Penerapan Metode Spatio-Temporal Analysis dalam Analisis Dinamika Tutupan dan Penggunaan Lahan Berbasis NDVI dan NDWI - Yerik Afrianto Singgalen (KLIK: Kajian Ilmiah Informatika dan Komputer, 2023)
Landscape changes brought on by converting land from community plantations to industrial areas must be continuously monitored for spatial data documentation. This study proposes documenting changes in the environment of coconut farms in Indonesia's North Maluku Province's Gulo Village, North Kao District, and North Halmahera Regency. The spatio-temporal analysis method was employed in this investigation. The results of this study show that the spatio-temporal analysis method is very relevant to be used in identifying and analyzing the dynamics of land cover and use in buffer zones and industrial zones through the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)  models. According to Landsat 8 OLI satellite image data based on 2018, 2019, and 2021, the land cover and use in the buffer zone, particularly in Gulo Village and Dowongimaiti Village, suggest agricultural activity and deep coconut plantations. The Landsat 8 OLI satellite image data calculation in August 2018 was 0.39 based on vegetation index data in the Gulo Village buffer zone. This value declined to 0.35 in September 2019 and increased to 0.43 in September 2021. In addition, changes in the vegetation index value of 0.39 in August 2018 were discovered based on data from the Dowongimaiti Village buffer zone, which declined to 0.38 in September 2019 and increased to 0.44 in September 2021. The NDWI model's calculation outcomes in industrial zones reveal water features and puddles with an average index value of -0.33. As a result, as a proactive and strategic measure to reduce the risk of disaster brought on by industrial activities, the output of the spatio-temporal analysis method can be used to optimize the function of supervision or control of environmental conditions in the form of appropriate program recommendations.