ENGLISH

13卷/4期

13卷/4期

華藝線上圖書館

Pages:

231-239

論文名稱

人工編修空載光達資料產製DEM成果之探討

Title

On the Manual Editing for DEM Generation with Airborne LiDAR Data

作者

何心瑜,陳大科,史天元,徐偉城

Author

Hsin-Yu Ho,Da-Ko Chen,Tian-Yuan Shih, Wei-Chen Hsu

中文摘要

空載光達數據產製數值高程模型(DEM)資料過程中,不同產品等級所需要的產製流程不盡相同。本研究於2007年4~ 6月間進行,依當時之參考規範草稿,DEM產品可分為三個等級;當產製Level 2以上的產品時,即需藉由人工編修來確保資料品質。研究中選擇五種不同覆蓋面共10幅1/5000圖幅範圍,探討編修時間、不同覆蓋面和不同編修者等三個不同的人工編修項目產製DEM結果。初步成果顯示,以同一圖幅範圍為例,不同編修人員所需之編修時間不同,成果亦不盡相同;如矮植被區之空載光達資料編修DEM成果的高程平均差為8公分,標準差為25公分。

Abstract

In the process of producing digital elevation model with airborne LiDAR, different procedures are required for products of different specified level. This thesis was written from April to June, 2007. Reference to a draft for specifications and standard operation procedures for airborne LiDAR survey, MOI, DEM products were classified to three levels. When producing products higher than level two, it requires manual editing to assure the correctness. In this study, several issues of manual editing are investigated. Ten map-sheets covering five major land-cover types are selected for the comparison. The area of each map-sheet is about 2.5 km by 2.5 km. Preliminary results show that for the same map-sheets, manual editing by different operators may produce different results and the time required is significantly different. In general, the height difference of the DEMs from two individuals is 8 cm and it gives a standard deviation of 25 cm.

關鍵字

空載光達、人工編修、過濾

Keywords

airborne LiDAR, manual editing, filtering

附件檔名

華芸線上圖書館

N / A

備註說明

200812-13-4-231-239

Pages:

241-251

論文名稱

應用遙測技術評估不同生態分類系統和空間尺度對環境參數之影響

Title

Assessment of Ecosystem Classification Systems at Various Spatial Scales on Environmental Parameters Using Remote Sensing Techniques

作者

鄭祈全,羅漢強,陳永寬,吳治達

Author

Chi-Chuan Cheng, Hann-Chung Lo, Yeong-Kuan Chen ,Chih-Da Wu

中文摘要

本研究旨在應用遙測技術,評估不同生態分類系統和空間尺度對環境參數的影響。研究方法包括:應用混合式影像分類法,將台灣北部地區之Landsat-5 TM影像進行土地使用分類;利用DTM及SEBAL模式萃取16種環境參數,並探討各土地使用型之環境參數的差異情形;以及透過多變量統計之逐步判別分析法,評估北台灣12個地理氣候區及7個集水區兩種生態分類系統在不同空間尺度下之環境參數的差異性。研究結果指出,試區經混合式影像分類法分為森林、建地、水體、耕作農地、無耕作農地、雲及陰影7種土地使用型;在分析5種(扣除雲及陰影)土地使用型之環境參數的差異比較時,顯示森林地區在太陽入射角之餘弦影像、大氣層頂單日輻射量、淨輻射量、常態化差異植生指標、地表熱紅外光放射率、摩擦速度、動量傳輸粗糙度、大氣可感熱、土壤熱通量及蒸發散量等指標值較高,而在一維穿透係數、大氣密度、地表反照率、大氣層頂表面反照率、空氣阻抗熱傳導係數、地表溫度等指標值較低;至於評估不同生態分類系統和空間尺度對環境參數之影響結果指出,在不同的生態分類系統和空間尺度之下,用來區分5種土地使用型所需要的環境參數與參數數目皆不盡相同,但常態化差異植生指標與地表熱紅外光放射率兩項參數不管在那一種生態分類系統,均為重要的影響參數。

