Semi-Automated Sample-Based Forest Degradation Monitoring with Photointerpretation of High-Resolution Imagery
Abstract
:1. Introduction
- Classification systems often more closely align with information needs than those associated with maps made with classified satellite imagery. The short reacquisition cycle of many high-resolution imagery sources (for example, NAIP is reacquired every 3–5 years) also allows for the consistent monitoring of individual points with the same classification system and protocols.
- The use of a survey sampling estimation paradigm leverages estimators and error metrics like confidence intervals with which decision-makers are accustomed to making decisions. Generally speaking, uncertainty metrics provided with most remote sensing maps cannot be viewed through the lens of survey sampling theory.
- PI plot networks can be interwoven with existing ground plot networks, creating opportunities for harmonization of ground- and PI-based reporting cycles, definitions, and estimation tools. For example, if a ground plot-based inventory cycle is 10 years, PI can be conducted over the same area every 5 years to provide mid-cycle updates of certain attributes that can be perceived on imagery.
- Efficient, low-cost protocols can be designed using less specialized knowledge and training than that which is typically needed for production mapping, something appealing to many REDD participants.
2. Materials and Methods
2.1. Study Sites and Sample Design
2.2. Plot Design
2.3. Fragmentation and Degradation Indicators
2.4. Data Collection Methods and Metric Calculation for the MD and PG Study Areas
2.4.1. PI and Subpopulation-Level Estimation Procedures
2.4.2. Variance Estimation for PAI for the MD and PG Study Areas
2.4.3. Comparison with Traditional Raster Data and Methods for the PG Study Area
2.5. Data Collection Methods, Metric Calculation, and Estimation Procedures for the SC Study Area
3. Results and Discussion
3.1. PG and MD Study Areas
3.1.1. PAI and NLCD Metrics in the PG Study Area
3.1.2. Temporal Differences in PAI across Different Scales and Historical Housing Permit Densities in the MD Study Area
3.2. Sample-Based Estimates of Metrics and Change in the SC Study Area
3.3. Additional Considerations for Landscape Metrics and Sample-Based Estimation
3.3.1. The Validity of Fragmentation Metrics and Functional Significance
3.3.2. Costs
3.3.3. Applications
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Metric | Subset of Plots Used | Equation | Description |
---|---|---|---|
Point Aggregation Index (PAI) 1 | Plots with at least 1 forested point (n = 1602) | The number of forest-forest point adjacencies divided by the total number of adjacencies between forest and any other type (including forest) in the estimation area. Calculated with Aij, an i × j (i = j = 1…m points in the estimation area) adjacency matrix with binary elements (1= a forested point adjacent to another forested point, 0 otherwise) and Bij, an i × j adjacency matrix with binary elements (1 = a forested point adjacent to any point type, 0 otherwise). Values can range from 0 (the case where there are no adjacent forest points) to 1 (the case where all points are forest). | |
TSP distance (TSPd) | Plots with at least 2 forested points (n = 1599) | The shortest path distance (meters) calculated using an algorithmic solution to what is known as the Traveling Salesperson Problem (TSP), farthest insertion method [48] found within the R package TSP [49]. di = the ith member of the set D of interpoint distances leading to the shortest overall distance between all forested points on the plot, returning to the first point. Values range from the minimum distance between two adjacent points (the case where there are 2 adjacent forested points on the plot; 12 m in the case of SC) to the sum of the shortest path roundtrip distance between all points on the plot (the case where all points on the plot are forest; approximately 675 m in the case of SC). | |
Relative TSP distance (rTSPd) | Plots with at least 2 forested points (n = 1599) | Relative (average per point) distance along the shortest path between all forested points on the plot, returning to point 1. k = the number of forested points in the plot. Values range from ½ the distance between 2 adjacent points (the case where there are k = 2 adjacent forested points on the plot) to 1/kth of the sum of the shortest path roundtrip distance between all points on the plot (the case where all points on the plot are forest; in the case of SC, k = 52). | |
