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）

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研究生：	倪可詩 Consolatha Sileyo Nicholaus
論文名稱：	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)
指導教授：	蔡富安 Tsai, Fuan
口試委員:
學位類別：	碩士 Master
系所名稱：	太空及遙測研究中心 - 遙測科技碩士學位學程 Master of Science Program in Remote Sensing Science and Technology
論文出版年：	2025
畢業學年度：	113
語文別：	英文
論文頁數：	134
中文關鍵詞：	土地覆蓋變遷、NDVI 、地表溫度、遙測、多元線性回歸
外文關鍵詞：	Land cover change, NDVI, Land surface temperature, Remote sensing, Multiple linear regression
相關次數：	點閱：35 下載：0
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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

摘要    i
Abstract    iii
Acknowledgments    v
Chapter 1. Introduction    1
1.1 Background    1
1.2 Remote Sensing and Land Cover Change    3
1.3. Geographic Context of the Study Area    5
1.4 Ecological Importance and Land Cover Attributes    6
1.5 Vegetation Indices and Environmental Variables    7
1.6 Statement of the Problem    7
1.7 Research Questions    9
1.8 Research Objectives    10
1.9 Scope and Limitation of the study    10
1.10  Theoretical Framework    11
2.1 Environmental Factors Affecting Land Cover Change    13
2.2. Global Trends and Influencing Factors    18
2.3 Regional Trends of Land cover, socioeconomic and Environmental influences    20
2.4.  Land Cover Change in Tanzania    21
2.5. Remote Sensing for Land Cover Classification    25
2.6 Random Forest model    30
2.7 Challenges in Tropical Remote Sensing    31
2.8 Relationship between NDVI and LST and Its Environmental Implications    32
Chapter 3: Materials and Methodology    37
3.1 Research Design    37
3.2 Data Sources and Acquisition    37
3.3 Methodology Framework    40
3.4 Instruments utilized for data processing and analysis    42
3.5 Processing of Sentinel Imagery    43
3.6 Evaluation Land Use/Land Cover Map Accuracy    47
3.7 Land Surface Temperature (LST) Calculation    49
3.8 Normalized Difference Vegetation Index (NDVI) Integration    50
3.9 Correlation of NDVI and LST and multiple linear regression model    52
3.10 Handling Missing Data and Data Cleaning    53
Chapter 4. Results    55
4.1. Land cover classification    55
4.3 Land Cover Distribution and Temporal Trends    62
4.4 Quantification of Land Cover Change    66
4.5. Analysis of the Transition Matrix    71
4.6. NDVI Patterns and Temporal Dynamics    72
4.7. Analysis of Land Surface Temperature    78
4.8 NDVI-LST Pearson Correlation Analysis (2018-2022)    85
4.9 Multiple Linear Regression Model Performance    86
4.10 Land cover Type Analysis and Thermal Characteristics    88
4.11 Model Validation and Statistical Confidence    89
Chapter 5. Discussion    92
5.1. Dynamics of Land Cover Change    92
5.2. Consequences for Ecosystem Services    93
5.3. Correlations between NDVI and LST and multiple linear regression model    95
5.4   Environmental Influences and Their limitations    98
Chapter 6. Conclusion    102
References    106




 
List of Figures
Figure 1: Map of study areas    6
Figure 2: A flowchart of the general methodology    42
Figure 3: Land cover classification 2018    58
Figure 4: Land cover classification 2019    59
Figure 5: Land cover classification 2020    59
Figure 6: Land cover classification 2021    60
Figure 7: Land cover classification 2022    60
Figure 8: Land cover composition    65
Figure 9: Land Cover Composition annual fluctuations and directional changes    66
Figure 10: Land Cover Change Quantification (2018-2022)    69
Figure 11: Land Cover Transition Analysis    71
Figure 12: Map of NDVI 2018    74
Figure 13: Map of NDVI 2019    74
Figure 14: Map of NDVI 2021    75
Figure 15: Map of NDVI 2020    75
Figure 16: Map of NDVI 2022    76
Figure 17: Temporal Trends in NDVI by Land cover types    78
Figure 18: Map of Land Surface Temperature 2018    81
Figure 19: Map of Land Surface Temperature 2019    81
Figure 20: Map of Land Surface Temperature 2020    82
Figure 21: Map of Land Surface Temperature 2021    82
Figure 22: Map of Land Surface Temperature 2022    83
Figure 23: Temporal Trends in Land Surface Temperature by land cover types    85
Figure 24: Correlation of NDVI, LST and landcover    86
Figure 25: Multiple Linear Regression Model    88
Figure 26: Model Validation    90

 

List of Tables
Table 1: Description of the data used    39
Table 2: Land cover classes    46
Table 3: Band numbers and specific wavelengths    49
Table 4: Rescaling and Thermal constants    50
Table 5: Classification Accuracy Assessment Results form 2018-2022    62
Table 6: Land Cover Change Quantification (2018-2022)    70



                                

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