The Regenerative Potential of Managed Calluna Heathlands—Revealing Optical and Structural Traits for Predicting Recovery Dynamics
Abstract
:1. Introduction
- A.
- The potential resprouting of Calluna heath,
- B.
- The potential threat of grass invasion in heathland sites?
2. Materials and Methods
2.1. Study Area and Field Survey
2.2. UAV Image Processing
2.3. Spatial Detection of After-Mowing Dynamics
2.4. Statistical Modeling of Before-Mowing Predictors
3. Results
3.1. Spatial Patterns of Heathland Regeneration after Mowing
3.2. The Predictive Potential of Calluna Resprouting and Grass Encroachment
4. Discussion
4.1. The Predictive Values of Before-Mowing Stand Properties in UAV Imagery
4.2. Consequences for Nature Conservation Monitoring
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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After-Mowing (n-Layers = 15) | Before-Mowing (n-Layers = 48) | DEM (n-Layers = 2) | ||||
---|---|---|---|---|---|---|
image dates | 24/07/2020 | 08/09/2020 | 02/10/2020 | 06/04/2018 | 13/02/2019 | |
optical traits | ||||||
RGB grey-values | - | - | - | 3 | 3 | - |
RGB-HSV transformed | 3 | 3 | 3 | 3 | 3 | - |
RGB chromaticity | - | - | - | 3 | 3 | - |
spectral indices | - | - | - | 8 | 8 | - |
structural traits | - | |||||
green texture | - | 6 | - | 6 | 6 | - |
canopy height | - | - | - | 1 | 1 | - |
micro-relief | - | |||||
topographic wetness index | - | - | - | - | - | 1 |
slope-length factor | - | - | - | - | - | 1 |
Index | Name | Equation | Citation |
---|---|---|---|
VARI | Visible Atmospheric Resistant Index | (G - R) / (G + R -B) | [37] |
TGI | Triangular Greeness Index | G - 0.39 · R - 0.61 · B | [38] |
NGRDI | Normalized Green Red Difference Index | (G - R) / (G + R) | [37] |
MGRVI | Modified Green Red Vegetation Index | (G2 - R2) / (G2 + R2) | [39] |
RGBVI | Red Green Blue Vegetation Index | G2 - (R · B) / G2 + (R · B) | [39] |
GLI | Green Leaf Index | (2 · G – R – B) / (2 · G + R + B) | [40] |
EXG | Excess Green Index | 2 · G - B - R | [41] |
DAVI | Daylight Adapted Vegetation Index | G/((R0.667) · (B(1 - 0.667))) | [42,43] |
Class | Individuals | n-Pixels per Layer |
---|---|---|
Calluna | Calluna vulgaris | 5292 |
Grass | Calamagrostis epigejos, Agrostis capillaris, Carex arenaria; Carex pilulifera, Nardus stricta, Deschampsia flexuosa, Corynephorus canescens | 3360 |
Herb | Rumex acetosella, Hypericum perforatum, Hieracium pilosella | 1753 |
Litter | Calluna vulgaris (dead, senescent branches) | 2090 |
Cryptogams | Cladonia mitis, Cladonia coccifera, Cladonia furcata, Pleurozium schreberi, Hypnum jutlandicum, Polytrichum piliferum | 2271 |
Shrub | Rubus spec., Populus tremula, Betula pendula, Pinus sylvestris | 3030 |
Background | open soil substrate | 1368 |
Classification | |||||||||
Calluna | Grass | Herb | Litter | Crypto | Shrub | Backgr. | Producer’s Accuracy (%) | ||
Reference | Calluna | 5244 | 11 | 2 | 8 | 18 | 9 | 0 | 99.09 |
Grass | 26 | 3309 | 10 | 6 | 9 | 45 | 0 | 97.18 | |
Herb | 10 | 10 | 1713 | 2 | 18 | 0 | 0 | 97.72 | |
Litter | 16 | 20 | 0 | 2029 | 15 | 0 | 10 | 97.08 | |
Crypto | 29 | 30 | 2 | 7 | 2201 | 0 | 2 | 96.92 | |
Shrub | 52 | 12 | 2 | 0 | 0 | 2964 | 0 | 97.82 | |
Backgr. | 0 | 8 | 0 | 12 | 2 | 0 | 1354 | 98.40 | |
User’s accuracy (%) | 97.53 | 97.32 | 99.07 | 98.3 | 97.26 | 98.21 | 99.12 |
R2 | RMSE (%) | Selected Predictors | |
---|---|---|---|
(a) Calluna resprouting | |||
Multivariate | 0.15 | 21.6 | mean.green06/04/2018, hsv.green06/04/2018, DAVI06/04/2018 |
Random Forest | 0.17 | 20.1 | mean.green06/04/2018, green06/04/2018, variance.green06/04/2018 |
(b) Grass encroachment | |||
Multivariate | 0.31 | 18.64 | dissimilarity13/02/2019, nDSM13/02/2019, hsv.red06/04/2018, dissimilarity06/04/2018, second.moment06/04/2018, TWI06/04/2018 |
Random Forest | 0.38 | 17.00 | red13/02/2019, red06/04/2018, RGBVI06/04/2018, variance06/04/2018, nDSM13/02/2019, variance13/02/2019 |
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Neumann, C.; Schindhelm, A.; Müller, J.; Weiss, G.; Liu, A.; Itzerott, S. The Regenerative Potential of Managed Calluna Heathlands—Revealing Optical and Structural Traits for Predicting Recovery Dynamics. Remote Sens. 2021, 13, 625. https://doi.org/10.3390/rs13040625
Neumann C, Schindhelm A, Müller J, Weiss G, Liu A, Itzerott S. The Regenerative Potential of Managed Calluna Heathlands—Revealing Optical and Structural Traits for Predicting Recovery Dynamics. Remote Sensing. 2021; 13(4):625. https://doi.org/10.3390/rs13040625
Chicago/Turabian StyleNeumann, Carsten, Anne Schindhelm, Jörg Müller, Gabriele Weiss, Anna Liu, and Sibylle Itzerott. 2021. "The Regenerative Potential of Managed Calluna Heathlands—Revealing Optical and Structural Traits for Predicting Recovery Dynamics" Remote Sensing 13, no. 4: 625. https://doi.org/10.3390/rs13040625