Version 1
: Received: 29 August 2019 / Approved: 2 September 2019 / Online: 2 September 2019 (04:51:15 CEST)
How to cite:
Osei, J.D.; Andam-Akorful, S.A.; Osei jnr, E.M. Long Term Monitoring of Ghana’s Forest Reserves Using Google Earth Engine. Preprints2019, 2019090016. https://doi.org/10.20944/preprints201909.0016.v1
Osei, J.D.; Andam-Akorful, S.A.; Osei jnr, E.M. Long Term Monitoring of Ghana’s Forest Reserves Using Google Earth Engine. Preprints 2019, 2019090016. https://doi.org/10.20944/preprints201909.0016.v1
Osei, J.D.; Andam-Akorful, S.A.; Osei jnr, E.M. Long Term Monitoring of Ghana’s Forest Reserves Using Google Earth Engine. Preprints2019, 2019090016. https://doi.org/10.20944/preprints201909.0016.v1
APA Style
Osei, J.D., Andam-Akorful, S.A., & Osei jnr, E.M. (2019). Long Term Monitoring of Ghana’s Forest Reserves Using Google Earth Engine. Preprints. https://doi.org/10.20944/preprints201909.0016.v1
Chicago/Turabian Style
Osei, J.D., S. A. Andam-Akorful and Edward Matthew Osei jnr. 2019 "Long Term Monitoring of Ghana’s Forest Reserves Using Google Earth Engine" Preprints. https://doi.org/10.20944/preprints201909.0016.v1
Abstract
Farm activities continued sand winning operations and the allocation of plots of land to prospective developers in Ghana pose a serious threat to the forest covers and lifespan of the Forest and game reserves. With all the positive add ups to the country from forests, Ghana has lost more than 33.7%(equivalent to 2,500,000 hectares) of its forest, since the early 1990s between 2005 and 2010, the rate of deforestation in Ghana was estimated at 2.19% per annum; the sixth highest deforestation rate globally for that period. This shows how important forest monitoring can be to the forestry commission in Ghana. Despite the frameworks which have been developed to help Ghana to protect and restore its forest resources, inadequate monitoring systems remain a barrier to effective implementation. In this study, Google earth engine was used to map and analyze the structural changes of forest cover using JavaScript to query and compute Landsat, MODIS and NOAA AVHRR satellite imageries of the study area (Ghana) with spatial resolutions 30m, 250m and 7km respectively. A supervised classification was performed on three multi-temporal satellite imageries and a total of six major land use and land cover classes were identified and mapped. By using random Forest-classification technique, from 1985 to 2018 recorded by NOAA AVHRR, forest cover has decreased by 66% and 2000 to 2018 recorded by Landsat and MODIS 61% and 47% respectively. A decrease in the forest has been as a result of anthropogenic activities in Ghana. A change detection analysis was performed on these images and it was noted that Ghana is losing forest reserves in every 5years. Overlay of the reserved forest of the 2000 and the classified map of 2018 shows vegetation changed during 2000-2018 remarkably. Therefore, forest-related institutions like the Forestry Commission can employ and use this monitoring system on Google Earth Engine for processing satellite images particularly Landsat, MODIS and NOAA AVHRR for forest cover monitoring and analysis for fast, efficient and reliable results.
Keywords
land cover; classification Spatial and temporal Analysis; forest cover; Google Earth Engine (GEE); MODIS; Landsat; NOAA AVHRR
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
