ENGLISH

25卷/3期

25卷/3期

華藝線上圖書館

Pages:

129-148

論文名稱

地面雷射掃瞄系統於小樣區之立木測計

Title

Terrestrial Laser Scanning Systems for Measuring Tree Based on Small Plot Data

作者

蕭子淳、陳建璋、陳朝圳、魏浚紘

Author

Tzu-Chun Shiao, Jan-Chang Chen, Chaur-Tzuhn Chen, Chun-Hung Wei

中文摘要

地面雷射掃瞄儀 (Terrestrial Laser Scanning, TLS) 具非破壞性量測之特性,其點雲可透過視覺化及自動化方式獲取準確林木性態值。本研究以地面雷射掃瞄儀取得立木點雲資料,探討不同立木胸徑與樹高之測計方法,並討論不同架站模式及不同掃瞄解析度對樣區點雲生成之影響。結果顯示,光達測得之胸徑及樹高在人工判釋測計法及半自動化偵測法上均與實測值無顯著差異。另掃瞄解析度為1/4 (43.70 MPts)及1/2 (174.80 MPts) 時,所測計之胸徑、樹高與實測值無顯著差異,而解析度1/2 (174.80 MPts) 時精度未上升,故以掃瞄解析度1/4 (43.70 MPts) 即可取得準確立木胸徑;且掃瞄站數多寡與胸徑、樹高萃取準確度呈正相關,故架站數應以四站以上為佳。

Abstract

The Terrestrial Laser Scanning (TLS) has the characteristics of non-destructive measurement, and its point cloud information acquired from TLS allows obtaining accurate forest attributes through visualization and automated extraction. The purposes of this study include using TLS to obtain point cloud information and discuss different methods to extract Diameter at breast height (DBH) and height (H) within small plot, and we also discuss the impact of point cloud generation in different resolution and station number settings. The results showed that the DBH and H measured by TLS were not significantly different from the measured values in the manual extraction and semi-automatic extraction detection methods. In addition, when the scanning resolution is 1/4(43.70 MPts) and 1/2(174.80 MPts), there is no significant difference between the measured DBH, H and the measured value, and the accuracy does not increase when the resolution is 1/2(174.80 MPts). Therefore, we can obtain the accurate DBH at the scanning resolution of 1/4(43.70 MPts); and the number of scanning stations is positively correlated with the DBH and H extraction accuracy. Therefore, the number of stations should be more than four stations.

關鍵字

地面雷射掃瞄系統、森林調查、自動化測計、點雲資料

Keywords

Terrestrial Laser Scanning System, Forest Inventory, Automated Extraction, Point Clouds Data

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10218661-202009-202010010001-202010010001-129-148

備註說明

N / A

Pages:

149-157

論文名稱

福衛八號植被紅邊波段時間延遲積分設計對葉綠素反演品質之影響評估

Title

Assessment of FORMOSAT-8 Time Delay Integration on the Retrieval of Chlorophyll Content Quality by Using Red Edge Bands

作者

張立雨、劉小菁、廖敦佑

Author

Li-Yu Chang, Cynthia Liu, Tun-Yu Liao

中文摘要

福衛八號衛星感測器預計除可見光與近紅外光波段外,在植被紅邊波段規劃兩米空間解析度之兩窄波長波段,預期除可提供豐富光譜資訊外並可進行植被葉綠素含量之推估。然而紅邊波段波長有限,該設計可能造生影像雜訊過大之結果,因此在福衛八號感測器上採用時間延遲積分技術以提升影像之訊噪比。為分析適當之時間延遲積分階數以達到合理之訊噪比,並能在後續葉綠素反演時得到合理之精度,本研究透過植被光譜反應模型模擬不同葉綠素含量之植被光譜,然後在不同時間延遲積分階數下推估受雜訊影響之葉綠素含量精度以作為後續感測器設計之參考。

Abstract

The FOMROSAT-8 (FS-8) program integrates two vegetation red edge (VRE) bands into its two-meter pushbroom multi-spectral sensors. Time-Delay-Integration (TDI) technique is utilized to improve Signal-to-Noise Ratio (SNR). Along with other applications, these two VRE bands are fruitful for the retrieval of chlorophyll contents of vegetation canopies. Without TDI, high spatial resolution and narrow bandwidth result in poor SNR which weakens the applicability of VRE bands. In order to obtain reasonable number of TDI stages setting for VRE bands, firstly the spectral responses of chlorophyll contents are simulated to obtain the noise affected reflectances observed by different number of TDI stages setting. Then, acceptable accuracy of retrieved chlorophyll contents and the corresponding number of TDI stages setting are suggested as a reference in the design of FS-8.

