservoirs can provide timely inputs for better policy making and management of water resources. To understand the decay pattern, a Modified Gaussian model fit on time series of surface extent helps to determine maximum water extent, peak extent day and storage cycle of the water body

The Dynamics and Factors Influencing The Land Use/Land Cover Change in Ciparay District, Bandung Regency

Population growth in Ciparay District, as in several areas in West Java Province, has been increasing, especially in the last ten years, leading to an increase in housing needs and converting rice fields to residential land. This study aims to identify the spatial conversion dynamic of rice fields and analyze the factors that influence the conversion of rice fields in Ciparay District, Bandung Regency. The method used in this research is a quantitative descriptive method with map overlay analysis techniques and multiple linear regression analysis. This study found that the characteristics of land conversion in Ciparay District are divided into four land use/ land cover changes: rice field to residential land, rice field to dryland farming, rice field to vacant land, and dryland agricultural to settlement. The most significant change in land use/ land cover is in the change of rice fields to residential areas, which is 336.09 hectares. The factor that had the most influence on the land conversion of rice fields was the accessibility ratio

PLANNING AND CONSERVATION OF URBAN RIPARIAN RESERVES: SPATIOTEMPORAL EVIDENCE FROM EARTH OBSERVATION

Context and backgroundAlthough a growing body of literature maintains that riparian reserves are infrequently conserved, a knowledge gap exists on how their status may be assessed through a triangulation of spatial and temporal approaches.Goal and Objectives:This study, therefore, sought to investigate through geospatial analysis the extent to which riparian reserves in Kenya were conserved as provided for under the legislation. The study area covered sections of Rivers Nyakomisaro and Nyanchwa which flows through Kisii town.Methodology:A case study research design was adopted to query the extent to which the standard that regulates riparian reserves was complied with along the two rivers. Geospatial data were collected using Google Earth images for the years 2005, 2011 and 2021 and analyzed using GIS.Results:Results showed that in 2005, 26% of the riparian reserves had been intruded by eucalyptus trees which are known to deplete water resources. This correspondingly increased to 28% and 29% in 2011 and 2021. Land under cultivation and short vegetation in 2005 covered 53%, 51% in 2011 and declined to 41% in 2021. Built-up land covered 21% in 2005 but increased to 22% in 2011 and 29% in 2021. Built-up land recorded the highest intrusion into the riparian reserves as evidenced by an increase of 8% between 2005 and 2021. The study concludes that intrusion into the riparian reserves in the study area continues unabated due to insufficient development control, the absence of a common legislative framework, and a lack of monitoring to determine the magnitude of land-use change that is shaping the riparian reserves.

OPEN FORIS AND GOOGLE EARTH ENGINE LINKING EXPERT PARTICIPATION WITH NATURAL RESOURCE MAPPING AND REMOTE SENSING TRAINING IN TANZANIA

There is an increasing amount of open Earth observation (EO) data available, offering solutions to map, assess and monitor natural resources and to obtain answers to global and local societal challenges. With the help of free and open source software (FOSS) and open access cloud computing resources, the remote sensing community can take the full advantage of these vast geospatial data repositories. To empower developing societies, support should be given to higher education institutions (HEIs) to train professionals in using the open data, software and tools. In this paper, we describe a participatory mapping methodology, which utilizes open source software Open Foris and QGIS, various open Earth observation data catalogues, and computing capacity of the free Google Earth Engine cloud platform. Using this methodology, we arranged a collaborative data collection event, Mapathon, in Tanzania, followed by a training of the related FOSS tools for HEIs’ teaching staff. We collected feedback from the Mapathon participants about their learning experiences and from teachers about the usability of the methodology in remote sensing training in Tanzania. Based on our experiences and the received feedback, using a participatory mapping campaign as a training method can offer effective learning about environmental remote sensing through a real-world example, as well as networking and knowledge sharing possibilities for the participating group