A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

Liu, Tao; Im, Jungho; Quackenbush, Lindi J.

doi:10.1016/j.isprsjprs.2015.10.002

Cited by 71 publications

(51 citation statements)

References 58 publications

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“…This way, RF overcomes the well-known limitation of CART, in that results are sensitive to the configuration and quality of training data (Lawrence and Wright, 2001;Rhee et al, 2014;Guo and Du, 2017). Thus, RF has recently gained popularity in remote sensing classification and regression Li et al, 2014;Liu et al, 2015;Lu et al, 2013;Park et al, 2016;Yoo et al, 2012). Two approaches are generally adopted to reach a final conclusion from the independent decision trees including a simple majority voting and weighted majority voting strategy for classification, while a final value is either simply averaged from the results of the multiple regression trees or averaged with weights for regression.…”

Section: Deterministic and Probabilistic Approaches For CI Detectionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

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Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 µm T b was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6-11.2 µm)-(11.2-12.4 µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20-40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

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Section: Deterministic and Probabilistic Approaches For CI Detectionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

Self Cite

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“…Among the methods developed, segmentation (especially watershed method) applied to DSM-nDSM-CHM from active and/or passive data draws attention (e.g. Hyyppa et al, 2001;Wolf and Heipke, 2007;Kwak et al, 2007;Ene et al, 2012;Wallace, 2014;Liu et al, 2015;Paris et al, 2016). It is observed that the segmentation-based methods are generally applied together with seed generation (e.g.…”

Section: Previous Studiesmentioning

confidence: 99%

“…Some other methods, like image classification (e.g. Dalponte et al, 2014;Matsuki et al, 2015;Liu et al, 2015;Dalponte et al, 2015b), morphological analysis (e.g. Heinzel et al, 2011), wavelet analysis (e.g.…”

Section: Previous Studiesmentioning

confidence: 99%

Detection of Citrus Trees From Uav DSMS

Ozdarici-Ok

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents an automated approach to detect citrus trees from digitals surface models (DSMs) as a single source. The DSMs in this study are generated from Unmanned Aerial Vehicles (UAVs), and the proposed approach first considers the symmetric nature of the citrus trees, and it computes the orientation-based radial symmetry in an efficient way. The approach also takes into account the local maxima (LM) information to verify the output of the radial symmetry. Our contributions in this study are twofold: (i) Such an integrated approach (symmetry + LM) has not been tested to detect (citrus) trees (in orchards), and (ii) the validity of such an integrated approach has not been experienced for an input, e.g. a single DSM. Experiments are performed on five test patches. The results reveal that our approach is capable of counting most of the citrus trees without manual intervention. Comparison to the stateof-the-art reveals that the proposed approach provides notable detection performance by providing the best balance between precision and recall measures.

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“…This approach effectively reduced the over-segmentation which are caused by branches of the deciduous tree, but required the dominant tree crown sizes beforehand. Liu et al [52] developed a boundary refining program called Fishing Net Dragging (FiND) based on watershed segmentation and used a boundary classification model to merge the adjacent segments. These approaches do not rely on prior detected treetops, but require some complicated post-process procedures.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies evaluated ITCD accuracy at both plot and individual tree level (e.g., [16,34]), while others only provided accuracy assessment at individual tree level (e.g., [52]). From two-step perspective, ITCD accuracy assessment can be divided into detection accuracy and delineation accuracy assessment.…”

Section: Introductionmentioning

confidence: 99%

A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data

et al. 2017

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Abstract:In recent years, airborne Light Detection and Ranging (LiDAR) that provided three-dimensional forest information has been widely applied in forest inventory and has shown great potential in automatic individual tree crown delineation (ITCD). Usually, ITCD algorithms include treetop detection and crown boundary delineation procedures. In this study, we proposed a novel method called region-based hierarchical cross-section analysis (RHCSA), which combined the two procedures together based on a canopy height model (CHM) derived from airborne LiDAR data for ITCD. This method considers the CHM as a three-dimensional topological surface, simulates stereoscopic scanning from top to bottom using an iterative process, and utilizes the individual crown and vertical structure of crowns to progressively detect individual treetops and delineate crown boundaries. The proposed method was tested in natural forest stands with high canopy densities in Liangshui National Nature Reserve and Maoershan Forest Farm, Heilongjiang Province, China. Its performance was evaluated by an accuracy procedure that considered both the relative position of treetops and overlapped area of crowns. The average overall accuracy achieved was 85.12% for coniferous plots, 83.86% for deciduous plots and 86.44% for coniferous and broad-leaved mixed forest plots. The results revealed that the RHCSA method can detect and delineate individual tree crowns with little influence from forest types and crown size. It could provide technical support for individual tree crown delineation in coniferous, deciduous and mixed forests with high canopy densities.

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A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

Cited by 71 publications

References 58 publications

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Detection of Citrus Trees From Uav DSMS

A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data

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