edit

_posts/2024-02-15-blog-paper-002.md

@@ -10,7 +10,7 @@ tags:
   - Forest
 ---
 
-Original paper: O’Sullivan, Hannah, et al. "Integrating terrestrial laser scanning with functional–structural plant models to investigate ecological and evolutionary processes of forest communities." Annals of Botany 128.6 (2021): 663-684. [https://doi.org/10.1093/aob/mcab120](https://doi.org/10.1093/aob/mcab120)
+Original paper: Hackenberg, Jan, et al. "SimpleTree—an efficient open source tool to build tree models from TLS clouds." Forests 6.11 (2015): 4245-4294. [https://doi.org/10.3390/f6114245](https://doi.org/10.3390/f6114245)
 
 This is a record of the reading process, aimed at personal learning, reading and writing practicing.
 
@@ -25,6 +25,15 @@ This is a record of the reading process, aimed at personal learning, reading and
   - [2.1 Filter and Clustering Routines](#21-filter-and-clustering-routines)
   - [2.2 Tree Modeling](#22-tree-modeling)
   - [2.3 Point Cloud Processing](#23-point-cloud-processing)
+  - [2.4 Output Data](#24-output-data)
+- [3 Software—Comparison Method Raumonen et al. (2013)](#3-softwarecomparison-method-raumonen-et-al-2013)
+- [4  Data Sets](#4--data-sets)
+- [5 Results](#5-results)
+- [6 Discussion](#6-discussion)
+  - [6.1 The Benefit of Open Source](#61-the-benefit-of-open-source)
+  - [6.2 The Benefit of QSMs](#62-the-benefit-of-qsms)
+- [7 Outlook](#7-outlook)
+- [8 Conclusion](#8-conclusion)
 
 Abstract
 ------
@@ -90,9 +99,9 @@ $$ AGB = a \times DBH^b \quad (1) $$
 
 >* Radius Outlier Removal (r, k) [83], Statistical Outlier Removal (sdMult, k) [83], Voxel Grid Filtering (cellsize) [83]
 >* **Curvature Filtering** (min1, max1, min2, max2, min3, max3):
-    >* A PCA is performed for each point’s neighbourhood. $\lambda_1$, $\lambda_2$ and $\lambda_3$ denote the normalized Eigenvalues for each point. For all $\lambda_1$ minimum $\text{min}$ and maximum $\text{max}$ value are computed. $\text{min}_1$ and $\text{max}_1$ are percentage numbers. A point is considered an outlier, if its $\lambda_1$ is smaller than $\text{min} + (\text{max} - \text{min})/100 \times \text{min}_1$ or larger than $\text{min} + (\text{max} - \text{min})/100 \times \text{max}_1$. $\lambda_2$ and $\lambda_3$ are processed in the same manner. The thresholds are adjusted with a slider and before the removal-confirmation all noise points are marked via transparency and colourisation in real time according to the slider values (Figure 3).
-    >* 对每个点的邻域执行主成分分析（PCA）。$\lambda_1$、$\lambda_2$ 和 $\lambda_3$ 表示每个点的归一化特征值。对于所有 $\lambda_1$，计算最小值 $\text{min}$ 和最大值 $\text{max}$。$\text{min}_1$ 和 $\text{max}_1$ 是百分比数。如果一个点的 $\lambda_1$ 小于 $\text{min} + (\text{max} - \text{min})/100 \times \text{min}_1$ 或大于 $\text{min} + (\text{max} - \text{min})/100 \times \text{max}_1$，则认为它是一个异常值。$\lambda_2$ 和 $\lambda_3$ 以相同的方式处理。阈值随滑块调整，在移除确认之前，根据滑块值实时通过透明度和着色标记所有噪声点（图3）。
-    >* [Algorithm: Principal components analysis (PCA)](_posts\2024-03-01-blog-algorithm-001.md)
+    >* A PCA is performed for each point’s neighbourhood. \( \lambda_1 \), \( \lambda_2 \) and \( \lambda_3 \) denote the normalized Eigenvalues for each point. For all \( \lambda_1 \) minimum \( \text{min} \) and maximum \( \text{max} \) value are computed. \( \text{min}_1 \) and \( \text{max}_1 \) are percentage numbers. A point is considered an outlier, if its \( \lambda_1 \) is smaller than \( \text{min} + (\text{max} - \text{min})/100 \times \text{min}_1 \) or larger than \( \text{min} + (\text{max} - \text{min})/100 \times \text{max}_1 \). \( \lambda_2 \) and \( \lambda_3 \) are processed in the same manner. The thresholds are adjusted with a slider and before the removal-confirmation all noise points are marked via transparency and colourisation in real time according to the slider values (Figure 3).
+    >* 对每个点的邻域执行主成分分析（PCA）。\( \lambda_1 \)、\( \lambda_2 \) 和 \( \lambda_3 \) 表示每个点的归一化特征值。对于所有 \( \lambda_1 \)，计算最小值 \( \text{min} \) 和最大值 \( \text{max} \)。\( \text{min}_1 \) 和 \( \text{max}_1 \) 是百分比数。如果一个点的 \( \lambda_1 \) 小于 \( \text{min} + (\text{max} - \text{min})/100 \times \text{min}_1 \) 或大于 \( \text{min} + (\text{max} - \text{min})/100 \times \text{max}_1 \)，则认为它是一个异常值。\( \lambda_2 \) 和 \( \lambda_3 \) 以相同的方式处理。阈值随滑块调整，在移除确认之前，根据滑块值实时通过透明度和着色标记所有噪声点（图3）。
+    >* [Algorithm: Principal components analysis (PCA)](https://yuxuannsun.github.io/posts/2024/03/blog-algorithm-001/)
 >* Intensity Filtering (min, max)
 >* Crop Sphere | Crop Box (radius)
 >* **Euclidean Clustering** (clusterTolerance, minPts, numCluster) [83]
@@ -110,3 +119,50 @@ $$ AGB = a \times DBH^b \quad (1) $$
 
