Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Measurements of PEU
2.3. Remotely Sensed Data
2.4. Methodology
2.4.1. Multispectral Image’s Pan Sharpening
2.4.2. Sampling PEUs and Classification System
2.4.3. PEUs Classification Using Different Classification Algorithms
2.4.4. Segmentation
2.4.5. Auxiliary Data and Prediction Assessment
2.4.6. Statistical Comparison of Classification Algorithms
3. Results
3.1. Pixel-Based Classification
3.2. Object-Based Classification
3.3. Comparing Supervised Classification Methods
3.4. Impact of Auxiliary Data on PEUs Classification Accuracy
3.5. Statistical Comparison
4. Discussion
4.1. The Selection of Pixel-Based and Object-Based Approaches in PEUs Classification
4.2. Selection Best Classification Algorithm
4.3. Impact of Auxiliary Data on PEUs Classification Accuracy
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Code | Dominant Species * | Field Photos | Abbreviation | Structure | Accompanied Species * | Dominant Soil Type |
---|---|---|---|---|---|---|
PEU1 | Astragalus verus Olivier. (23.4%) | | As ve | Scrubby | Alyssum linifolium Steph. ex Wild. (2.5%) Echinophora platyloba DC. (2.5%) Scariola orientalis (Boiss)Sojak. (2.5%) Eurotia ceratoides (L.) C.A. Mey. (2%) Heteranthelium piliferum Hochst. ex Jaub. (1.8%) Cousinia bachtiarica Boiss. & Hausskn. (1.8%) Bromus tectorum L. (1.6%) Astragalus macropelmatus Bunge. (1.3%) Taeniatherum crinitum (Schreb.) Nevski. (1%) Acanthophyllum spinosum (Desf.) C.A.Mey. (0.8%) | Sandy loamy to loamy clay |
PEU2 | Bromus tomentellus Boiss. (8.9%) | | Br to | Grassland | Phlomis olivieri Benth. (3%) Bromus danthoniae Trin. (3%) Stipa hohenackeriana Trin & Rupr. (2.6%) Alyssum marginatum Steud. (2.5%) Bromus tectorum L. (2.4%) Achillea wilhelmsii C. Koch, L. (1.8%) Astragalus microcephalus Willd. (1.5%) Centaurea aucheri (DC.) Wagenitz. (1.2%) Gypsophila struthium. (1%) Ajuga chamaecistus Ging. (0.5%) | loamy and Silty Loamy |
PEU3 | Scariola orientalis (Boiss.) Sojak. (9.25%) | | Sc or | Semi-scrub | Noaea mucronata (Forsk.) Aschers et. Sch. (2.5%) Onobrychis cornuta (L.) Desv. (1.6%) Astragalus microcephalus Willd. (1.5%) Polygonum aridum Boiss. & Hausskn. (1.5%) Taeniatherum crinitum (Schreb.) Nevski. (1.5%) Cousinia crispa, Jaub & Spach. (1.2%) Stachys inflata Benth. (1.2%) Tragopogon longirostris Bischoff ex Sch.Bip. (1%) Acanthophyllum spinosum (Desf.) C.A.Mey. (0.5%) Chardinia orientalis (L.) Kuntze. (0.5%) | Clay loam |
PEU4 | Astragalus verus Olivier (8.6%)—Bromus tomentellus Boiss (5.4) | | As ve-Br to | Scrubby-grassland | Noaea mucronata (Forsk.) Aschers et. Sch. (2%) Alyssum marginatum Steud. (1.5%) Euphorbia azerbadjhanica Bordz. (1.5%) Phlomis persica Boiss. (1.5%) Turginia latifolia (L.) Hoffm. (1.5%) Astragalus effusus Bunge. (1.3%) Bromus danthoniae Trin. (1.2%) Stachys lavandulifolia Vahl. (1%) Cichorium intybus L. (0.5%) Achillea wilhelmsii C. Koch, L. (0.5%) | loamy and Silty Loamy |
