Keywords :
Land Degradation; Savanna; 2016 drought in Thailand; ISODATA clustering; Vegetative Succession; NDVI; NDWI
บทคัดย่อ :
In forest restoration projects, assessing progress is critically important. This study was conducted
to quantify the restoration progress for approximately 30 year old plantation plots of Acacia and
Pterocarpus in Sakaerat, Thailand. For 22.5 x 22.5 m areas in the plantation plots, as a continuous
measure of forest restoration, dry evergreen forest-likelihood was determined using 45 cm
resolution satellite images and an artificial neural network architecture. The Acacia plot achieved a
moderate mean evergreen forest likelihood of 0.451 (April 2016) to 0.679 (May 2019), but the
values for the Pterocarpus plot were 0.076 or smaller. Two other Pterocarpus plots at cooler and
moister sites had mean evergreen forest-likelihood values of up to 0.884 (April 2016), which were
significantly greater than that (0.451) for the Acacia plot, but the values dropped in May 2019.
Throughout the period of the four image acquisition times from March 2014 to May 2019, the
plantation plots had significantly smaller evergreen forest-likelihood than the dry evergreen forest
did. The current approach would be a helpful option for stakeholders of forest management by
applying sub-m-resolution images and machine learning followed by quantification of the forest
restoration effects relying on the appearance and texture of tree canopies at multiple data
acquisition times.
เอกสารอ้างอิง :
Doi, R. (2021). Assessing the reforestation effects of plantation plots in the Thai savanna based on 45 cm resolution true-color images and machine learning. Environmental Research Letters, 16(1), 014030.