Abstract

The main purpose of this study was to assess the effect of ecosystem classification systems at various scales on environmental parameters using remote sensing techniques. The processes included applying hybrid classification to generate a land-use map of the north Taiwan using Landsat-5 TM image in 1995; using the DTM and the SEBAL model to calculate 16 environmental parameters and compare the differences among different land-use types; and assessing the effects of 2 ecosystem classification systems (i.e., geographic climate method and watershed division method) at various scales on environmental parameters using stepwise discriminant analysis. The results indicated that the study area was classified into 7 land-use types. They were forest-land, building, farm-land, baring farm-land, water body, cloud, and shadow. The Comparison of 16 environmental parameters among 5 land-use types (excluding cloud and shadow) showed that forestland had higher value with cosine of solar incidence angle, twenty-four hour extraterrestrial radiation, net radiation, normalized difference vegetation index, emissivity, estimating friction velocity, surface roughness for momentum transport, sensible heat flux, soil heat flux, evapotranspiration, and had lower value with transmittance, air density, surface albedo, surface albedo at the top of atmosphere, aerodynamic resistance to heat transport, surface temperature. As for assessing the effect of 2 ecosystem classification systems at various scales on environmental parameters, the result pointed out that ecosystem classification systems at various scales indeed caused the variation of environmental parameters according to the selected parameters and the number of parameters for discriminating 5 land-use types. However, among environmental parameters, normalized difference vegetation index and emissivity were the most important factors regardless of ecosystem classification systems at various scales.

關鍵字

生態分類系統、尺度、遙感探測、環境參數、SEBAL模式

Keywords

Ecosystem classification, scales, remote sensing, environmental parameters,

附件檔名

華芸線上圖書館

N / A

備註說明

200812-13-4-241-251

Pages:

253-260

論文名稱

利用福衛二號衛星資料估測水稻族群葉面積指數

Title

Using Formosat-2 Satellite Data to Estimate Leaf Area Index of Rice Crop

作者

楊純明,劉正千,王薏雯

Author

Chwen-Ming Yang, Cheng-Chien Liu, Yi-Wen Wang

中文摘要

在稻作生育期間定期針對大面積水田稻株進行生長估測,以提供稻株生長狀態之即時資訊,乃實施稻作精準管理所必需,俾達到水稻各特定地點之差異性操作。將葉面積指數(leaf area index; LAI)估測值輸入預先研擬之作業決策模組,不僅可以評估稻作之生長狀態,亦可同時預測收穫產量。本研究旨在稻作生育過程中定期同步取樣水稻族群LAI及量測植被高解析反射光譜,據以建立實測LAI (即LAImeasured)與高光譜窄波段標準差植被指數(即NDVINB)之數學關係。試驗係於2006年一、二期稻作期間實施,將水稻植株以8種不同栽植密度栽種於田間,族群密度介於0.28-2.78  105 hills ha-1之間,再於生育季節中定期測定LAI (m2 m-2)及量測光譜。兩期稻作合計收集36幅福衛二號衛星資料(影像及光譜),經幾何與輻射校正後計算得到多頻譜寬波段標準差植被指數(即NDVIBB),將其輸入前述建立之LAImeasured─NDVINB數學關係式以獲得LAI估值(即LAIBB)。試驗結果顯示,具有高時間、高空間解析力特性之福衛二號衛星資料能夠提供合理LAI估值,因此適合作為追蹤水稻族群生長狀態之資訊來源,此一能力亦使得福衛二號衛星資料具有應用於稻作精準管理之潛力。

Abstract

Estimation of plant growth over a large paddy field provides the needed information for site-specific management of crop including rice. With the estimated leaf area index (LAI) as input, growth status of rice crop may be evaluated and yield production at harvest may be assessed through functioning algorithms. This study measured the near-ground hyperspectral reflectance of rice canopy periodically on the dates of plant samplings during crop development, and then established the relationship between LAImeasured (i.e. the measured LAI) and NDVINB (i.e. normalized difference vegetation index calculated from narrowbands of hyperspectral reflectance) from the collected data. Rice plants of different planting densities, in the range of 0.28-2.78  105 hills ha-1, were grown in the field to produce varied values (m2 m-2) of LAI along plant growth for such purpose in the first and the second cropping seasons of 2006. A total of thirty-six multi-spectral images of the study area taken by Formosat-2 satellite on days of ground samplings were also acquired to calculate the broadband values of NDVI (NDVIBB) and input to the LAImeasured─NDVINB relationship to obtain the estimated LAI (LAIBB). Results indicate that the high-temporal and high-spatial-resolution images of Formosat-2 satellite are good source for monitoring plant growth of rice crop by providing reasonable estimated values of LAI. Such a capability of Formosat-2 spectral images enables their applicability in areas of precision farming.