 ## 2.3 Point Cloud Processing
 
+>* Iterative Closest Point (ICP) (β) [74,89]
+>* Merge ()
+
+## 2.4 Output Data
+
+>* Cloud To Model Distance
+>* Single Value Tree Parameters
+    >* This file contains the entries for the total tree volume and the stem volume. Solid volume is the volume of all compartments with a diameter larger 7 cm. The tree’s height and its length are included, as well as the DBH and the root diameter at lowest z-coordinate. The stem volume from the root up to the first branch and the stem volume up to the crown base are printed with additionally the crown base height. The crown volume and the crown surface from the convex hull crown model and lastly the crown projection area are additional output parameters. **In Hackenberg et al. [12] more detailed definitions on various output parameters are given**.
+>* Complete Cylinder Model
+
+# 3 Software—Comparison Method Raumonen et al. (2013)
+
+* Comparison.
+
+# 4  Data Sets
+
+* to estimate biomass.
+
+# 5 Results
+
+* the results of estimation and comparision.
+
+# 6 Discussion
+
+## 6.1 The Benefit of Open Source
+
+>* The Computree [70] platform is written in C++ and supports PCL usage. 
+>* Due to the strict modularization, efficient integration of other methods is possible.
+>* Such a symbioses reduces the amount a single scientist has to perform by itself.
+
+## 6.2 The Benefit of QSMs
+
+>* **Stem curves** can be extracted efficiently from the results models, as can be seen in Figure 18. This stem curve is not validated on ground truth data, but shows a strong natural pattern.
+>* Additionally the branches of the same target tree have been binned according to the height of their base. The utilized bin width here is half a meter, within a bin the volume of all contained branches was summed up. In Figure 19 only three major whorls are visible, the height of the first whirl can be defined as the crown base. (don't quiet understand yet)
+>* A time series analysis utilizing SimpleTree is in preparation by Sheppard et al. [97].
+
+* Sheppard, Jonathan, et al. "Terrestrial laser scanning as a tool for assessing tree growth." iForest: Biogeosciences and Forestry 10.1 (2017): 172-179. [https://doi.org/10.3832/ifor2138-009](https://doi.org/10.3832/ifor2138-009)
+
+# 7 Outlook
+
+>* **Bad input parameters** though lead to **undesirable models**. We conclude that the most beneficial way to estimate high accurate models is to create an artificial intelligence (AI) to support the **threshold setting for sensitive methods** like ours.
+>* The proposed optimization method is a brute force one. Literature reports optimization algorithms such as the downhill simplex method published in Nelder et al. [98] or other methods described in Press et al. [99]. Libraries like Scipy [100] provide implementations of those methods.
+
+# 8 Conclusion
+
+>* The public availability of the source code allows the recompilation of the program on different Operating Systems with a **C++** compiler. The recommended system for using SimpleTree is **Ubuntu Linux**, but it has also been successfully compiled on **Windows 7**.
+