Mapping and Recovering Cloud-contaminated Area in Optical Satellite Imagery

Visiting Graduate Student Department of Geography University of KansasPlatinum Sponsors * KU Transportation Research Institute Gold Sponsors * KU Department of Geography * KU Institute for Policy & Social Research * State of Kansas Data Access and Support Center (DASC) * KU Libraries GIS and Scholar Services * Wilson & Company Engineers and Architects Silver Sponsors * Bartlett & West * KansasView Consortium * KU Biodiversity Institute Bronze Sponsors * AECOM * Kansas Biological Survey * C-CHANGE Program (NSF IGERT) * KU Environmental Studies Program * KU Department of Ecology and Evolutionary Biology * Mid-West CAD * National Weather Service * Spatial Data Researc

Crowdsourcing as a participative tool in a landscape conservation initiative at the urbanrural buffer zone: a case study of the Waipu District in Taichung, Taiwan

The built environment in rural settlements located in urban-rural buffer zones increasingly shows urban elements and character in response to the global impact of rapid urbanization. Due to its cultural and agricultural value, preserving natural resources and rural landscape features becomes a pressing contemporary issue. This study focuses on exploring consensus from different stakeholders concerning natural landscape preservation in the rural settlement of Waipu District surrounding the Taichung core metropolitan area. Both desakota mapping with remote sensing data and road curvature were applied to explore the relationship between desakota expansion and road network development. Based on the concepts of crowdsourcing and Google street view (Naik et al., 2014), we also designed an “i-Scoring” platform to elicit participation and collaboration of multiple stakeholders and non-stakeholders. “i-Scoring” collected perceptions of the Waipu landscape, facilitating the evaluation of its attractiveness, ecological value and vulnerability using quantitative benchmarks. The results imply that winding agricultural road networks may play a role in identifying and disrupting emerging illegal industries in Waipu. Agriculture in nature facilitates the development of ecological conservation efforts such as tours. With a total of 1210 clicks, we derived “hot spot” maps using quantitative benchmarks to show areas of high attractiveness, ecological value and vulnerability. Identified hot spot areas are both primary conservation targets and potential areas for ecological tours. This study also allowed us to introduce to stakeholders the Satoyama Initiative (Takeuchi, 2010), whose aim is to help develop socio-ecological production landscapes based on a prosperous agricultural environment

Down-regulation of PKCζ in renal cell carcinoma and its clinicopathological implications

<p>Abstract</p> <p>Background</p> <p>Metastatic renal cell carcinoma (RCC) is highly resistant to systemic chemotherapy. Unfortunately, nearly all patients die of the metastatic and chemoresistant RCC. Recent studies have shown the atypical PKCζ is an important regulator of tumorigenesis. However, the correlation between PKC<b>ζ </b>expression and the clinical outcome in RCC patients is unclear. We examined the level of PKCζ expression in human RCC.</p> <p>Methods</p> <p>PKCζ mRNA and protein expressions were examined by real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) respectively in RCC tissues of 144 patients. Cellular cytotoxicity and proliferation were assessed by MTT.</p> <p>Results</p> <p>PKCζ expression was significantly higher in normal than in cancerous tissues (<it>P </it>< 0.0001) by real-time PCR and IHC. Similarly, PKCζ expression was down-regulated in four renal cancer cell lines compared to immortalized benign renal tubular cells. Interestingly, an increase of PKCζ expression was associated with the elevated tumor grade (<it>P </it>= 0.04), but no such association was found in TNM stage (<it>P </it>= 0.13). Tumors with higher PKCζ expression were associated with tumor size (<it>P </it>= 0.048). Expression of higher PKCζ found a poor survival in patients with high tumor grade. Down-regulation of PKCζ showed the significant chemoresistance in RCC cell lines. Inactivation of PKCζ expression enhanced cellular resistance to cisplatin and paclitaxel, and proliferation in HK-2 cells by specific PKC<b>ζ </b>siRNA and inhibitor.</p> <p>Conclusions</p> <p>PKCζ expression was associated with tumorigenesis and chemoresistance in RCC.</p

