Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis
Abstract
:1. Introduction
2. Methods and Materials
2.1. Study Area and Remote Sensing Data
2.2. NDVI
2.3. Landscape Matrices
2.4. Variogram
2.5. Conditional Latin hypercube
- Divide the quantile distribution of Z into n strata, and calculate the quantile distribution for each variable, . Calculate the correlation matrix of Z (C).
- Pick n random samples from N; calculate the correlation matrix of z (T).
- Calculate the objective function. The overall objective function combines to three components of the objective function (O1, O2, and O3). For general applications, all weightings are set to equal for all components of the objective function.
- For continuous variables,
- For categorical data, the objective function is to match the probability distribution for each class of
- To ensure that the correlation of the sampled variables will replicate the original data, another objective function is added:
- Perform an annealing schedule:M = exp [–ΔO/T], where ΔO is the change in the objective function, and T is a cooling temperature (between 0 and 1), which is decreased by a factor d during each iteration.
- Generate a uniform random number between 0 and 1. If rand < M, accept the new values; otherwise, discard changes.
- Try to perform changes: Generate a uniform random number rand. If rand < P, pick a sample randomly from z and swap it with a random site from unsampled sites r. Otherwise, remove the sample(s) from z that has the largest and replace it with a random site(s) from unsampled sites r. End when the value of P is between 0 and 1, indicating that the probability of the search is a random search or systematically replacing the samples that have the worst fit with the strata.
- Go to step 3. Repeat steps 3–7 until the objective function value falls beyond a given stop criterion or a specified number of iterations.
2.6. Sequential Gaussian Simulation
- Establish a random path that is visited once and only once, all nodes {xi, i = 1, Λ, N} discretizing the domain of interest Doman. A random visiting sequence ensures that no spatial continuity artifact is introduced into the simulation by a specific path visiting N nodes.
- At the first visited N nodes x1:
- Model, using either a parametric or nonparametric approach, the local ccdf of Z(x1) conditional on n original data {Z (xα), α = 1,Λ, n} FZ (x1; z1|(n)) = prob {Z (x1) ≤ z1|(n)}
- Generate, via the Monte Carlo drawing relation, a simulated value z(l)(x1) from this ccdf FZ (x1: z1|(n)), and add it to the conditioning data set, now of dimension n + 1, to be used for all subsequent local ccdf determinations.
- At the ith node xi along the random path:
- Model the local ccdf of Z(xi) conditional on n original data and the i − 1 near previously simulated values { z(l)(xi), j = 1,Λ, i − 1}:
- Generate a simulated value z(l)(xi) from this ccdf and add it to the conditioning data set, now of dimension n + i.