Nearest Neighbour distance (NNd) | Plots with at least 2 forested points (n = 1599) | Nearest neighbor distance, calculated as the nonzero minimum of the i × j (i = j = 1…k forested points on the plot) distance matrix C with elements comprised of geographic distance between forested points i and j. Values range from the minimum distance between two adjacent points (the case where there are k = 2 adjacent forested points on the plot; 12 m in the case of SC) to the maximum distance between 2 points on the plot (the case where there are k = 2 forest points located at the extreme ends of the plot; approximately 82 m in the case of SC). | |
Mean Interpoint Distance (MId) | Plots with at least 2 forested points (n = 1599) | The average distance between forested points on the plot, calculated as the mean of the i × j (i = j = 1…k forested points on the plot) distance matrix D with elements comprised of geographic distance between forested points i and j. Values range from ½ the distance between 2 adjacent points (the case where there are 2 adjacent forested points on the plot) to ½ the distance between the 2 points located at extreme ends of the plot (the case where there are 2 forested points located at the extreme ends of the plot). | |
Number of f-f point adjacencies (NA) | Plots with at least 1 forested point (n = 1602) | The number of forest-forest point adjacencies on the plot, summarized from Aij, the i × j (i = j = 1…m points in the plot) adjacency matrix with binary elements (1= a forested point adjacent to another forested point, 0 otherwise). Values range from 0 (the case where there are no forest-forest adjacencies) to the total count of possible point to point adjacencies on the plot (the case where all points are forest; 170 m in the case of SC). | |
Relative number of adjacencies (rNA) | Plots with at least 1 forested point (n = 1602) | The number of forest-forest point adjacencies divided by the total number of possible adjacencies of any type on the plot, calculated with NA and Dij, the i × j (i = j = 1…m points in the plot) adjacency matrix obtained when all points are a forest. Values range between 0 (the case where there are no forest-forest adjacencies) and 1 (the case where all points are forest). | |
Mean Forest Proportion 18-m window (MFP18) | All plots (n = 2099) | Mean of the proportions of forested points surrounding each of the m points on the plot. pl = the proportion of forest points around point l. Values range from 0 (the case where there are no forested points on the plot) to 1 (the case where all points on the plot are forest). The radius of the buffer used will vary by plot design (for the SC area, 18 m was chosen). | |
SD Forest Proportion 18-m window (SDFP18) | All plots (n = 2099) | The standard deviation of the proportions of forested points surrounding each of the m points on the plot. Values range from 0 (all points are either forest or nonforest) to a maximum value of approximately 0.5 (when half of the points on the plot are surrounded by forest). |
Original | Degraded | Change (Degraded − Original) | |||||
---|---|---|---|---|---|---|---|
Metric | Mean | SE | Mean | SE | Mean | SE | CI Half-Width |
PAI | 0.84 | 0.0055 (0.7%) | 0.82 | 0.0054 (0.7%) | −0.02 | 0.0006 (4%) | 0.001 |
TSPd | 528.46 | 4.2896 (0.8%) | 525.25 | 4.2727 (0.8%) | −3.21 | 0.2075 (6.5%) | 0.407 |
rTSPd | 12.83 | 0.0354 (0.3%) | 12.88 | 0.0356 (0.3%) | 0.05 | 0.0061 (11.6%) | 0.012 |
NNd | 10.09 | 0.0156 (0.2%) | 10.09 | 0.0157 (0.2%) | 0.00 | 0.0013 (92.7%) | 0.002 |
MID | 41.15 | 0.1724 (0.4%) | 41.13 | 0.173 (0.4%) | −0.01 | 0.0068 (46.1%) | 0.013 |
NA | 130.60 | 1.3301 (1%) | 128.01 | 1.3086 (1%) | −2.59 | 0.1013 (3.9%) | 0.199 |
rNA | 0.77 | 0.0078 (1%) | 0.75 | 0.0077 (1%) | −0.02 | 0.0006 (3.9%) | 0.001 |
MFP18 | 0.62 | 0.0106 (1.7%) | 0.61 | 0.0105 (1.7%) | −0.01 | 0.0003 (4.6%) | 0.001 |
SDFP18 | 0.09 | 0.0033 (3.6%) | 0.10 | 0.0032 (3.2%) | 0.01 | 0.0005 (6.3%) | 0.001 |
PF | 0.62 | 0.0106 (1.7%) | 0.61 | 0.0105 (1.7%) | −0.01 | 0.0003 (4.5%) | 0.001 |
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Lister, A.; Lister, T.; Weber, T. Semi-Automated Sample-Based Forest Degradation Monitoring with Photointerpretation of High-Resolution Imagery. Forests 2019, 10, 896. https://doi.org/10.3390/f10100896
Lister A, Lister T, Weber T. Semi-Automated Sample-Based Forest Degradation Monitoring with Photointerpretation of High-Resolution Imagery. Forests. 2019; 10(10):896. https://doi.org/10.3390/f10100896
Chicago/Turabian StyleLister, Andrew, Tonya Lister, and Thomas Weber. 2019. "Semi-Automated Sample-Based Forest Degradation Monitoring with Photointerpretation of High-Resolution Imagery" Forests 10, no. 10: 896. https://doi.org/10.3390/f10100896