PL EN
Monitoring Vegetation Change Using Forest Cover Density Model
 
More details
Hide details
1
Physics Study Program, Mathematics and Natural Sciences Faculty, Universitas Lambung Mangkurat. Jl. Ahmad Yani Km. 36 Banjarbaru, Kalimantan Selatan, Indonesia
 
 
Corresponding author
Nurlina Nurlina   

Physics Study Program, Mathematics and Natural Sciences Faculty, Universitas Lambung Mangkurat. Jl. Ahmad Yani Km. 36 Banjarbaru, Kalimantan Selatan, Indonesia
 
 
Ecol. Eng. Environ. Technol. 2024; 9
 
KEYWORDS
TOPICS
ABSTRACT
The regional ecological environment has become a significant subject of study due to the dynamics of the ecosystem, which is represented by vegetation, under the influence of human activities. The objective of this research is to demonstrate the implementation and effectiveness of the Forest Canopy Density (FCD) model in generating a map that illustrates changes in forest canopy density using multitemporal remote sensing data in Tabunio watershed. The methodology relies on vegetation index, including the Normalized Difference Vegetation Index (NDVI), shadow index (SI), and bare soil index (BI), to generate a composite vegetation index (CVI). FCD uses multitemporal remote sensing data from Landsat TM images from 2005 to 2020, which have been utilized to accomplish multi-source categorization. The findings indicated that the vegetation coverage of the Tabunio watershed presented a predominant pattern of high coverage in the northeastern and eastern regions, whereas most areas of the western region had low coverage; (2) vegetation cover from 2005 to 2020 is dominated by sparse to very dense vegetation cover classes; (3) changes in vegetation cover over two decades are very significant. The expansion of plantation land in 2005 caused a lot of non-vegetated land, which gradually changed in the following year period along with plant growth. At the end of 2020, the percentage of very dense vegetation became increasingly dominant, which was around 42 percent. The results of the study indicate the three biophysical index (NDVI, SI, and BI) used in this model approach were appropriate for precisely discriminating across all canopy density classes, as seen by the overall producer's accuracy of 81.3%. FCD model in multitemporal data can help in the early identification of deforestation or forest degradation activities. Furthermore, the FCD model may have certain constraints, as it requires an understanding of ground conditions to establish threshold values for each class.
Journals System - logo
Scroll to top