Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Modeling LULC Change
2.3. Data Issues
LULC Class Nomenclature Definition
2.4. Parameterization of the LULC Simulation Model
2.5. Validation of the Model
2.6. Analysis of LULC Changes
2.7. Assessment of the Effectiveness of the Chapecó River EC as a Public Policy
3. Results and Discussions
3.1. Model Validation and LULC Simulation for 2036
3.2. LULC Changes
3.2.1. Spatial and Temporal Evolution of Land Use and Land Cover
3.2.2. LULC Changes and the Main Systematic Transitions Observed between the Years 2000 and 2018
3.2.3. LULC Changes and the Main Simulated Systematic Transitions for the Year 2036
3.3. Effectiveness of the Chapecó River EC as an Environmental Management Tool
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Data Source | Variables (Quantity) | Format | Year |
---|---|---|---|
MapBiomas [69] | 1 | raster | 2000 and 2018 |
Center for Environmental Resources Information and Hydrometeorology-Epagri/Ciram [68] | 2 | raster | 2002 |
Embrapa [66] | 1 | raster | 2004 |
NIMA/NASA [73] | 2 | raster | 2000 |
OSM/IBGE [74,75] | 1 | vector | 2018 |
Agricultural Census/IBGE [61] | 10 | tabular | 2006 and 2017 |
Demographic Census/IBGE [61] | 2 | tabular | 2000 and 2010 |
Municipal Livestock Survey-PPM/IBGE [61] | 4 | tabular | 2000 and 2018 |
Municipal agricultural production-PAM/IBGE [61] | 4 | tabular | 2002 and 2017 |
Production of Vegetable Extraction and Forestry-PEVS/IBGE [61] | 1 | tabular | 2000 and 2018 |
Gross Domestic Product of the Municipality/IBGE [61] | 2 | tabular | 2000 and 2018 |
Population estimate/IBGE [61] | 1 | tabular | 2000 and 2018 |
Center of Socioeconomics and Agricultural Planning-Epagri/Cepa [76] | 1 | tabular | 2000 and 2018 |
Atlas of Human Development of Brazil/UNDP [77] | 1 | tabular | 2000 and 2010 |
Annual Social Information Report—RAIS/Ministry of Economy [63] | 4 | tabular | 2006 and 2018 |
Dimension | Driving Forces |
---|---|
Physical/natural | Land use and land cover |
Temperature | |
Accumulated precipitation | |
Type of soil | |
Type of relief | |
Altimetry | |
Economic | Road network |
Rural agribusiness | |
Cattle herd | |
Swine Herd | |
Chicken Herd | |
Formal employment—commerce | |
Formal employment—industry | |
Formal employment—agriculture | |
Financing—Pronaf | |
Processing industries | |
Corn yield | |
Soybean yield | |
Bean yield | |
Tobacco yield | |
Gross Domestic Product—GDP | |
Agricultural land price | |
Per capita income | |
Log Production | |
Gross value added of agriculture and cattle raising | |
Milk production value | |
Social | Family agriculture |
Land structure | |
Schooling of the head farmer | |
Age of the head farmer | |
Human Development Index—HDI | |
Rural workers | |
Technology | Use of agrochemicals |
Mechanization in the rural property | |
Technical orientation | |
Population | Population density |
Rural population |
LULC Class | Description |
---|---|
Forest (forest formation) | Dense, open, and mixed ombrophilous forest, semi-deciduous and deciduous seasonal forest, and pioneer formation. |
Silviculture (forest plantation) | Planted tree species for commercial use (e.g., eucalyptus, pinus and araucaria). |
Grassland | Savannahs, park and grassland steppe savannahs, steppe and shrub, and herbaceous pioneers (natural fields). |
