Indirect Measurement of Forest Canopy Temperature by Handheld Thermal Infrared Imager through Upward Observation

Su, Anni; Qi, Jianbo; Huang, Huaguo

doi:10.3390/rs12213559

Cited by 7 publications

(8 citation statements)

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“…Above‐canopy Structure from Motion photogrammetry, from which it is now possible to estimate LAI profiles (Lin et al ., 2021 ), could be complemented with within‐canopy spherical photogrammetry (Fangi & Nardinocchi, 2013 ). Finally, a recent study showed the potential of upward‐looking thermal imaging to obtain canopy temperatures, which relate to transpiration (Su et al ., 2020 ). A UAS thermal approach could be developed, with the caveat that complementary visible imagery should be used for binarization.…”

Section: Discussionmentioning

confidence: 99%

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

et al. 2022

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The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits.We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography.When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy.Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.

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Section: Discussionmentioning

confidence: 99%

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

et al. 2022

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“…Stand density, namely the number of trees per hectare, is an important factor for evaluating site productivity and affecting the growth and development of trees, as well as the main factor affecting the structure and function of forest ecosystems; 9 , 10 it has irreplaceable practical significance for the sustainable development of forestry and the stability of forest ecosystems. At the same time, crown width is one of the most essential stand characteristics, 11 and acquiring precise crown data is useful for estimating the forest stock and biomass 12 , 13 . Traditional tree species survey methods rely mostly on field surveys or manual assessment utilizing large-scale aerial pictures 14 , 15 .…”

Section: Introductionmentioning

confidence: 99%

“…Using UAVs to carry high-resolution cameras, 18 LiDAR, 19 thermal infrared, 12 hyperspectrometers, 20 , 21 and other sensors to collect forest remote sensing data has become popular in recent years. LiDAR is an advanced active remote sensing technology that can quickly obtain the height information and three-dimensional structure information of surface targets with high precision, and it has the advantages of strong anti-interference ability and good low-altitude detection performance 22 , 23 .…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, crown width is one of the most essential stand characteristics, 11 and acquiring precise crown data is useful for estimating the forest stock and biomass. 12,13 Traditional tree species survey methods rely mostly on field surveys or manual assessment utilizing largescale aerial pictures. 14,15 These methods are costly, expensive, and inefficient, and they cannot meet the needs of contemporary forestry development.…”

Section: Introductionmentioning

confidence: 99%

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Multispecies individual tree crown extraction and classification based on BlendMask and high-resolution UAV images

Zhou

Chen

et al. 2023

J. Appl. Rem. Sens.

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Accurate detection and segmentation of individual trees from unmanned aerial vehicle images is critical for forestry resource surveys and accurate forest management. Deep learning methods have been used for studies of individual tree crown segmentation, classification, and number of trees in mixed coniferous and broad-leaved forests, but the accuracy needs to be improved. Therefore, this study uses BlendMask, a simpler and more efficient algorithm that combines Mask R-CNN and Yolact algorithms to effectively combine instance-level information with semantic information at a finer granularity level, greatly improving crown segmentation accuracy and classification results. Three coniferous species and five broad-leaved species unmanned aerial vehicle images collected from the Jing Yue multispecies ecological forestry site in Changping District, Beijing, were used as the dataset, and the results were compared with Yolact and Mask R-CNN. The results show that the method described in this work has the highest Kappa coefficient (0.89) and overall accuracy (92.14%) in the test set. For segmentation accuracy, coniferous species' producer's accuracy was 0.91 to 0.95, whereas that of broadleaved species was 0.89 to 0.92. For species classification, the F1-score and mean average precision for coniferous species were greater than 91%, whereas those for broad-leaved species were 77.64% to 85.63%. The accuracy of extracting stand density in low and medium canopy density stands was 0.9909 and 0.9422, respectively, whereas that in high canopy density stands was 0.8913. This study shows that the BlendMask model has a good effect in studying the classification of multiple tree species, the segmentation of individual tree crowns, and the statistics of the number of trees in complex forest areas. Compared with broad-leaved forests and high canopy density stands, this model is more suitable for coniferous forest and medium and low canopy density stand scenarios. This study provides an important tool for obtaining more accurate species classification, canopy segmentation, and resource inventory results in complex forest areas.

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“…These methods have been utilized to map stand characteristics, for instance, species composition [18], biomass [19] and canopy biochemical in individual tree species by using RGB images and CHM [20,21]. As the above methods only provide color and texture features among pixels but no attention to semantic information in this content, it is still difficult to simultaneously segment the individual tree crown and discriminate species attributes at a multi-species environment [22]. In this case, deep learning (DL) and various convolutional neural networks (CNN) give a novel idea in dealing with the segmentation and classification from the multi-species individual trees [1,23].…”

Section: Introductionmentioning

confidence: 99%

Multi-Species Individual Tree Segmentation and Identification Based on Improved Mask R-CNN and UAV Imagery in Mixed Forests

et al. 2022

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High-resolution UAV imagery paired with a convolutional neural network approach offers significant advantages in accurately measuring forestry ecosystems. Despite numerous studies existing for individual tree crown delineation, species classification, and quantity detection, the comprehensive situation in performing the above tasks simultaneously has rarely been explored, especially in mixed forests. In this study, we propose a new method for individual tree segmentation and identification based on the improved Mask R-CNN. For the optimized network, the fusion type in the feature pyramid network is modified from down-top to top-down to shorten the feature acquisition path among the different levels. Meanwhile, a boundary-weighted loss module is introduced to the cross-entropy loss function Lmask to refine the target loss. All geometric parameters (contour, the center of gravity and area) associated with canopies ultimately are extracted from the mask by a boundary segmentation algorithm. The results showed that F1-score and mAP for coniferous species were higher than 90%, and that of broadleaf species were located between 75%–85.44%. The producer’s accuracy of coniferous forests was distributed between 0.8–0.95 and that of broadleaf ranged in 0.87–0.93; user’s accuracy of coniferous was distributed between 0.81–0.84 and that of broadleaf ranged in 0.71–0.76. The total number of trees predicted was 50,041 for the entire study area, with an overall error of 5.11%. The method under study is compared with other networks including U-net and YOLOv3. Results in this study show that the improved Mask R-CNN has more advantages in broadleaf canopy segmentation and number detection.

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Indirect Measurement of Forest Canopy Temperature by Handheld Thermal Infrared Imager through Upward Observation

Cited by 7 publications

References 50 publications

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

Multispecies individual tree crown extraction and classification based on BlendMask and high-resolution UAV images

Multi-Species Individual Tree Segmentation and Identification Based on Improved Mask R-CNN and UAV Imagery in Mixed Forests

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