關鍵字

生長估測、特定地點精準管理、高解析反射比光譜、標準差植被指數(正規化

Keywords

Growth estimation, Site-specific management, Hyperspectral reflectance spectrum, Normalized difference vegetation index, Satellite image of Formosat-2

附件檔名

華芸線上圖書館

N / A

備註說明

200812-13-4-253-260

Pages:

261-271

論文名稱

高解析影像應用於土地利用分類之探討

Title

Land cover and Land use Classification using High Spatial Resolution Images

作者

蕭國鑫,劉治中,劉進金,何心瑜,黃英婷

Author

Kuo-Hsin Hsiao, Chi-Chung Lau, Jin-King Liu , Hsin-Yu Ho, Ying-Ting Huang

中文摘要

本研究採用航空照片與高解析衛星影像進行國土利用之第三級分類,並搭配外業調查評估影像分類精度。分類中之航空照片結合立體像對與正射照片,直接在螢幕上判釋與數化地物類別,衛星影像則以監督式及非監督式分類方式為之。初步結果顯示,航空照片判釋國土利用調查之土地利用第三級分類,對於農地、林地、水利與交通用地之全體精度在97~98%之間,人為建物區則難以分辨到第三級分類;衛星影像適合分辨林地與水利用地之第一或第二級分類,分類精度為60~75%,但對於第三級分類別,則需輔以GIS資料及結合多時期影像,方可達到較高的分類精度;對於人為建物之第三級分類,航空照片與衛星資料均無法明確分辨。另以多時期衛星影像結合農作坵塊資料,單獨判釋桃園地區2006年二期稻作之第三級分類水稻類別,全體精度達96.47%,但生產者與使用者精度為73.38%與74.23%,仍低於平均精度之85.76%;因此,若水稻類別的辨識若能再結合種植頻率分析,則分類精度應還有再提昇的空間。

Abstract

High spatial resolution images applied in this study include digital orthophotos of 0.5m grid and pan-sharpened SPOT images of 2.5m grid. A land-cove/land-use classification scheme upto level 3 is adopted, with randomly-sampled field checks for accuracy verification. Manual interpretation with the assistance of on-screen tool-kit is adopted for the discrimination of various land-cover/land-use types on orthophotos. Whereas automatic classifications both supervised and unsupervised approaches are applied for satellite images. Preliminary results show that aerial photographs give an accuracy of 97~98% for agriculture, forestry, hydraulic and telecommunication land units. Satisfied results of level 3 of building up areas can never been achieved solely by photo-interpretation. For satellite classification, an accuracy of 60~75% can be achieved for level 1 and level 2 for the forestry and hydraulic land units. If the classes of level 3 are to be achieved, more ancillary information from GIS data-base should be incorporated. Level 3 can not be attained using satellite images. Multi-temporal images with complementary GIS polygons of field parcels can give rice parcels an overall accuracy of 96.47%. Whereas, the average accuracy (85.76%) is higher than the produce’s accuracy (73.38%) and user’s accuracy (74.23%). It is concluded that temporal plant histogram information is helpful and critical for the classification of agriculture lands.

關鍵字

空載光達、人工編修、過濾

Keywords

Remote Sensing, GIS, Classification

附件檔名

華芸線上圖書館

N / A

備註說明

200812-13-4-261-271

Pages:

273-284

論文名稱

物件導向分類於高解析度影像自動判釋

Title

Object-Oriented Image Classification with High Spatial Resolution Satellite Imagery

