Estimating Mean Tree Crown Diameter of Mangrove Stands Using Aerial Photo

Suhardiman, Ali; Tsuyuki, Satoshi; Setiawan, Yunianto

doi:10.1016/j.proenv.2016.03.092

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…The crown diameter of plants serves as a valuable indicator, reflecting important information regarding plant growth, biomass, spatial utilization, and ecological functions and holding significant relevance in agriculture, forestry, ecology, and related fields [12,39]. Additionally, it is also an important indicator for evaluating the ornamental characteristics of landscape flowers.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery

Zhang,

Lu,

Zhang

et al. 2024

Agronomy

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Crown diameter is one of the crucial indicators for evaluating the adaptability, growth quality, and ornamental value of garden chrysanthemums. To accurately obtain crown diameter, this study employed an unmanned aerial vehicle (UAV) equipped with a RGB camera to capture orthorectified canopy images of 64 varieties of garden chrysanthemums at different growth stages. Three methods, namely RGB color space, hue-saturation-value (HSV) color space, and the mask region-based convolutional neural network (Mask R-CNN), were employed to estimate the crown diameter of garden chrysanthemums. The results revealed that the Mask R-CNN exhibited the best performance in crown diameter estimation (sample number = 2409, R2 = 0.9629, RMSE = 2.2949 cm). Following closely, the HSV color space-based model exhibited strong performance (sample number = 2409, R2 = 0.9465, RMSE = 3.4073 cm). Both of the first two methods were efficient in estimating crown diameter throughout the entire growth stage. In contrast, the RGB color space-based model exhibited slightly lower performance (sample number = 1065, R2 = 0.9011, RMSE = 3.3418 cm) and was only applicable during periods when the entire plant was predominantly green. These findings provide theoretical and technical support for utilizing UAV-based imagery to estimate the crown diameter of garden chrysanthemums.

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Section: Discussionmentioning

confidence: 99%

Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery

Zhang,

Lu,

Zhang

et al. 2024

Agronomy

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“…Describing tree crown shapes and sizes from aerial imagery is a complicated task that has been tried with different methods [62]. By using instance segmentation networks on well-defined training data, the task can be seemingly simplified [41].…”

Section: Seeing the Forest For The Trees: An Inven(s)torymentioning

confidence: 99%

Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence

et al. 2023

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Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.

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“…Hutan mangrove di Delta Mahakam mengalami kerusakan yang sangat akut, diperkirakan kerusakan mencapai 85 persen atau 91,906 hektar dari total luas 108,125 hektar di Kecamatan Samboja, Kabupaten Kutai Kartanegara (Kukar) (Iskandar, 2010). Kerusakan ini hanya berdasarkan informasi dua dimensi seperti penutup lahan sedangkan terdapat cara lain yang bisa digunakan untuk mengidentifikasi perubahan secara vertikal (3 dimensi), yaitu tinggi vegetasi, diameter kanopi vegetasi (Suhardimana et al, 2016), dan geovisualisasi kawasan.…”

Section: Pendahuluanunclassified

Deteksi Tinggi Vegetasi di Delta Mahakam dengan Penginderaan Jauh

Anggraini

Julzarika

2019

OLDI

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<strong>Detection of Vegetation Height in Mahakam Delta Using Remote Sensing. </strong>The vegetation height is a vertical distance between top of the vegetation to ground surface. Vegetation height is one of the parameters for vegetation growth. There are various methods to measure vegetation height; one of them is the use of remote sensing technology. This study aims to map vegetation height in Mahakam Delta by using height models derived from remote sensing data. Such models are Digital Surface Model (DSM) and Digital Terrain Model (DTM). DSM was generated using a combination of interferometric processing of ALOS PALSAR interferometry, X-SAR, Shuttle Radar Topography Mission (SRTM), and geodetic height of Icesat/GLAS satellite imagery. This integration technique incorporated the Digital Elevation Model (DEM) method. The geoid model used in this study was EGM 2008. The following step was the correction of height errors of DSM. Terrain correction was undertaken to convert DSM into DTM, while vegetation heights were obtained from subtraction of DSM and DTM. Vertical accuracy verification refers to a tolerance of 1.96σ (95%) or ~80 cm. In DSM, a vertical accuracy value of 60.4 cm was obtained so that the DSM is feasible for mapping with scale of 1: 10,000, while the DTM was 37 cm so it is also applicable for mapping with such scale. Based on the subtraction of DSM and DTM, the vegetation heights in Mahakam Delta varied between 0 and 64 m.

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Estimating Mean Tree Crown Diameter of Mangrove Stands Using Aerial Photo

Cited by 10 publications

References 17 publications

Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery

Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery

Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence

Deteksi Tinggi Vegetasi di Delta Mahakam dengan Penginderaan Jauh

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