Very High-Resolution Imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types
Abstract
:1. Introduction
- (I)
- To evaluate the final results for Random Forest classification models for the levels Basic surface type (BA, e.g., substrate types, water), Vegetation units (VE, e.g., reed, herbaceous vegetation), Dominant stands (DO, up to species level) and Substrate types (SU, e.g., sand, gravel);
- (II)
- To compare classification results from the Random Forest algorithm (RF) with Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost);
- (III)
- To identify areas in the classification results with high degrees of uncertainty or certainty, respectively; and
- (IV)
- To transfer the workflow to data acquired by gyrocopter and to compare the achieved results with those from UAS data.
2. Materials and Methods
2.1. Study Area
2.2. Image Data and Pre-Processing
2.3. Additional Abiotic Data
2.4. Reference Data
2.5. Image Segmentation
2.6. Feature Selection
2.7. Classification Algorithm
2.8. Accuracy Measures and Model Fitting
3. Results and Discussion
3.1. Random Forest Classification of Different Thematic Levels
3.1.1. Results
3.1.2. Discussion
3.2. Comparison of Algorithms
3.2.1. Results
3.2.2. Discussion
3.3. Spatial Evaluation of Classification Probability
3.3.1. Results
3.3.2. Discussion
3.4. Transfer of Classification Method from UAS to Larger Scale Gyrocopter Data
3.4.1. Results
3.4.2. Discussion
4. Conclusions
- (I)
- Classification results for UAS data with RF decreased with increasing class detail from BA (OA = 88.9%) and VE (OA = 88.4%) to DO (OA = 74.8%) and SU (OA = 62%). Classes with high spatial coverage or those which are homogeneous could be mapped sufficiently. Classes with low sample sizes had high intra-class variability and, even when good median accuracies were achieved. In general, RF was a suitable algorithm to classify vegetation and substrate types in riparian zones. The results of the feature selections showed for BA level, that the spectral indices have the largest explanatory power in the models, whereas for the VE and DO level the highest explanatory power lies on the hydrotopographic parameters and for the SU level textural indices were predominant.
- (II)
- Classification performance did not change notably when using SVM or XGBoost instead of RF. SVM introduced more heterogeneous and patchy maps while classifying vegetation that did not match with the visual interpretation and would be difficult to work with in the field. On the other hand, XGBoost consumed the highest computational time. Thus, for the rest of this study RF was used.
- (III)
- Classification probability maps can be used to identify areas of low performance and prioritize them during (re-)visits in the field. For instance, areas located in the transition zone and shaded areas of vegetation had low classification probabilities and were often classified incorrectly. Hence, when using probability maps efficiency of field surveys may be increased.
- (IV)
- Gyrocopter data can be used within the same classification workflow and achieve comparable results as UAS data for classes of the levels BA and VE as well as for classes covering larger and homogeneous areas. For management purposes, it might be useful to collect information over larger areas, possibly in combination with UAS.
5. Outlook and Further Work
- To apply the workflow on the whole gyrocopter area and including classes, such as “urban areas” or “agriculture”, which are not represented in the area under investigation in this study. This step also includes the collection of training and validation data of those classes based on the imagery.
- To evaluate the transferability of the classification workflow in new areas. This step also includes the application of the already existing classification models on new areas and evaluation of the question of what extent the existing reference data can be used in addition to newly collected reference data to build new models.
- To examine the effect of multi-temporal imagery on classification results, as demonstrated by van Iersel, Straatsma, Middelkoop and Addink [18], and to evaluate if a potential increase in classification performance may justify the additional workload.
- To implement the proposed workflow in management routines of the waterway and shipping administration and to adjust them to the future needs of the stakeholder concerns [13]. Potential routine could be to use the classification maps as a basis for more detailed vegetation mapping or to use them within the hydromorphological evaluation and classification tool, Valmorph [89].
