Land Use and Land Cover Change Patterns from Orbital Remote Sensing Products: Spatial Dynamics and Trend Analysis in Northeastern Brazil
Abstract
1. Introduction
2. Materials and Methods
2.1. Characterization of the Study Area
2.2. Processing, Statistical Tests and Classification of LULC Data (MapBiomas)
2.2.1. LULC Classification and Cloud Processing
2.2.2. Estimation of Spectral and Surface Indices, Predictor Variables and Their Statistical Reducers
2.2.3. Mann–Kendall and Sen’s Slope Trend Tests in LULC Data
2.3. TRMM_3B43v7 Data and Surface Data Validation
2.4. Data from Terra and Aqua Satellites (MODIS Sensor)
2.5. Multivariate Principal Component Analysis (PCA)
2.6. Statistical Analysis
3. Results and Discussion
3.1. Spatiotemporal Dynamics of LULC, Global Accuracy and Surface Albedo
3.2. Dynamics of Quantitative Variability of LULC Data
3.3. Trend Analysis from Mann–Kendall and Sen’s Slope
3.4. Multivariate Analysis: Principal Components (PC)
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Satellite | Multispectral Band | Band Description | Wavelength (μm) | Statistical Reducers per Band |
---|---|---|---|---|
Landsat-5 TM and Landsat-7 ETM+ | B1 | Blue | 0.45–0.52 | Minimum; Annual median; Dry season median; Rainy season median; Standard deviation |
B2 | Green | 0.52–0.60 | ||
B3 | Red | 0.63–0.69 | ||
B4 | Near infrared—NIR | 0.76–0.90 | ||
B5 | Shortwave infrared 1—SWIR1 | 1.55–1.75 | ||
B6 | Thermal infrared | 10.40–12.50 | ||
B7 | Shortwave infrared 2—SWIR2 | 2.08–2.35 | ||
Landsat-8 OLI/TIRS | B2 | Blue | 0.45–0.51 | |
B3 | Green | 0.53–0.59 | ||
B4 | Red | 0.64–0.67 | ||
B5 | Near infrared—NIR | 0.85–0.88 | ||
B6 | Shortwave infrared 2—SWIR1 | 1.57–1.65 | ||
B7 | Shortwave infrared 2—SWIR2 | 2.11–2.29 | ||
B10 | Thermal infrared | 10.60–11.19 |
Spectral Index | Equation | Source/Authors | Statistical Reducers per Band |
---|---|---|---|
Cellulose Absorption Index—CAI | CAI = SWIR2/SWIR1 | Nagler et al. [56] | Amplitude; Maximum; Minimum; Annual median; Dry season median; Rainy season median; Standard deviation |
Enhanced Vegetation Index 2—EVI 2 | EVI 2 = 2.5 × (NIR − Red)/(NIR + 2.4 × Red + 1) | Parente et al. [57] | |
Green Chlorophyll Vegetation Index—GCVI | GCVI = (NIR/Green − 1) | Burke et al. [58] | |
Hall Cover | Hall Cover = (– Red × 0.017 − NIR × 0.007 − SWIR2 × 0.079 + 5.22) | Hall et al. [59] | |
Normalized Difference Vegetation Index—NDVI | NDVI = (NIR − Red)/(NIR + Red) | Rouse et al. [60] | |
Normalized Difference Water Index—NDWI | NDWI = (NIR − SWIR1)/(NIR + SWIR1) | Gao et al. [61] | |
Normalized Difference Fraction Index—NDFI | NDFI = (GV − (NPV + SOIL))/(GV + NPV + SOIL) | USGS | |
