Crown closure segmentation on wetland lowland forest using the mean shift algorithm

Abstract

The availability of high and very high-resolution imagery is helpful for forest inventory, particularly to measure the stand variables such as canopy dimensions, canopy density, and crown closure. This paper describes the examination of mean shift (MS) algorithm on wetland lowland forest. The study objective was to find the optimal parameters for crown closure segmentation Pleiades-1B and SPOT-6 imageries. The study shows that the segmentation of crown closure with the red band of Pleiades-1B image would be well segmented by using the parameter combination of (hs: 6, hr: 5, M: 33) having overall accuracy of 88.93% and Kappa accuracy of 73.76%, while the red, green, blue (RGB) composite of SPOT-6 image, the optimal parameter combination was (hs:2, hr: 8, M: 11), having overall accuracy of 85.72% and kappa accuracy of 68.33%. The Pleiades-1B image with a spatial resolution of (0.5 m) provides better accuracy than SPOT-5 of (1.5 m) spatial resolution. The differences between single spectral, synthetic, and RGB does not significantly affect the accuracy of segmentation. The study concluded that the segmentation of high and very high-resolution images gives promising results on forest inventory.

Analisis Tinggi Terbang Unmanned Aerial Vehicle Terhadap Klasifikasi Tutupan Lahan Menggunakan Object Based Image Analysis

“…Penggunaan skala segmentasi yang sama juga dapat membuat hasil segmentasi antar ketinggian terbang drone pada kelas tutupan lahan hutan ini menjadi tidak terlihat. [13] menyatakan bahwa kondisi segmentasi pada tajuk yang homogen memberikan akurasi yang lebih baik.…”

“…Untuk hutan campuran meranti dan jenis kayu daun lebar lainnya dicirikan dengan rona agak gelap dan warna yang dilihat yaitu perpaduan warna hijau tua dan hijau kekuningan dengan tajuk membulat.Vegetasi hutan alam yang heterogen membuat ketajaman segmentasi pada setiap beda tinggi terbang UAS tidak terlalu terlihat. Penggunaan skala segmentasi yang sama juga dapat membuat hasil segmentasi antar ketinggian terbang drone pada kelas tutupan lahan hutan ini menjadi tidak terlihat [13]. menyatakan bahwa kondisi segmentasi pada tajuk yang homogen memberikan akurasi yang lebih baik.…”

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

“…3, December 2021: 1499 -1514 1500 pan-sharpened image with a combination of the false color composites which consist of near-infrared (NIR), red, and green channels. Subsequently, the contrasts of the SPOT-5 images are improved by integrating the near infrared histogram equalization (NIR-HE) method for features preservation and edge detection [23], [33], [37]- [40].…”

“…The goal of NIR-HE using the NIR channel is to improve the image contrast and hence to make it suitable for classification of vegetation in feature extraction [2], [28], [33], [38], [40], [48], [52]- [55]. The false color composite image is enhanced using HE algorithm.…”