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Precisely estimating the yield of paddy rice is crucial for national food security and development evaluation. Rice yield estimation based on satellite imagery is usually performed with global regression models; however, estimation errors may occur because the spatial variation is not considered. Therefore, this study proposed an approach estimating paddy rice yield based on global and local regression models. In our study area, the overall per-field data might not available because it took lots of time and manpower as well as resources. Therefore, we gathered and accumulated 26 to 63 ground survey sample fields, accounting for about 0.05% of the total cultivated areas, as the training samples for our regression models. To demonstrate whether the spatial autocorrelation or spatial heterogeneity exists and dominates the estimation, global models including the ordinary least squares (OLS), support vector regression (SVR), and the local model geographically weighted regression (GWR) were used to build the yield estimation models. We obtained the representative independent variables, including 4 original bands, 11 vegetation indices, and 32 texture indices, from SPOT-7 multispectral satellite imagery. To determine the optimal variable combination, feature selection based on the Pearson correlation was used for all of the regression models. The case study in Central Taiwan rendered that the error rate was between 0.06% and 13.22%. Through feature selection, the GWR model’s performance was more relatively stable than the OLS model and nonlinear SVR model for yield estimation. Where the GWR model considers the spatial autocorrelation and spatial heterogeneity of the relationships between the yield and the independent variables, the OLS and nonlinear SVR models lack this feature; this led to the rice yield estimation of GWR in this study be more stable than those of the other two models

The Study on Cloud Processing in Optical Satellite Imagery

在利用光學式衛星影像進行土地利用判釋或農作物產量估測時，雲層覆蓋是無法避免的干擾之一。以往研究的瓶頸在於多數去雲流程皆需要另外的無雲參考區域或是多時期影像，然而真實世界中，這些參考資訊可能難以取得；再者，對於去雲結果的優劣，通常是以質化而非量化的方式來進行視覺化評估，因此欠缺客觀性；最重要的是，去雲過程通常也會破壞原本的地物資訊，然而去雲後影像能否用來進行自動化地物判釋也欠缺探討。 為解決以上瓶頸，降低雲層的影響並提升地物判釋的正確性，就單時期具有厚雲層的影像而言，本研究以標準差延伸加強 (standard deviation stretch enhancement) 進行影像處理，再以區域增長 (region growing) 之方式偵測並切除無法還原地物資訊的厚雲層。單時期具有薄雲的影像則以傅利葉 (Fourier) 分析建立薄雲的數學模式，再以此模型薄雲並還原薄雲底下的地物光譜資訊，雖然傅利葉分析的方法在模式建立階段仍需兩時期影像，但建立後的模式在對其它影像進行去雲處理時則僅需單時期資訊。而去雲結果的量化評估，厚雲方面以專家法評估偵測去除的範圍準確性，薄雲方面則以影像分類法以及常態化差異植被指數 (normalized difference vegetation index, NDVI) 評估雲下地物資訊還原的程度以及非雲下地物資訊的被破壞程度。 本研究證明了僅以綠、紅、近紅外波段且沒有無雲參考區或參考影像時，對於厚雲偵測來說資訊量是足夠的，在不同特性的研究區，整體精度皆可達到90%以上。而對薄雲去除而言，三個波段在視覺上能達到一些改善的效果，對於地物光譜資訊還原方面，就全幅影像來探討，薄雲過濾器提升了約4%的分類精度，而就各分區來探討，過濾器對雲區的分類精度提升最多，達到了6%，無雲無影區亦有少許提升，影區的分類精度則反而下降，雖然薄雲過濾器無法全面提升影像各區之分類精度，然而其去雲的功效已有發揮。而薄雲過濾器也減輕了薄雲對NDVI值的影響，使其接近無雲狀態下的地物光譜資訊。總體來看，薄雲過濾器對影像分類以及NDVI值的改善程度而言在統計上有達到顯著性 (p < 0.01)。本研究之成果可應用在土地利用判釋和農作物產量估測中的影像前處理程序，除能減少人工判釋和去除雲層的人力，也可增加衛星影像的利用度。Cloud cover is an inevitable interference when mapping land use/cover with optical satellite imagery. In this study, we apply region growing processing to delineate unrecoverable thick cloud and use Fourier analysis to recover ground information from hazy areas with single temporal imagery. Several methodologies across literature successfully solve cloud problems, but most methods require additional cloud-free reference areas or imagery, which may be unavailable in the real world. Moreover, visual methods rather than quantitative methods are used for assessing results, which can be subjective and arbitrary. Most importantly, the feasibility of applying haze-off imagery to image classification is seldom discussed. To overcome the existing limits, expert method is applied to assess the thick cloud delineation and image classification and normalized difference vegetation index (NDVI) is used to evaluate the recovery degree of ground information after the haze-off processing for quantitative verification of the results. This study revises the image enhancement and region growing algorithm to delineate unrecoverable thick cloud. Accuracy assessment shows the overall accuracy of delineation could be 90% above in each study area. For hazy areas, Fourier analysis is used to reduce haze interference and recover ground information. The proposed haze filter increases the overall accuracy of the whole scenes by about 4%. The overall accuracy of hazy areas in the imagery increases the most (by 6%), while that of shadow areas decreased slightly. The influence of haze on NDVI is also reduced with statistical significance (p < 0.01). Both thick cloud and hazy areas processing can be achieved with no cloud-free area or reference imagery required. Future applications include preprocessing of satellite imagery in land use/cover mapping, which can decrease the manpower to interpret and remove cloud areas and increase the usability of the satellite imagery

