遙測資訊在生態環境變遷監測上應用之研究－以台南地區為例

Study on the Change Detection of Ecological Environment by Using the Remotely Sensed Data-An Illustration of Tainan District

林金樹(Chin-Su Lin) ； 焦國模(Kuo-Mu Chiao)

《航測及遙測學刊》 2卷3期 (1997/09) Pp. 37-76

https://doi.org/10.6574/JPRS.1997.2(3).4

摘要

本文利用大地衛星多譜資料，檢測台南地區土地利用型景觀組類的全區分類準確度可達80%，顯示應用遙測多譜資訊檢測大面積生態景觀組類資訊具有相當的潛在優勢。都會區與非都會區的景觀發展型態差異很大，利用遙測資訊檢測其變遷發展時，應將都會區獨立分析，以避免資訊混淆的結果。在林區的應用上，遙測多譜資料可以準確檢測出早期林木被覆區被開發的資訊，其對林木區、墾殖區以及裸地區三種土地利用型主題組類的分類可達80%的全區分類準確度，且對林木組類面積率變遷發展的預測準確度可達95%。林分鬱閉度的全區分類準確度可達65%，由兩個時期的林分鬱閉度主題組類檢測的變遷準確度，約為該兩個影像分類準確度的乘積，有誤差相乘的擴大效應；但若以林分鬱閉度成長資訊為變遷準確度的檢測標準時，則無誤差相乘的擴大效應問題。林分鬱閉度變遷發展的動態平衡出現時期，多譜資料檢測的結果與資源調查資料預測的結果是一致的。人口、各級產業人口、產業結構比以及道路密度等社經因子與都會區及非都會區的土地利用組類面積率有極顯著的結構關係，該等變數所建立的脊迴歸模式對景觀組類面積率變異量的解釋能力均在90%以上，模式預測效率及穩定性均很高。顯示利用社經因子等變數所建立的景觀組類發展模式，極適合於預測未來的組類發展資訊。

關鍵字

遙測 ； 環境變遷監測 ； 土地利用 ； 林分鬱閉度 ； 脊迴歸分析

並列摘要

The overall classification accuracy of both Landsat MSS and TM imagery data were more than 80 percent in this study. These results are acceptable for a large landscape area. It also showed that remote sensing techniques has an important potential value in landscape change monitoring. Since the landscape development type of urban and rural area is very different, they should be analyzed separately to extract a reasonable and correct information. The overall accuracy of the classification of tree-cover area and cultivated land and bared land is about 80 percent, and the predicted tree-cover area is very accurate with 95 percent accuracy. The detected accuracy of crown density is more than 65 percent. The change detection accuracy of two thematic images is almost equal to the multiplication of their overall classification accuracy. It implied that an error expansion effect was existed. It is the except case for detecting the change of crown density growth. Whose accuracy was more than 71 percent. The beginning time of ecological dynamic balance of classes' development predicted by MSS detected data and surveyed data is almost same. All the predicted development trends can retain the real cases. The social-economic variables, inverse population density, industrial structure ratio, and road density, have a very significant and strong cause-effect relationship on the landscape developments. All regression models developed by these factors can explain more than 90 percent variance of area percentage of every landscape class. The predicted efficiencies of these ridge regression models were very stable and could retain on the level of models. This encourages us to combine the multispectral scanning imagery data and ridge regression models to monitoring and predicting the landscape developments in the future.

並列關鍵字

Remote sensing ； environment change detection ； land use ； crown density ； ridge regression analysis