作者

鄭雅文,史天元,蕭國鑫

Author

Ya-Wen Cheng, Tian-Yuan Shih, Kuo-Hsing Hsiao

中文摘要

以往利用遙測影像分類之自動判釋土地覆蓋作業,多以各光譜段中各類別輻射值之差異進行分類,但影像各像元間之空間相關性,亦有益於分類之資訊;隨著衛星影像空間解析度的提昇,其所擁有之空間相關性資訊預期將更高,且有助於分類成果精度的提昇。本研究採用之物件導向分類方法,是利用影像中呈現自然相鄰狀態特徵的方法,將影像依均調性、空間相關性…等因素,分割成不等的影像區塊再進行分類,故為一種結合空間相關資訊和光譜資訊的分類技術。其中之物件導向分類方法:包括ECHO、Definiens,與以逐像元方式進行分類之分類法:並與高斯最大似然法、倒傳遞類神經網路及支持向量機等,進行分類成果比較。 研究中利用三幅不同土地覆蓋複雜度之SPOT-5融合影像(空間解像力2.5公尺*2.5公尺),針對上述五種分類演算法進行測試。結果顯示自動分類法應用於較繁雜之土地覆蓋,若影像具明顯的紋理特徵,則物件導向方法可得到相對較佳的分類成果;而支持向量機分類法對於過於複雜的土地覆蓋類型,因無法模擬出最適超平面區的分類別,故未能於各案例中穩定保持其分類精度之優勢。

Abstract

For the land cover classification with remote sensing images, the spectral differences among classes provide the vital information. Besides the spectral information, the spatial relation of pixels also carries useful information for classification. Along with the increase of spatial resolution for satellite images, the spatial information is expected to be more abundant than before. Object based classification utilizes the spatial segmentation procedure prior to the classification. This study investigates the performance of ECHO, Definiens, and compared with the maximum likelihood, back-propagation, and support vector machines, classification schemes. Three SPOT-5 images with different complexity are selected for the experiments. The spatial resolution of these two images is 2.5 m, which were produced through the fusion process. From the experiments, it is shown that object based classification schemes provide stable result, while the SVMs showed dependency of data set.

關鍵字

多光譜影像、土地覆蓋、分類

Keywords

Multispectral image, Land cover, Classification

附件檔名

華芸線上圖書館

N / A

備註說明

200812-13-4-273-284

Pages:

285-294

論文名稱

使用多光譜影像結合光譜及空間資訊偵測海洋油污

Title

Using Spectral and Spatial Information for Oil Spill Detection in Multi-spectral Imagery

作者

高宏明,曹伶伶,李昭興,任玄

Author

Hung-Ming Kao, Ling-Ling Tsao, Chao-Shing Lee, Hsuan Ren

中文摘要

海洋溢油污染伴隨而來的是嚴重的生態浩劫災害。如何偵測、監測及追蹤油污的分佈,一直以來都是重要的課題。在廣闊的海洋裡要能監測油污的分佈使用遙測影像是最有效率的方法。但是海面上的油污通常只佔有遙測影像中很小的區域,如何使用遙測技術偵測油污的分佈將是一項挑戰。本研究主要的目地是發展一套能運用光學衛星影像來偵測油污的技術。由於油污在海上的分佈經常只佔較小的面積,所以油污的分佈可以看成是影像裡的異常物。在本研究裡運用了異常物偵測的演算法,將油污的份佈大致偵測出來。但是面對海面上的許多干擾,異常物演算法經常會有許多誤判。為了將油污分佈從其他的干擾中區分出來,我們將空間特徵資訊加入異常物偵測的計算。我們所提供的方法可利用海面上油污分佈特有的空間特徵及油污的光譜特性,將在海面上的油汙分佈精準的估計出來。

Abstract

Oil spill on the sea surface produced by human activities is disastrous to the ecological environment. How to detect, monitor and track the oil spill are always very important tasks. Due to oil spill often occur in open sea, remotely sensed image provides an effective technology to monitor the sea area. But the oil spill only occur in a very small are in the image, therefore, to detect the oil spill on the sea surface is a challenge problem. This study focuses on the detection of oil slick on sea surface using both spectral and spatial information of the remotely sensed images. Since the oil spill area usually only occupies a few pixels in the image scene, it can be considered as anomaly. By applying anomaly detection algorithm with only spectral information, the oil spill area can be extracted along with other marine phenomena as interference. In order to eliminate the interferences, spatial features are introduced. The experimental result shows the proposed method combining the spatial features of oil spill with spectral information improves the oil spill detection performance.

關鍵字

溢油污染、異常物偵測、RX演算法、空間資訊特徵

Keywords

oil spill, anomaly detection, RX detector (RXD), spatial feature.

附件檔名

華芸線上圖書館

N / A

備註說明

200812-13-4-285-294

更多活動學刊