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Platform | Sensor | Center of Wavebands in nm (Bandwidth in Parenthesis) | Spatial Resolution in cm | Flight Altitude in m | ||||
---|---|---|---|---|---|---|---|---|
Blue | Green | Red | Red-Edge | Near-Infrared | ||||
UAS (DJI Phantom 4 Pro) | Micasence RedEdge-M | 475 (20) | 560 (20) | 668 (10) | 717 (10) | 840 (40) | 5 | 70 |
Gyrocopter | PanX 2.0 | 475 (50) | 550 (50) | 650 (25) | _ | 875 (25) | 28 | 650 |
Index | Equation | Case Study |
---|---|---|
NDVI | Jensen [44] | |
gNDVI | Xue and Su [45] | |
NDRE | Jorge, et al. [46] | |
NDWI | Jensen [44] | |
NRBI | Michez, et al. [47] | |
SAVI | Jensen [44] | |
SR | Jensen [44] | |
GVI | Michez, et al. [47] | |
Total Brightness | This study, based on Jensen [44] |
Algorithm(R-Package) | Hyperparameter | Start | End | Tuned Parameter Values for Each Level | |||
---|---|---|---|---|---|---|---|
BA | VE | DO | SU | ||||
RF (ranger) | mtry | 1 | 63 | 2 | 19 | 12 | 17 |
num.tree | 100 | 1000 | 201 | 513 | 499 | 200 | |
SVM (ksvm) | cost (C) | 0.1 | 10,000 | 841 | 71.7 | 31.7 | 3.04 |
sigma | 0.001 | 10 | 0.00135 | 0.00344 | 0.00221 | 0.02040 | |
XGBoost (xgboost) | nrounds | 100 | 500 | 103 | 286 | 155 | 276 |
max-depth | 1 | 10 | 6 | 3 | 4 | 10 | |
eta | 0.1 | 0.5 | 0.170 | 0.137 | 0.177 | 0.181 | |
lambda | 0.1 | 1 | 0.496 | 0.704 | 0.840 | 0.127 |
Classification Level with Classified Classes | UAS | Gyrocopter | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
RF | SVM | XGBoost | RF | |||||||
PA | UA | PA | UA | PA | UA | n | PA | UA | n | |
Basic surface types (BA) | OA = 88.9 Kc = 0.85 | OA = 88.6 Kc = 0.85 | OA = 88.3 Kc = 0.85 | OA = 88.4 Kc = 0.85 | ||||||
Water | 90 | 88 | 90 | 87 | 89 | 88 | 150 | 87 | 94 | 156 |
Water shallow | 77 | 81 | 75 | 83 | 77 | 80 | 165 | 79 | 85 | 172 |
Substrate types | 88 | 93 | 88 | 92 | 87 | 93 | 259 | 87 | 94 | 244 |
Substrate types wet | 83 | 76 | 85 | 77 | 82 | 76 | 198 | 90 | 78 | 232 |
Vegetation | 96 | 94 | 95 | 94 | 96 | 94 | 623 | 97 | 90 | 555 |
Vegetation shadow | 80 | 89 | 80 | 87 | 81 | 86 | 180 | 72 | 89 | 170 |
Vegetation units (VE) | OA = 88.4 Kc = 0.82 | OA = 89.2 Kc = 0.83 | OA = 89.1 Kc = 0.83 | OA = 86.4 Kc = 0.79 | ||||||
Grassland | 95 | 91 | 91 | 88 | 90 | 94 | 27 | 95 | 96 | |
Herbaceous vegetation | 94 | 92 | 94 | 92 | 94 | 92 | 222 | 93 | 91 | 221 |
Pioneers | 84 | 83 | 83 | 91 | 85 | 76 | 40 | 83 | 89 | 37 |
Reed | 72 | 71 | 76 | 78 | 71 | 83 | 48 | 65 | 65 | 49 |
Woody | 83 | 90 | 85 | 87 | 87 | 89 | 81 | 79 | 81 | |
Dominant stands (DO) | OA = 74.8 Kc = 0.72 | OA = 73.8 Kc = 0.71 | OA = 66.5 Kc = 0.63 | OA = 65.6 Kc = 0.62 | ||||||
Agrostis stolonifera | 32 | 65 | 37 | 50 | 8 | 14 | 8 | 13 | 28 | |
Arctium lappa | 86 | 80 | 89 | 84 | 75 | 77 | 22 | 76 | 78 | |
Brassica nigra | 76 | 64 | 82 | 66 | 64 | 60 | 33 | 63 | 55 | |