- Repeat step 3 until all N nodes along the random path are visited.
2.7. Moran’s I
3. Results and Discussion
3.1. Statistics and Spatial Analysis of NDVI Images
3.2. Simulation with Selected Samples for Multiple Images
4. Conclusions
Acknowledgments
References
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Name | Equation | Note |
---|---|---|
Number of patches (NP) | NP=ni | Patch size metrics |
Mean patch size (MPS) | Patch size metrics | |
Patch Size Standard Deviation (PSSD) | Patch size variability | |
Patch Size Coefficient of Variance (PScov) | Patch size variability | |
Edge Density (ED) | Edge metrics | |
Mean shape index (MSI) | Shape metrics | |
Mean nearest neighbor (MNN) | Diversity metrics |
Date | Mean | Std. | Min. | Max. | Skewness | Kurtosis |
---|---|---|---|---|---|---|
1996/11/08 | 0.36 | 0.04 | 0.11 | 0.48 | −0.98 | 1.45 |
1999/03/06 | 0.32 | 0.04 | 0.13 | 0.43 | −0.58 | 0.08 |
1999/10/31 | 0.14 | 0.07 | −0.22 | 0.33 | −1.23 | 1.35 |
2000/11/27 | 0.15 | 0.07 | −0.14 | 0.35 | −0.47 | −0.30 |
2001/11/20 | 0.37 | 0.05 | 0.03 | 0.50 | −1.03 | 1.34 |
2003/12/17 | 0.15 | 0.06 | −0.12 | 0.33 | −0.27 | 0.00 |
2004/11/19 | 0.35 | 0.06 | 0.05 | 0.54 | −0.44 | 0.07 |
Date | Model | Parameters | RSS | r2 | ||
---|---|---|---|---|---|---|
C0 (mg/kg)2 | C0+C(mg/kg)2 | R(m) | ||||
1996/11/08 | Exp. | 0.000138 | 0.001326 | 654 | 1.61E-08 | 0.953 |
1999/03/06 | Exp. | 0.000712 | 0.001814 | 4620 | 6.07E-08 | 0.945 |
1999/10/31 | Exp. | 0.000590 | 0.004440 | 564 | 1.68E-07 | 0.939 |
2000/11/27 | Exp. | 0.000186 | 0.004676 | 2646 | 2.47E-07 | 0.952 |
2001/11/20 | Exp. | 0.000121 | 0.002429 | 1281 | 5.62E-08 | 0.933 |
2003/12/17 | Exp. | 0.000126 | 0.003126 | 2298 | 1.57E-07 | 0.949 |
2004/11/19 | Exp. | 0.000116 | 0.003832 | 1680 | 1.19E-07 | 0.977 |
Samples | Date | Mean | Std | Min | Max | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|
100 | 1996/11/08 | 0.25 | 0.08 | −0.03 | 0.44 | −0.40 | −0.42 |
1999/03/06 | 0.24 | 0.07 | 0.00 | 0.41 | −0.42 | −0.27 | |
1999/10/31 | 0.02 | 0.04 | −0.12 | 0.28 | 0.45 | 0.52 | |
2000/11/27 | 0.10 | 0.04 | −0.06 | 0.29 | −0.13 | −0.23 | |
2001/11/20 | 0.02 | 0.05 | −0.13 | 0.46 | 1.64 | 5.51 | |
2003/12/17 | 0.08 | 0.07 | −0.12 | 0.29 | 0.17 | −0.33 | |
2004/11/19 | 0.26 | 0.06 | 0.07 | 0.49 | −0.08 | −0.59 | |
500 | 1996/11/08 | 0.36 | 0.05 | 0.11 | 0.52 | −0.23 | −0.07 |
1999/03/06 | 0.30 | 0.04 | 0.15 | 0.42 | −0.22 | −0.09 | |
1999/10/31 | 0.12 | 0.05 | −0.15 | 0.26 | −0.42 | −0.14 | |
2000/11/27 | 0.14 | 0.05 | −0.05 | 0.30 | −0.22 | −0.41 | |
2001/11/20 | 0.29 | 0.04 | 0.09 | 0.46 | −0.15 | 0.30 | |
2003/12/17 | 0.13 | 0.04 | −0.05 | 0.31 | −0.29 | −0.11 | |
2004/11/19 | 0.36 | 0.05 | 0.11 | 0.52 | −0.23 | −0.07 | |
1000 | 1996/11/08 | 0.35 | 0.05 | 0.10 | 0.49 | −0.83 | 1.55 |
1999/03/06 | 0.26 | 0.04 | 0.12 | 0.41 | −0.04 | 0.09 | |
1999/10/31 | 0.13 | 0.05 | −0.12 | 0.29 | −0.49 | 0.09 | |