Pasture | Pasture areas, natural or planted, related to the farming activity. |
Agriculture (annual and perennial crop) | Areas predominantly occupied with annual crops (short to medium-term crops, usually with a vegetative cycle of less than one year, that has to be re-planted after harvest) and in some regions with perennial crops (areas occupied with crops with a long cycle (more than one year), which allow successive harvests without the need for a new crop). |
Mosaic (mosaic of agriculture and pasture) | Farming areas where it was not possible to distinguish between pasture and agriculture. |
Artificial Area (urban infrastructure + other non-vegetated area) | Urban infrastructure: urban areas with a predominance of non-vegetated surfaces, including roads, highways and constructions, and other non-vegetated area non-permeable surface areas (infrastructure, urban expansion or mining) not mapped into their classes and regions of exposed soil in natural or crop areas. |
Water bodies (river, lake and ocean) | rivers, lakes, dams, reservoirs and other water bodies. |
Parameter | Parameterization Object | Parameterization Adopted | |||||||
---|---|---|---|---|---|---|---|---|---|
Constants parameters | Input layer | Independent variables | 67 | ||||||
Rescaling method | Normalized | ||||||||
Hidden layer | Activation function | Hyperbolic tangent | |||||||
Output layer | Dependent variable | LULC 2018 | |||||||
Activation function | Softmax | ||||||||
Error function | Cross-entropy | ||||||||
ANN Models | |||||||||
ANN1 | ANN2 | ANN3 | ANN4 | ANN5 | ANN6 | ANN7 | ANN8 | ||
Parameters | Partitions | 7-2-1 | 6-2-2 | 7-2-1 | 6-2-2 | 7-2-1 | 6-2-2 | 7-2-1 | 6-2-2 |
Hidden layer | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | |
Neurons | 56 | 56 | 56 | 56 | 49-49 | 49-49 | 49-49 | 49-49 | |
Iterations | 500 | 500 | 1000 | 1000 | 500 | 500 | 1000 | 1000 |
Change Metrics | Description |
---|---|
Persistence (Pjj) | Percentage of LULC class area that did not change over the time interval considered (diagonal of the transition matrix). |
Gain (Gj) | Difference of the total value of each LULC class from the final time (P+j) and the persistence value (Pjj). |
Loss (Lj) | Difference of the total value of each LULC class from the initial time (Pj+) and the persistence value (Pjj). |
Total change (Cj) | Sum of the gain (Gj) and loss (Lj) of each LULC class. |
Swap (Sj) | Swap trend: twice whichever presents the smaller value (gain or loss), for each LULC. |
Net change (Dj) | Absolute value of the area difference for each class at the final time and at the initial time. |
Gain-to-loss (G/L) | Proportion of gain compared to loss. |
Loss-to-persistence (Lp) | Proportion of loss compared to persistence. |
Gain-to-persistence (Gp) | Proportion of gain compared to persistence. |
ANN Models | |||||||||
---|---|---|---|---|---|---|---|---|---|
ANN1 | ANN2 | ANN3 | ANN4 | ANN5 | ANN6 | ANN7 | ANN8 | ||
Results | Cross-entropy error | 133,961.6 | 134,384.8 | 135,483.9 | 134,766.5 | 134,606.6 | 134,151.7 | 134,429.9 | 134,039.8 |
Percent Correct | 67.1 | 67.1 | 67.1 | 67.1 | 67.1 | 66.8 | 67.0 | 67.1 | |
Training time | 0:15:14.9 | 0:16:12.2 | 0:14:51.0 | 0:17:13.1 | 0:28:17.8 | 0:21:27.4 | 0:27:29.9 | 0:22:06.2 |
LULC Class | 2000 | 2018 | 2036 |