Photochemical Reflectance Index—PRI | PRI = (Blue − Green)/(Blue + Green) | Gamon et al. [62] | |
Soil-Adjusted Vegetation Index—SAVI | SAVI = 1.5 × (NIR − Red)/(NIR + Red + 0.5) | Huete et al. [63] | |
Surface Index | Equation | Source/Authors | Statistical reducers per band |
Hall’s Forest Cover | HFC = −0.017 × RED − 0.007 × NIR − 0.079 × SWIR2 + 5.22 | - | Annual median; Dry season median; Rainy season median |
Hall’s Forest Height | HFH = −0.039 × RED − 0.011 × NIR − 0.026 × SWIR1 + 4.13 | - | |
Index/Fraction | Equation | Source/Authors | Statistical reducers per band |
Green Vegetation Fraction—GV | GV = Fractional abundance of green vegetation within the pixel | Souza et al. [64] | Amplitude; Maximum; Minimum; Annual median; Dry season median; Rainy season median; Standard deviation |
Green Vegetation Shade Fraction—GVS | GVS = GV/(GV + NPV + Soil + Cloud) | Housman et al. [65] | |
Normalized Difference Fraction Index—NDFI | NDFI = (GVS − (NPV + Soil))/(GVS + (NPV + Soil)) | Souza et al. [64] | |
Non-photosynthetic Vegetation Fraction—NPV | NPV = Fractional abundance of non-photosynthetic vegetation within the pixel | Souza et al. [64] | |
Savanna Ecosystem Fraction Index—SEFI | SEFI = (GV + NPV_S − Soil)/(GV + NPV_S + Soil) | Alencar et al. [66] | |
Shade Fraction—Shade | Shade = 100 − (GV + NPV + Soil + Cloud) | Housman et al. [65] | |
Soil Fraction—Soil) | Soil = Fractional abundance of soil within the pixel | Souza et al. [64] | |
Wetland Ecosystem Fraction Index—WEFI | WEFI = ((GV + NPV) − (Soil + Shade))/((GV + NPV)) + (Soil + Shade)) | Rosa [67] | |
Spectral Mixture Analysis | Equation | Source/Authors | Statistical reducers per band |
Green Vegetation | GV = SMA(GV) × 100 | Fraction from SMA | Annual median; Standard deviation |
Soil | SOIL = SMA(Soil) × 100 | ||
Forest/Non-Forest Index | FNS = 100 × (GVshade − SOIL)/(GVshade + SOIL) + 100 | Adapted from NDFI | |
GVshade = GV + |GV + NPV + SOIL − 100| | |||
Scaled Water-Enhanced Forest Index | WEFI = ((GV + NPV) − (SOIL + SHADE))/((GV + NPV) + (SOIL + SHADE)) × 100 + 100 | Fraction from SMA | |
Land Slope | Global digital surface model: 30 m | Tadono et al. [68] |
Band/MODIS | Wavelength | Spatial Resolution (Meters) | Temporal Resolution (Days) | Radiometric Resolution (Bits) | Processing Level | Multiplier Factor (for Each Band) |
---|---|---|---|---|---|---|
rBLUE | 0.459–0.479 µm | 500 | 8 | 16 | L3 | 0.0001 |
rGREEN | 0.545–0.565 µm | |||||
rRED | 0.620–0.670 µm | |||||
rNIR1 | 0.841–0.876 µm | |||||
rNIR2 | 1.230–1.250 µm | |||||
rSWIR1 | 1.628–1.652 µm | |||||
rSWIR2 | 2.105–2.155 µm |
Biome/Collection | Global Accuracy (%) | Area Disagreement (%) | Allocation Disagreement (%) |
---|---|---|---|
Caatinga | 81.8 | 3.5 | 14.7 |
Cerrado | 83.8 | 4.9 | 11.3 |
Atlantic Forest | 90.7 | 2.0 | 7.3 |
Amazon | 97.6 | 0.8 | 1.6 |
Collection 5 | 91.0 | 2.0 | 7.0 |
Inclusion Errors | |||||
---|---|---|---|---|---|
Mapped Class | Forest | Non-Forest Natural | Farming | Non-Vegetated Area | Water |
Forest | 0.93 | −0.02 | −0.04 | −0.0002 | −0.001 |
Non-forest natural | −0.17 | 0.70 | −0.12 | −0.001 | −0.005 |
Farming | −0.05 | −0.03 | 0.91 | −0.003 | −0.0009 |
Non-vegetated area | −0.01 | −0.0052 | −0.03 | 0.95 | 0.00 |
Water | −0.01 | −0.04 | −0.006 | −0.00002 | 0.94 |
Errors of omission | |||||
Forest | 0.96 | −0.02 | −0.02 | −0.00006 | −0.0002 |
Non-forest natural | −0.19 | 0.68 | −0.12 | −0.0002 | −0.009 |
Farming | −0.11 | −0.03 | 0.85 | −0.0005 | −0.00035 |
Non-vegetated area | −0.02 | −0.02 | −0.20 | 0.76 | −0.00007 |
Water | −0.05 | −0.02 | −0.01 | 0.00 | 0.91 |
Year | Forest | Non-Forest Natural | Farming | Non-Vegetated Area | Water | Non Observed | Rainfall (TRMM) |
---|---|---|---|---|---|---|---|
Total Area of Classes: 1,553,402 km2 | mm Year−1 | ||||||
2000 | 981,577.33 | 90,468.03 | 456,363.72 | 9826.71 | 15,050.78 | 115.41 | 1261 |
2001 | 977,099.95 | 91,018.30 | 459,750.94 | 10,732.90 | 14,685.71 | 114.19 | 937 |
2002 | 968,541.36 | 90,650.87 | 469,107.64 | 10,865.84 | 14,121.95 | 114.33 | 1069 |
2003 | 961,045.17 | 88,843.80 | 477,413.53 | 10,749.36 | 15,234.77 | 115.35 | 925 |
2004 | 956,590.84 | 86,963.71 | 480,788.55 | 10,788.43 | 18,154.12 | 116.35 | 1238 |
2005 | 956,164.28 | 85,964.77 | 482,273.70 | 11,083.97 | 17,798.70 | 116.51 | 1066 |
2006 | 950,886.47 | 83,349.65 | 490,099.75 | 10,972.04 | 17,977.24 | 116.84 | 1108 |
2007 | 945,015.92 | 84,166.35 | 495,581.57 | 10,915.87 | 17,605.67 | 116.61 | 866 |
2008 | 942,529.53 | 83,239.95 | 498,061.55 | 11,188.72 | 18,265.55 | 116.69 | 1142 |
2009 | 940,718.53 | 82,260.09 | 500,258.79 | 11,490.75 | 18,556.61 | 117.22 | 1266 |
2010 | 941,757.29 | 83,572.40 | 498,390.82 | 11,757.79 | 17,807.39 | 116.30 | 930 |
2011 | 939,495.92 | 84,562.89 | 500,277.41 | 11,580.49 | 17,370.16 | 115.11 | 1188 |
2012 | 929,616.57 | 87,419.77 | 508,230.95 | 11,613.88 | 16,407.51 | 113.32 | 651 |
2013 | 925,884.22 | 87,341.30 | 512,643.20 | 12,127.84 | 15,293.59 | 111.84 | 950 |
2014 | 919,758.51 | 84,463.21 | 521,930.22 | 11,946.48 | 15,190.72 | 112.85 | 938 |
2015 | 918,074.91 | 83,524.76 | 525,599.75 | 12,114.83 | 13,973.08 | 114.67 | 731 |
2016 | 913,452.63 | 83,687.29 | 529,657.71 | 12,576.51 | 13,911.23 | 116.62 | 848 |
2017 | 914,847.99 | 84,653.53 | 527,286.50 | 12,359.62 | 14,135.78 | 118.55 | 921 |
2018 | 915,881.56 | 85,401.19 | 523,671.08 | 13,449.02 | 14,820.50 | 178.63 | 1011 |
2019 | 903,850.95 | 84,122.09 | 536,750.41 | 13,334.88 | 15,224.77 | 118.883 | 953 |
Descriptive and dispersion statistics | |||||||
Minimum | 903,850.95 | 82,260.09 | 456,363.72 | 9826.72 | 13,911.23 | 111.84 | 651.00 |
Mean | 940,139.50 | 85,783.70 | 499,706.89 | 11,573.80 | 16,079.30 | 118.81 | 999.95 |
Maximum | 981,577.33 | 91,018.31 | 536,750.41 | 13,449.03 | 18,556.62 | 178.63 | 1266.00 |
1 SD | 22,332.43 | 2696.57 | 23,723.34 | 910.82 | 1670.97 | 116.33 | 951.50 |
2 CV (%) | 2.38 | 3.14 | 4.75 | 7.87 | 10.39 | 14.19 | 168.03 |
LULC Data and Rainfall Data | Mann–Kendall Trend Test | |||
---|---|---|---|---|
ZMK-Value | Tau | Sen’s Slope | p-Value | |
Forest | −5.872 | −0.958 | −3705.853 | 4.29 × 10−9 ** |
Non-forest natural | −1.914 | −0.316 | −277.263 | 0.055 ns |
Farming | 5.807 | 0.947 | 3978.898 | 6.34 × 10−9 ** |
Non-vegetated area | 5.418 | 0.884 | 137.084 | 6.02 × 10−8 ** |
Water | −1.330 | −0.221 | −93.586 | 0.183 ns |
Rainfall/TRMM | −1.460 | −0.242 | −11.878 | 0.144 ns |
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Silva, J.L.B.d.; Silva, M.V.d.; Lopes, P.M.O.; Santos, R.C.; Carvalho, A.A.d.; Moura, G.B.d.A.; Silva, T.G.F.d.; Cézar Bezerra, A.; Jardim, A.M.d.R.F.; Ferreira, M.B.; et al. Land Use and Land Cover Change Patterns from Orbital Remote Sensing Products: Spatial Dynamics and Trend Analysis in Northeastern Brazil. Land 2025, 14, 1954. https://doi.org/10.3390/land14101954
Silva JLBd, Silva MVd, Lopes PMO, Santos RC, Carvalho AAd, Moura GBdA, Silva TGFd, Cézar Bezerra A, Jardim AMdRF, Ferreira MB, et al. Land Use and Land Cover Change Patterns from Orbital Remote Sensing Products: Spatial Dynamics and Trend Analysis in Northeastern Brazil. Land. 2025; 14(10):1954. https://doi.org/10.3390/land14101954
Chicago/Turabian StyleSilva, Jhon Lennon Bezerra da, Marcos Vinícius da Silva, Pabrício Marcos Oliveira Lopes, Rodrigo Couto Santos, Ailton Alves de Carvalho, Geber Barbosa de Albuquerque Moura, Thieres George Freire da Silva, Alan Cézar Bezerra, Alexandre Maniçoba da Rosa Ferraz Jardim, Maria Beatriz Ferreira, and et al. 2025. "Land Use and Land Cover Change Patterns from Orbital Remote Sensing Products: Spatial Dynamics and Trend Analysis in Northeastern Brazil" Land 14, no. 10: 1954. https://doi.org/10.3390/land14101954
APA StyleSilva, J. L. B. d., Silva, M. V. d., Lopes, P. M. O., Santos, R. C., Carvalho, A. A. d., Moura, G. B. d. A., Silva, T. G. F. d., Cézar Bezerra, A., Jardim, A. M. d. R. F., Ferreira, M. B., Silva, P. C., Silva, J. A. O. S., Mesquita, M., Batista, P. H. D., Jordan, R. A., & Oliveira, H. F. E. d. (2025). Land Use and Land Cover Change Patterns from Orbital Remote Sensing Products: Spatial Dynamics and Trend Analysis in Northeastern Brazil. Land, 14(10), 1954. https://doi.org/10.3390/land14101954