2000/11/27 | 0.13 | 0.05 | −0.05 | 0.30 | −0.13 | −0.13 | |
2001/11/20 | 0.31 | 0.06 | 0.02 | 0.50 | −0.64 | 0.61 | |
2003/12/17 | 0.12 | 0.04 | −0.04 | 0.29 | −0.14 | −0.04 | |
2004/11/19 | 0.33 | 0.05 | 0.12 | 0.51 | −0.21 | −0.05 | |
2000 | 1996/11/08 | 0.37 | 0.04 | 0.14 | 0.52 | −0.56 | 0.77 |
1999/03/06 | 0.29 | 0.04 | 0.14 | 0.42 | −0.11 | −0.11 | |
1999/10/31 | 0.14 | 0.05 | −0.20 | 0.31 | −0.70 | 0.43 | |
2000/11/27 | 0.14 | 0.05 | −0.1 | 0.35 | −0.10 | −0.36 | |
2001/11/20 | 0.36 | 0.05 | 0.07 | 0.52 | −0.64 | 1.07 | |
2003/12/17 | 0.13 | 0.04 | −0.05 | 0.33 | −0.10 | −0.21 | |
2004/11/19 | 0.37 | 0.06 | 0.10 | 0.54 | −0.22 | −0.19 | |
3000 | 1996/11/08 | 0.37 | 0.04 | 0.18 | 0.49 | −0.57 | 0.65 |
1999/03/06 | 0.29 | 0.04 | 0.15 | 0.42 | −0.17 | −0.08 | |
1999/10/31 | 0.14 | 0.05 | −0.19 | 0.33 | −0.76 | 0.58 | |
2000/11/27 | 0.14 | 0.05 | −0.04 | 0.32 | −0.18 | −0.29 | |
2001/11/20 | 0.37 | 0.04 | 0.07 | 0.51 | −0.59 | 0.64 | |
2003/12/17 | 0.14 | 0.05 | −0.11 | 0.31 | −0.18 | −0.09 | |
2004/11/19 | 0.35 | 0.05 | 0.12 | 0.52 | −0.15 | −0.19 | |
5000 | 1996/11/08 | 0.37 | 0.04 | 0.15 | 0.48 | −0.58 | 0.60 |
1999/03/06 | 0.30 | 0.04 | 0.14 | 0.43 | −0.19 | −0.09 | |
1999/10/31 | 0.14 | 0.06 | −0.20 | 0.29 | −0.81 | 0.61 | |
2000/11/27 | 0.15 | 0.06 | −0.10 | 0.31 | −0.29 | −0.29 | |
2001/11/20 | 0.33 | 0.04 | 0.03 | 0.48 | −0.20 | 0.16 | |
2003/12/17 | 0.14 | 0.05 | −0.10 | 0.31 | −0.22 | −0.16 | |
2004/11/19 | 0.35 | 0.05 | 0.10 | 0.52 | −0.26 | −0.10 | |
7000 | 1996/11/08 | 0.37 | 0.04 | 0.12 | 0.49 | −0.67 | 0.81 |
1999/03/06 | 0.30 | 0.04 | 0.13 | 0.42 | −0.25 | −0.05 | |
1999/10/31 | 0.14 | 0.06 | −0.18 | 0.32 | −0.84 | 0.55 | |
2000/11/27 | 0.14 | 0.06 | −0.10 | 0.33 | −0.26 | −0.30 | |
2001/11/20 | 0.37 | 0.05 | 0.04 | 0.51 | −0.70 | 0.75 | |
2003/12/17 | 0.14 | 0.05 | −0.10 | 0.31 | −0.24 | −0.07 | |
2004/11/19 | 0.35 | 0.05 | 0.07 | 0.52 | −0.27 | −0.05 | |
10000 | 1996/11/08 | 0.37 | 0.04 | 0.16 | 0.48 | −0.69 | 0.77 |
1999/03/06 | 0.30 | 0.04 | 0.14 | 0.43 | −0.25 | −0.14 | |
1999/10/31 | 0.14 | 0.06 | −0.17 | 0.33 | −0.91 | 0.77 | |
2000/11/27 | 0.15 | 0.06 | −0.10 | 0.32 | −0.27 | −0.36 | |
2001/11/20 | 0.37 | 0.05 | 0.09 | 0.50 | −0.75 | 0.82 | |
2003/12/17 | 0.14 | 0.05 | −0.10 | 0.31 | −0.22 | −0.20 | |
2004/11/19 | 0.35 | 0.07 | 0.06 | 0.54 | −0.27 | −0.11 |
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Chu, H.-J.; Lin, Y.-P.; Huang, Y.-L.; Wang, Y.-C. Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis. Sensors 2009, 9, 6670-6700. https://doi.org/10.3390/s90906670
Chu H-J, Lin Y-P, Huang Y-L, Wang Y-C. Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis. Sensors. 2009; 9(9):6670-6700. https://doi.org/10.3390/s90906670
Chicago/Turabian StyleChu, Hone-Jay, Yu-Pin Lin, Yu-Long Huang, and Yung-Chieh Wang. 2009. "Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis" Sensors 9, no. 9: 6670-6700. https://doi.org/10.3390/s90906670