---|---|---|---|
Forest | 2418.36 | 2097.62 | 2418.25 |
Silviculture | 315.60 | 914.02 | 422.88 |
Grassland | 350.55 | 148.99 | 34.22 |
Pasture | 1699.32 | 886.34 | 1306.09 |
Agriculture | 1787.50 | 2416.28 | 2637.90 |
Mosaic | 614.54 | 677.71 | 363.66 |
Artificial Area | 30.70 | 50.14 | 31.65 |
Water bodies | 25.76 | 51.23 | 27.68 |
Total | 7242.33 | 7242.33 | 7242.33 |
2018 | |||||||||
---|---|---|---|---|---|---|---|---|---|
LULC Class | Forest | Silviculture | Grassland | Pasture | Agriculture | Mosaic | Artificial Area | Water Bodies | |
2000 | Forest | 25.85 ** | 3.42 * | 0.00 | 0.77 | 1.74 | 1.42 | 0.02 | 0.17 |
Silviculture | 0.07 | 4.25 ** | 0.00 | 0.01 | 0.01 | 0.01 | 0.00 | 0.01 | |
Grassland | 0.05 | 0.60 | 1.51 ** | 0.52 | 1.96 | 0.18 | 0.00 | 0.01 | |
Pasture | 1.71 | 2.93 | 0.41 | 8.59 ** | 5.46 * | 4.17 * | 0.09 | 0.10 | |
Agriculture | 0.24 | 0.60 | 0.13 | 1.22 | 21.54 ** | 0.77 | 0.10 | 0.08 | |
Mosaic | 1.03 | 0.82 | 0.00 | 1.10 | 2.65 | 2.78 ** | 0.09 | 0.03 | |
Artificial Area | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.40 ** | 0.00 | |
Water bodies | 0.01 | 0.00 | 0.00 | 0.02 | 0.00 | 0.01 | 0.00 | 0.31 ** |
LULC Class | Pj | Gj | Lj | Cj | Sj | Dj | G/L | Lp | Gp |
---|---|---|---|---|---|---|---|---|---|
Forest | 25.85 | 3.11 | 7.54 | 10.66 | 6.23 | 4.43 | 0.41 | 0.29 | 0.12 |
Silviculture | 4.25 | 8.37 | 0.11 | 8.48 | 0.22 | 8.26 | 76.18 | 0.03 | 1.97 |
Grassland | 1.51 | 0.55 | 3.33 | 3.87 | 1.09 | 2.78 | 0.16 | 2.20 | 0.36 |
Pasture | 8.59 | 3.65 | 14.87 | 18.52 | 7.29 | 11.23 | 0.25 | 1.73 | 0.42 |
Agriculture | 21.54 | 11.82 | 3.14 | 14.96 | 6.28 | 8.68 | 3.77 | 0.15 | 0.55 |
Mosaic | 2.78 | 6.58 | 5.71 | 12.29 | 11.42 | 0.87 | 1.15 | 2.05 | 2.37 |
Artificial Area | 0.40 | 0.29 | 0.03 | 0.32 | 0.05 | 0.27 | 11.13 | 0.07 | 0.74 |
Water bodies | 0.31 | 0.39 | 0.04 | 0.43 | 0.08 | 0.35 | 9.58 | 0.13 | 1.25 |
Total | 65.23 | 34.77 | 34.77 | 34.77 | 16.33 | 18.44 |
Inter-Class LULC Transitions in Terms of Gains (2000–2018) | |||
---|---|---|---|
Transition | Observed minus expected (%) | Difference divided by expected | Interpretation of systematic transition |
Forest in 2000 and Silviculture in 2018 | 0.50 | 0.17 | When silviculture gains, it replaces forest |
Forest in 2000 and Agriculture in 2018 | −3.50 | −0.67 | When agriculture gains, it does not replace forest |
Grassland in 2000 and Agriculture in 2018 | 1.20 | 1.58 | When agriculture gains, it replace grassland |
Pasture in 2000 and Mosaic in 2018 | 2.48 | 1.47 | When mosaic gains, it replacespasture |
Inter-Class LULC Transitions in Terms of Losses (2000–2018) | |||
Transition | Observed minus expected (%) | Difference divided by expected | Interpretation of systematic transition |
Forest in 2000 and Silviculture in 2018 | 2.08 | 1.55 | When forest loses, silviculture replaces it |
Forest in 2000 and Agriculture in 2018 | −1.80 | −0.51 | When forest loses, agriculture does not replace it |
Altitude fields in 2000 and Agriculture in 2018 | 0.83 | 0.73 | When grassland loses, agriculture replaces it |
Pasture in 2000 and Mosaic in 2018 | 2.58 | 1.63 | When pasture loses, mosaic replaces it |
2036 | |||||||||
---|---|---|---|---|---|---|---|---|---|
LULC Class | Forest | Silviculture | Grassland | Pasture | Agriculture | Mosaic | Artificial Area | Water Bodies | |
2018 | Forest | 25.85 ** | 0.27 | 0.01 | 1.30 | 0.87 | 0.64 | 0.00 | 0.02 |
Silviculture | 3.42 * | 4.78 ** | 0.04 | 2.27 | 1.58 | 0.52 | 0.00 | 0.00 | |
Grassland | 0.00 | 0.00 | 0.19 ** | 0.39 | 1.47 | 0.00 | 0.00 | 0.00 | |
Pasture | 0.77 | 0.30 | 0.06 | 7.13 ** | 3.19 * | 0.75 | 0.01 | 0.02 | |
Agriculture | 1.74 | 0.20 | 0.16 | 3.59 * | 26.50 ** | 1.16 | 0.00 | 0.00 | |
Mosaic | 1.42 | 0.27 | 0.02 | 3.20 * | 2.55 | 1.87 ** | 0.02 | 0.01 | |
Artificial Area | 0.02 | 0.00 | 0.00 | 0.06 | 0.15 | 0.06 | 0.40 ** | 0.00 | |
Water bodies | 0.17 | 0.01 | 0.00 | 0.08 | 0.12 | 0.01 | 0.00 | 0.32 ** |
LULC Class | Pj | Gj | Lj | Cj | Sj | Dj | G/L | Lp | Gp |
---|---|---|---|---|---|---|---|---|---|
Forest | 25.85 | 7.54 | 3.12 | 10.66 | 6.23 | 4.43 | 2.42 | 0.12 | 0.29 |
Silviculture | 4.78 | 1.06 | 7.84 | 8.89 | 2.11 | 6.78 | 0.13 | 1.64 | 0.22 |
Grassland | 0.19 | 0.29 | 1.87 | 2.16 | 0.57 | 1.58 | 0.15 | 10.09 | 1.55 |
Pasture | 7.13 | 10.90 | 5.11 | 16.01 | 10.21 | 5.80 | 2.14 | 0.72 | 1.53 |
Agriculture | 26.50 | 9.92 | 6.86 | 16.78 | 13.72 | 3.06 | 1.45 | 0.26 | 0.37 |
Mosaic | 1.87 | 3.15 | 7.49 | 10.64 | 6.31 | 4.34 | 0.42 | 4.01 | 1.69 |
Artificial Area | 0.40 | 0.04 | 0.29 | 0.33 | 0.07 | 0.26 | 0.12 | 0.72 | 0.09 |
Water bodies | 0.32 | 0.07 | 0.39 | 0.46 | 0.13 | 0.33 | 0.17 | 1.23 | 0.21 |
Total | 67.04 | 32.96 | 32.96 | 32.96 | 19.68 | 13.28 |
Inter-Class LULC Transitions in Terms of Gains (2018–2036) | |||
---|---|---|---|
Transition | Observed minus expected (%) | Difference divided by expected | Interpretation of systematic transition |
Silviculture in 2018 and Forest in 2036 | 2.08 | 1.55 | When forest gains, it replaces silviculture |
Forest in 2018 and Agriculture in 2036 | −3.44 | −0.80 | When agriculture gains, it does not replace forest |
Grassland in 2018 and Agriculture in 2036 | 1.17 | 3.81 | When agriculture gains, it replaces grassland weak |
Mosaic in 2018 and Pasture in 2036 | 2.04 | 1.75 | When pasture gains, it replaces mosaic |
Inter-Class LULC Transitions in Terms of Losses (2018–2036) | |||
Transition | Observed minus expected (%) | Difference divided by expected | Interpretation of systematic transition |
Silviculture in 2018 and Forest in 2036 | 0.64 | 0.23 | When silviculture loses, forest replaces it. This signal is weak |
Forest in 2018 and Agriculture in 2036 | −0.83 | −0.49 | When forest loses, agriculture does not replace it |
Grassland in 2018 and Agriculture in 2036 | 0.79 | 1.15 | When grassland loses, agriculture replaces it |
Mosaic in 2018 and Pasture in 2036 | 1.78 | 1.25 | When mosaic loses, pasture replaces it |
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Souza, J.M.d.; Morgado, P.; Costa, E.M.d.; Vianna, L.F.d.N. Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil. Sustainability 2022, 14, 4038. https://doi.org/10.3390/su14074038
Souza JMd, Morgado P, Costa EMd, Vianna LFdN. Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil. Sustainability. 2022; 14(7):4038. https://doi.org/10.3390/su14074038
Chicago/Turabian StyleSouza, Juliana Mio de, Paulo Morgado, Eduarda Marques da Costa, and Luiz Fernando de Novaes Vianna. 2022. "Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil" Sustainability 14, no. 7: 4038. https://doi.org/10.3390/su14074038