Developing a technique for the detection and removal of cloud and haze in remote sensing images

遙測技術發展的最大限制之一，便是大氣活動的干擾，尤其對地球資源衛星上裝載可見光段的光學感測器而言，雲霧的干擾最不易處理。而台灣又為多雲區，雲霧的干擾無可避免，若受遮蔽的區域恰為研究區，則會大大減低了衛星影像的價值性。另外，國內災害監測對遙測影像之需求週期幾乎以「天」為單位，更突顯災害發生時遙測影像供不應求之窘境。因此如何去除雲霧之干擾便成為相當重要的課題。本研究即針對上述課題，發展雲霧的偵測及影像鑲嵌之技術。 厚、薄雲霧兩者具有不同之光譜特性，故需個別進行處理。厚雲霧因具高反射特性，故可經由閾值的設定將可見光段呈高反射之部分偵測並去除；薄雲霧雖仍可觀測到底下地物，但已將地物本身的光譜特性扭曲，且難以用演算法偵測去除，因此本研究先將影像由RGB轉換成HIS系統，再假設薄雲霧的加入等於使影像加入白色，即R、G、B三者提高，因此僅改變光譜的亮度或飽和度值，色相並無改變，藉此可偵測並去除薄雲霧。去除厚薄雲霧後殘缺之部分再利用影像鑲嵌之方式，以鄰近日期之無雲影像補償之，並進行色差之調整，使影像資訊量損失到最小。 對於雲霧偵測之結果，本研究以專家法之方式進行檢核。結果厚雲霧部分之總體精度可達97％；在均質海面及有地形效應之區域上，薄雲霧偵測精度約為83％；極度非均質之農業區則降為80％。地物較複雜之區域，需取得較高時間精度之影像才可增加偵測精度。本研究雖因影像取得之限制而無法提升複雜區域之精度，但已證明在HIS系統中可簡化薄雲霧之偵測準則，大大提升自動化偵測雲霧之可能性。Detection and removal of cloud and haze are arduous problems in optical remote sensing imagery processing. Thick cloud and haze have the character of high reflection, so we can set the threshold to detect and remove the areas having extremely high reflection and even mosaic the images with near dates’ ones to create clear and cloudless images. Relatively, areas covered by thin cloud and haze have the spectral characteristics of both surface features and cloud and haze, thus making it difficult to separate them. This research first processed the images with relative radiometric normalization and then transformed the images from the RGB to the HIS color model. Our assumption was that the interference of thin cloud and haze, similar to mixing a color pigment with white, would increase the color intensity and decrease the saturation of an image but would not change its hue value. Guided by this assumption, we processed the multi-temporal images and isolated areas contaminated by thin cloud and haze. The results thus suggest that an automatic method based on the HIS color model is possible for detecting thin cloud and haze on satellite images.中文摘要 I 英文摘要 II 目錄 IV 圖目錄 V 表目錄 IX 第1章 緒論 1 第一節 研究動機 1 第二節 研究目的 3 第2章 文獻回顧 7 第一節 雲霧的偵測與去除 7 第二節 RGB和HIS彩色模型間之轉換及應用 23 第三節 相對性輻射校正 28 第3章 研究方法 31 第一節 研究架構及流程 31 第二節 研究方法 33 第4章 成果與討論 50 第一節 厚雲霧之偵測及去除 50 第二節 薄雲霧之偵測及去除 54 第5章 結論與未來研究 68 第一節 結論 68 第二節 未來研究 71 參考文獻 74 附錄一 雲霧處理及誤差評估之SML語法 78 附錄二 各專家判釋雲霧之差異比較表 8

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply