A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

Abstract

Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

Multidecadal Trend Analysis of Armenian Mountainous Grassland and Its Relationship to Climate Change Using Multisensor NDVI Time-Series

“…Analyses of previous studies demonstrated that there was a similar performance of MODIS and Landsat data [69], [70]. Therefore, we can use the low spatial resolution MODIS data to study relationships between NDVI and climate factors [71].…”

“…The goal of this research was to study the time-lag effects of the vegetation responses to climate variables in the mountain grassland ecosystems during 1984-2018 using MODIS-, Landsat-derived NDVI data with BRDF, topographic and atmospheric corrections applied, temperature and precipitation data from meteorological stations and Google Earth Engine (GEE) cloud platform [122]. GEE is a cloud-based geospatial analysis platform for scientific analysis and visualization of geospatial datasets; it enables processing of satellite imagery to detect changes, which has been widely used in similar studies in recent years [37], [71], [123].…”

Multidecadal Trend Analysis of Armenian Mountainous Grassland and Its Relationship to Climate Change Using Multi-Sensor NDVI Time-Series

“…Analyses of previous studies demonstrated that there was a similar performance of MODIS and LANDSAT data [59,64,67,68]. Therefore, we can use the low spatial resolution MODIS data to study relationships between NDVI and climate factors [69].…”

“…The goal of this research was to study the time-lag effects of the vegetation responses to climate variables in the mountain grassland ecosystems during 1984-2018 using MODISand LANDSAT-derived NDVI data with BRDF, with topographic and atmospheric corrections applied, and temperature and precipitation data from meteorological stations and the Google Earth Engine (GEE) cloud platform [122]. GEE is a cloud-based geospatial analysis platform for scientific analysis and visualization of geospatial datasets; it enables processing of satellite imagery to detect changes, which has been widely used in similar studies in recent years [34,69,123].…”

Analysis of Carbon Monoxide Relation with NDVI and Land Cover in Spatio-Temporal Perspective

“…From 2009-2011, deforestation in Java consisted of 500 Ha of conservation forest, 800 Ha of protected forest, 4,100 Ha of limited production forest, and 3,500 Ha of production forest. From the side of NDVI, Java is getting down the NDVI year by year with a -0.00047 rate [10].…”