MD | MLC | NN-MLP | CTA | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Type | PA | UA | KIA | PA | UA | KIA | PA | UA | KIA | PA | UA | KIA | |
PBCM | PEU1 | 89 | 80 | 73 | 89 | 91 | 87 | 97 | 67 | 55 | 80 | 86 | 80 |
PEU2 | 49 | 67 | 55 | 72 | 79 | 70 | 97 | 49 | 31 | 63 | 64 | 51 | |
PEU3 | 45 | 36 | 13 | 85 | 67 | 55 | 5 | 100 | 100 | 67 | 58 | 43 | |
PEU4 | 41 | 47 | 28 | 71 | 84 | 78 | 23 | 5 | 33 | 57 | 61 | 48 | |
OK = 42% OA = 57% | OK = 70% OA = 78% | OK = 39% OA = 55% | OK = 55% OA = 67% | ||||||||||
OBCM | PEU1 | 89 | 84 | 77 | 89 | 93 | 90 | 93 | 91 | 88 | 97 | 90 | 86 |
PEU2 | 63 | 61 | 47 | 72 | 75 | 65 | 69 | 58 | 43 | 93 | 80 | 72 | |
PEU3 | 32 | 34 | 100 | 83 | 64 | 51 | 65 | 50 | 32 | 83 | 81 | 73 | |
PEU4 | 50 | 52 | 35 | 69 | 86 | 81 | 41 | 90 | 86 | 69 | 96 | 95 | |
OK = 44% OA = 59% | OK = 71% OA = 78% | OK = 61% OA = 72% | OK = 80% OA = 86% |
Accuracy Assessment Results Based on Raw Bands Using Classification Tree Analysis | |||||||
Type | PEU1 | PEU2 | PEU3 | PEU4 | PA | UA | KIA |
PEU1 | 44 | 0 | 0 | 5 | 97 | 90 | 86 |
PEU2 | 0 | 42 | 8 | 3 | 93 | 80 | 72 |
PEU3 | 0 | 3 | 37 | 6 | 83 | 81 | 73 |
PEU4 | 1 | 0 | 0 | 30 | 69 | 96 | 95 |
Overall Kappa: 80% | Overall Accuracy: 86% | ||||||
Accuracy Assessment Results Based on Raw Bands + PCA Using Classification Tree Analysis | |||||||
Type | PEU1 | PEU2 | PEU3 | PEU4 | PA | UA | KIA |
PEU1 | 44 | 0 | 0 | 0 | 97 | 100 | 100 |
PEU2 | 0 | 42 | 6 | 4 | 93 | 81 | 74 |
PEU3 | 0 | 3 | 37 | 4 | 83 | 85 | 78 |
PEU4 | 1 | 0 | 2 | 36 | 82 | 92 | 89 |
Overall Kappa: 85% | Overall Accuracy: 89% |
PEUs Accuracy | Sig | |||
---|---|---|---|---|
Classification Algorithms | PBCM-Sig | OBCM-Sig | ||
User’s Accuracy (UA) | 0.021 * | MLC-MD | 0.036 | 0.03 |
MLC-NN | 0.071 | 0.34 | ||
MLC-CTA | 0.22 | 0.66 | ||
MD-NN | 0.77 | 0.22 | ||
MD-CTA | 0.38 | 0.009 | ||
NN-CTA | 0.56 | 0.17 | ||
Kappa Index of Agreement (KIA) | 0.039 * | MLC-MD | 0.043 | 0.030 |
MLC-NN | 0.083 | 0.38 | ||
MLC-CTA | 0.24 | 0.66 | ||
MD-NN | 0.77 | 0.19 | ||
MD-CTA | 0.38 | 0.009 | ||
NN-CTA | 0.56 | 0.19 | ||
Producer’s Accuracy (PA) | 0.095 |
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Aghababaei, M.; Ebrahimi, A.; Naghipour, A.A.; Asadi, E.; Verrelst, J. Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms. Remote Sens. 2021, 13, 3433. https://doi.org/10.3390/rs13173433
Aghababaei M, Ebrahimi A, Naghipour AA, Asadi E, Verrelst J. Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms. Remote Sensing. 2021; 13(17):3433. https://doi.org/10.3390/rs13173433
Chicago/Turabian StyleAghababaei, Masoumeh, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi, and Jochem Verrelst. 2021. "Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms" Remote Sensing 13, no. 17: 3433. https://doi.org/10.3390/rs13173433