Carduus crispus | 87 | 77 | 90 | 79 | 77 | 71 | 44 | 81 | 72 | 47 |
Cirsium arvense | 20 | 53 | 9 | 21 | 8 | 13 | 11 | 19 | 38 | |
Grassland | 95 | 92 | 81 | 69 | 81 | 81 | 13 | 78 | 72 | 15 |
Lythrum salicaria | 68 | 92 | 62 | 64 | 99 | 99 | 8 | 67 | 97 | |
Pasture | 92 | 91 | 73 | 86 | 91 | 88 | 19 | 74 | 82 | 18 |
Phalaris arundinacea | 64 | 62 | 61 | 49 | 64 | 65 | 24 | 54 | 54 | 25 |
Phragmites australis | 79 | 67 | 66 | 61 | 77 | 66 | 24 | 61 | 59 | |
Pioneers | 83 | 70 | 61 | 82 | 72 | 63 | 20 | 84 | 80 | |
Pioneers small | 88 | 84 | 97 | 79 | 84 | 93 | 7 | 90 | 82 | 6 |
Populus spp. | 57 | 73 | 73 | 79 | 49 | 55 | 40 | 59 | 63 | 39 |
Rubus caesius | 38 | 79 | 75 | 82 | 39 | 46 | 13 | 40 | 47 | |
Salix spp. | 82 | 75 | 75 | 83 | 74 | 72 | 55 | 71 | 63 | 56 |
Tanacetum vulgare | 4 | 19 | 9 | 18 | 12 | 16 | 10 | 1 | 4 | 11 |
Tripleurospermum perforatum | 76 | 89 | 66 | 62 | 8 | 13 | 10 | 79 | 63 | |
Urtica dioica | 87 | 76 | 86 | 84 | 80 | 70 | 66 | 77 | 68 | 61 |
Woody | 82 | 82 | 81 | 75 | 74 | 81 | 18 | 47 | 73 | 15 |
Substrate types (SU) | OA = 62 Kc = 0.53 | OA = 64.9 Kc = 0.56 | OA = 59.7 Kc = 0.50 | OA = 52 Kc = 0.37 | ||||||
Armour stones | 73 | 70 | 83 | 74 | 74 | 74 | 22 | 24 | 62 | |
Fine grained material | 68 | 58 | 66 | 61 | 60 | 57 | 29 | 66 | 55 | |
Gravel | 73 | 68 | 79 | 68 | 70 | 66 | 75 | 73 | 54 | |
Sand | 65 | 69 | 69 | 73 | 58 | 61 | 59 | 49 | 49 | |
Layer of shells | 64 | 68 | 56 | 68 | 67 | 71 | 18 | 52 | 60 | |
Stones | 28 | 50 | 21 | 44 | 34 | 42 | 17 | 5 | 17 | |
Wood | 33 | 32 | 36 | 38 | 34 | 34 | 29 | 16 | 29 |
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Rommel, E.; Giese, L.; Fricke, K.; Kathöfer, F.; Heuner, M.; Mölter, T.; Deffert, P.; Asgari, M.; Näthe, P.; Dzunic, F.; et al. Very High-Resolution Imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types. Remote Sens. 2022, 14, 954. https://doi.org/10.3390/rs14040954
Rommel E, Giese L, Fricke K, Kathöfer F, Heuner M, Mölter T, Deffert P, Asgari M, Näthe P, Dzunic F, et al. Very High-Resolution Imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types. Remote Sensing. 2022; 14(4):954. https://doi.org/10.3390/rs14040954
Chicago/Turabian StyleRommel, Edvinas, Laura Giese, Katharina Fricke, Frederik Kathöfer, Maike Heuner, Tina Mölter, Paul Deffert, Maryam Asgari, Paul Näthe, Filip Dzunic, and et al. 2022. "Very High-Resolution Imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types" Remote Sensing 14, no. 4: 954. https://doi.org/10.3390/rs14040954
APA StyleRommel, E., Giese, L., Fricke, K., Kathöfer, F., Heuner, M., Mölter, T., Deffert, P., Asgari, M., Näthe, P., Dzunic, F., Rock, G., Bongartz, J., Burkart, A., Quick, I., Schröder, U., & Baschek, B. (2022). Very High-Resolution Imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types. Remote Sensing, 14(4), 954. https://doi.org/10.3390/rs14040954