坦尚尼亞姆貝亞和松圭地區環境因素影響土地覆蓋變化的地理空間分析（2018-2022年）;Geospatial Analysis of Environmental Factors Influencing Land Cover Change in Mbeya and Songwe, Tanzania (2018-2022)

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题名:	坦尚尼亞姆貝亞和松圭地區環境因素影響土地覆蓋變化的地理空間分析（2018-2022年）;Geospatial Analysis of Environmental Factors Influencing Land Cover Change in Mbeya and Songwe, Tanzania (2018-2022)
作者:	倪可詩;Nicholaus, Consolatha Sileyo
贡献者:	遙測科技碩士學位學程
关键词:	土地覆蓋變遷;NDVI;地表溫度;遙測;多元線性回歸;Land cover change;NDVI;Land surface temperature;Remote sensing;Multiple linear regression
日期:	2025-07-31
上传时间:	2025-10-17 12:50:27 (UTC+8)
出版者:	國立中央大學
摘要:	摘要土地覆蓋變遷為人類對地球系統最主要的改變形式之一，對環境退化具有顯著影響。本研究針對坦尚尼亞南部高地的姆貝亞（Mbeya）與松圭（Songwe）地區，進行2018年至2022年間多時期土地覆蓋變遷動態分析。研究量化了環境衝擊，評估地表溫度（Land Surface Temperature, LST）與正規化植生指數（Normalized Difference Vegetation Index, NDVI）之間的關係，並辨識出主要的環境驅動因子。本研究運用隨機森林分類法處理多時期Sentinel-2（10公尺解析度）衛星影像，產製土地覆蓋分類圖，其總體分類精度達86.35%，Kappa係數為83.13%。研究結果顯示當地景觀結構發生明顯變化，其中農業用地擴張為主要變遷因素，增加幅度達41.39%；相對地，森林覆蓋減少9.08%，草地覆蓋則減少48.68%。進一步分析顯示，森林減少面積中有64.1%轉為農地，77.8%轉為灌木地。 NDVI分析顯示顯著的空間與時間變異，其中高地森林地區NDVI值最高（0.57–0.92），而農業與都市地區則相對偏低（0.02–0.44）。LST分析則揭示各類土地覆蓋類型具有明顯的熱特徵變化，高地森林地區溫度最低，約為11.7°C，而裸地與都市區域最高可達44.5°C。研究亦發現明顯的冷卻效應，高植被區溫度比建成區低3–5°C；反之，都市發展則造成熱島效應，使溫度高出鄰近植被區2–4°C。NDVI與LST之間呈現高度負相關（r = -0.738, p < 0.001），顯示植被在熱調節中扮演關鍵角色，其中每增加0.1的NDVI，約可降低地表溫度1.5°C。多元線性回歸模型顯示具中度相關性（R² = 0.5584–0.5867），可解釋55–59%的熱–植生關係變異。環境因子分析結果指出，植被密度為主要的熱調節因子，其次為土地覆蓋類型、季節性氣候模式、地形條件、水資源、人為管理措施與土壤性質等。本研究指出，NDVI高於0.6的高地山地森林具備明顯的蒸散作用與樹冠遮蔽功能，為重要的熱庇護區；相對地，農業強化與都市擴張則加劇熱壓力。此外，植被與溫度之間的關係於不同年度間表現出穩定性，年度變異小於1%，顯示本研究結果具潛力應用於長期環境規劃與氣候調適策略。關鍵詞：土地覆蓋變遷、NDVI、地表溫度、遙測、多元線性回歸 ;Abstract Land cover change represents a primary human alteration to the Earth system, substantially contributing to environmental degradation. This research analyzed the dynamics of multi-pretemporal land cover changes in the Mbeya and Songwe regions of Tanzania′s Southern Highlands between 2018 and 2022. The study quantified environmental impacts, measured the relationships between land surface temperature (LST) and normalized difference vegetation index (NDVI), and identified significant environmental drivers of these processes. Utilizing random forest classification, multi-temporal Sentinel-2 (10m) satellite data yielded land cover maps with an overall accuracy of 86.35% and a Kappa coefficient of 83.13%. The observations indicated landscape changes, with agricultural expansion identified as the principal factor, increasing by 41.39% while forest cover decreased by 9.08% and grasslands diminished by 48.68%. Analysis of the transition matrix indicated that 64.1% of forest reduction was converted to cropland, while 77.8% transitioned to shrubland. The analysis of the Normalized Difference Vegetation Index (NDVI) revealed significant spatial and temporal variations, with highland forested regions exhibiting the highest values (0.57-0.92) compared to lower values in agricultural and urban areas (0.02-0.44). Land Surface Temperature (LST) patterns exhibited clear thermal signatures across various land cover types, with temperatures ranging from 11.7°C in highland forested areas to 44.5°C in bare land and urban environments. The most substantial cooling effect was demonstrated, with temperatures maintained at 3-5°C lower than built-up areas, whereas urban development resulted in heat islands that were 2-4°C warmer than adjacent vegetated regions. A strong negative correlation (r = -0.738, p < 0.001) between NDVI and LST indicates the significant role of vegetation in thermal regulation, with each 0.1 increase in NDVI associated with an approximate 1.5°C decrease in surface temperature. Multiple linear regression models demonstrated moderate predictive capability (R² = 0.5584-0.5867), accounting for 55-59% of the variance in thermal-vegetation relationships. The analysis of environmental factors identified vegetation density as the principal thermal regulator, followed by land cover type, seasonal climate patterns, topographic controls, water availability, human management practices, and soil properties. The study indicates that highland montane forests with NDVI values above 0.6 serve as essential thermal refugia due to evapotranspiration and canopy shading. In contrast, agricultural intensification and urban expansion contribute to considerable thermal stress. The temporal stability of vegetation-temperature relationships, with minimal year effects accounting for less than 1% variance, indicates dependable patterns for long-term environmental planning and climate adaptation strategies. Key words: Land cover change, NDVI, land surface temperature, remote sensing, and multiple linear regression
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