Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. ALS Processing
2.4. Accuracy Assessment
3. Results
3.1. Object-Based Classification of LiDAR Data
OBIA Class | Number of Classified Objects | Mean Area of Classified Objects [m2] | % of Analyzed Area | Number of Classified Objects in Sample Used for the Accuracy Assessment |
---|---|---|---|---|
no veg. | 2936 | 5254 | 18.21 | 529 |
sparse low veg. | 917 | 2465 | 2.67 | 141 |
sparse medium veg. | 4821 | 1510 | 8.59 | 172 |
sparse high veg. | 1137 | 999 | 1.34 | 0 |
dense low veg. | 30 | 957 | 0.03 | 0 |
dense medium veg. | 8823 | 1625 | 16.92 | 135 |
dense high veg. | 18270 | 2423 | 52.24 | 12 |
Total | 36934 | 100 | 989 |
3.2. Relationships between the BDOT10k and LiDAR-Based Classification
BDOT10k Class OBIA Class | Arable Land | Grassland | Forest, Woodland and Grove | Shrubland | Total | ||||
---|---|---|---|---|---|---|---|---|---|
% of SA | % of BDOT10k | % of SA | % of BDOT10k | % of SA | % of BDOT10k | % of SA | % of BDOT10k | % of SA | |
no vegetation | 8.9 | 81.3 | 8.4 | 59.6 | 0.9 | 1.2 | 0.0 | 5.7 | 18.2 |
sparse low veg | 0.8 | 6.7 | 1.4 | 10.0 | 0.5 | 0.7 | 0.0 | 10.4 | 2.7 |
sparse medium veg | 0.5 | 4.9 | 2.0 | 14.2 | 6.0 | 8.0 | 0.0 | 25.7 | 8.5 |
sparse high veg | 0.0 | 0.0 | 0.0 | 0.1 | 1.3 | 1.8 | 0.0 | 0.0 | 1.3 |
dense low veg | 0.0 | 0.1 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
dense medium veg | 0.6 | 5.6 | 1.7 | 12.1 | 14.5 | 19.4 | 0.1 | 57.2 | 16.9 |
dense high veg | 0.2 | 1.4 | 0.6 | 3.9 | 51.6 | 68.9 | 0.0 | 1.0 | 52.4 |
TOTAL | 11.0 | 100 | 14.1 | 100 | 74.8 | 100 | 0.1 | 100 | 100 |
sum of vegetated area | 2.1 | 18.7 | 5.7 | 40.4 | 73.9 | 98.8 | 0.1 | 94.3 | 81.8 |
3.3. Accuracy Assessment of the Extended National Database
4. Discussion
Reference Map | No Veg | Sparse Low Veg | Sparse Medium Veg | Sparse High Veg | Dense Low Veg | Dense Medium Veg | Dense High Veg | Total | Users Accuracy |
---|---|---|---|---|---|---|---|---|---|
No veg | 514 | 10 | 3 | 2 | 529 | 97.2 | |||
Sparse low veg | 125 | 13 | 3 | 141 | 88.7 | ||||
Sparse medium veg | 9 | 158 | 5 | 172 | 91.9 | ||||
Sparse high veg | 0 | 0 | 0.0 | ||||||
Dense low veg | 0 | 0 | 0.0 | ||||||
Dense medium veg | 3 | 131 | 1 | 135 | 97.0 | ||||
Dense high veg | 12 | 12 | 100.0 | ||||||
Total | 514 | 144 | 177 | 2 | 3 | 136 | 13 | 989 | |
Producers accuracy | 100.0 | 86.8 | 89.3 | 0.0 | 0.0 | 96.3 | 92.3 | ||
Overall accuracy | 95.0 |
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Kolecka, N.; Kozak, J.; Kaim, D.; Dobosz, M.; Ginzler, C.; Psomas, A. Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography. Remote Sens. 2015, 7, 8300-8322. https://doi.org/10.3390/rs70708300
Kolecka N, Kozak J, Kaim D, Dobosz M, Ginzler C, Psomas A. Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography. Remote Sensing. 2015; 7(7):8300-8322. https://doi.org/10.3390/rs70708300
Chicago/Turabian StyleKolecka, Natalia, Jacek Kozak, Dominik Kaim, Monika Dobosz, Christian Ginzler, and Achilleas Psomas. 2015. "Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography" Remote Sensing 7, no. 7: 8300-8322. https://doi.org/10.3390/rs70708300