關鍵字

福衛八號、植被紅邊波段、葉綠素含量、訊噪比、時間延遲積分

Keywords

FORMOSAT-8, Vegetation Red Edge, Chlorophyll Contents, Signal-to-Noise Ratio, Time Delay Integration

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10218661-202009-202010010001-202010010001-149-157

備註說明

N / A

Pages:

159-179

論文名稱

應用MODIS資料估算稻作之綠水足跡

Title

Estimation of Green Water Footprint of Rice Paddies Using MODIS Data

作者

黃姿瑜、林士淵、吳治達、林俊德

Author

Tzu-Yu Huang, Shih-Yuan Lin, Chih-Da Wu, Justin Chun-Te Lin

中文摘要

綠水足跡 (Green Water Footprint) 指作物生產過程中消耗的雨水量,含降水的總蒸發散量及產品中所含水分;綠水能使作物成長而具生產性,然因其潛藏於產品中且含量甚低、或為蒸發散形式而使推估不易。為使作物生產用水量能被精確統計,有效估算綠水足跡至為重要。MODIS 全球地表蒸發散監測資料(MOD16) 具有高精度、涵蓋面積廣闊等優勢,然欲實際運用則有時間及空間上的限制。故本研究以MOD16 為基礎,搭配稻作蒸發散相關因子,透過統計方法中的逐步迴歸法,建立蒸發散推估模型,以改進MOD16 資料之時空限制、並供後續精確估算依使用者特定時間需求且完整的稻作綠水足跡。

Abstract

Green Water Footprint (GWF) is referred to total rainwater evapotranspiration (ET) plus the amount of water incorporating in a product. As the water incorporated into the crop is about 0.1~1% of the evapotranspiration volume, GWF is normally referred to as ET volume. Together with irrigation water withdrawn from ground or surface water, they are the main indicators of contribution of water usage introduced in agricultural products. Therefore in order to comprehensively understand the total amount of water used during crop growing stage, it is essential to identify the amount of and Green water. Based on the high accuracy, wide coverage and long-term extraction of rainwater evapotranspiration, a MODIS Global Terrestrial Evapotranspiration Data Set (MOD16) is applied to estimate GWF. Although the MOD16 product offers aforementioned advantages, we still encountered spatial and temporal inaccessibility. Therefore, this research aimed to overcome the drawbacks and develop a regression method considering multiple variables to improve the performance of GWF estimation. Through the experiments implemented in Taichung and Taitung, it was successfully demonstrated that the spatial and temporal accessibility of GWF of rice paddies calculated based on MOD16 data was improved significantly.

關鍵字

稻作、綠水足跡、蒸發散量、MOD16、逐步迴歸

Keywords

Rice, Green Water Footprint (GWF), Evapotranspiration (ET), MOD16, Stepwise regression

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10218661-202009-202010010001-202010010001-159-179

備註說明

N / A

Pages:

181-192

論文名稱

以密度為基礎的演算法在高光譜水稻田影像的應用:考慮影像屬性刪減

Title

The Study of Density-based Classification through Hyper-spectrum Image Data of Paddy Rice with Considering Attribute Reduction

作者

萬絢、王依蘋、鄭育欣

Author

Shiuan Wan, Yi-Ping Wang, Yu-Hsin Cheng

中文摘要

過去,資料挖掘方法和通過遙感數據吸引了人們的廣泛興趣且廣為接收。本研究的目的是通過高光譜與多光譜成像提取有影響性之信息,本研究將稻田遙感圖像與監督學習線性判別分析和基於監督的學習密度的聚類算法一起應用。以密度為基礎的演算法在高光譜材料上有86.85% 的辨識成果比傳統光譜79.45%高出很多。本研究使用主成分分析 (PCA) 四個案例研究:(1)基於線性判別分析的高光譜與多光譜;(2)高光譜與多光譜基於密度的聚類算法;(3)高光譜與多光譜PCA+線性判別分析;(4)高光譜與多光譜PCA+基於密度的聚類算法,結果由誤差矩陣 (準確率)表示,並繪製了主題圖。

Abstract

In the past, it draws the great attraction of using Data Mining approaches and geostatistics analysis through remote sensing data which are well-accepted. The goal of this study is decided to extract the core spectral information through hyperspectral vs. multispectral imaging. More specifically, the paddy-field remote sensing image is applied with a supervised learning linear discriminant analysis and unsupervised learning density-based clustering algorithm in this study. The pre-processing is used the Principal Component Analysis (PCA) to design parallel study for four case studies: (1) hyper-spectrum versus multi-spectrum with linear discriminant analysis (2) hyper-spectrum versus multi-spectrum density-based clustering algorithm (3) hyper-spectrum versus multispectrum principal component analysis + linear discriminant analysis (4) hyper-spectrum versus multi-spectrum by principal component + density-based clustering algorithm. The DBSCAN with hyper-spectrum image data has an overall accuracy rate of 86.85% which is higher than those of DBSCAN with multi-spectrum (79.45%). The results are presented by error matrix (accuracy rate) and the thematic maps are drawn.

關鍵字

影像分類、資料探勘、基於密度的聚類算法

Keywords

Image Classification, Data Mining, Density-based Clustering Algorithm

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10218661-202009-202010010001-202010010001-181-192

備註說明

N / A

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