Effectiveness of the spectral area index created by three algorithms for tree species recognition

Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Liu, Huaipeng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	WorldView-3 and WorldView-2 data Polygon area index Tree species classification Algorithm effectiveness evaluation Random forest

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Annals of forest science - Paris : Springer, 1999, 80(2023), 1 vom: 06. Apr.
Übergeordnetes Werk:	volume:80 ; year:2023 ; number:1 ; day:06 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s13595-023-01184-w

Katalog-ID:	SPR049972804

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520			\|a Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification.
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allfields	10.1186/s13595-023-01184-w doi (DE-627)SPR049972804 (SPR)s13595-023-01184-w-e DE-627 ger DE-627 rakwb eng Liu, Huaipeng verfasserin (orcid)0000-0003-0456-1545 aut Effectiveness of the spectral area index created by three algorithms for tree species recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. WorldView-3 and WorldView-2 data (dpeaa)DE-He213 Polygon area index (dpeaa)DE-He213 Tree species classification (dpeaa)DE-He213 Algorithm effectiveness evaluation (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Enthalten in Annals of forest science Paris : Springer, 1999 80(2023), 1 vom: 06. Apr. (DE-627)312842457 (DE-600)2012340-1 1297-966X nnns volume:80 year:2023 number:1 day:06 month:04 https://dx.doi.org/10.1186/s13595-023-01184-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 80 2023 1 06 04
spelling	10.1186/s13595-023-01184-w doi (DE-627)SPR049972804 (SPR)s13595-023-01184-w-e DE-627 ger DE-627 rakwb eng Liu, Huaipeng verfasserin (orcid)0000-0003-0456-1545 aut Effectiveness of the spectral area index created by three algorithms for tree species recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. WorldView-3 and WorldView-2 data (dpeaa)DE-He213 Polygon area index (dpeaa)DE-He213 Tree species classification (dpeaa)DE-He213 Algorithm effectiveness evaluation (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Enthalten in Annals of forest science Paris : Springer, 1999 80(2023), 1 vom: 06. Apr. (DE-627)312842457 (DE-600)2012340-1 1297-966X nnns volume:80 year:2023 number:1 day:06 month:04 https://dx.doi.org/10.1186/s13595-023-01184-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 80 2023 1 06 04
allfields_unstemmed	10.1186/s13595-023-01184-w doi (DE-627)SPR049972804 (SPR)s13595-023-01184-w-e DE-627 ger DE-627 rakwb eng Liu, Huaipeng verfasserin (orcid)0000-0003-0456-1545 aut Effectiveness of the spectral area index created by three algorithms for tree species recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. WorldView-3 and WorldView-2 data (dpeaa)DE-He213 Polygon area index (dpeaa)DE-He213 Tree species classification (dpeaa)DE-He213 Algorithm effectiveness evaluation (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Enthalten in Annals of forest science Paris : Springer, 1999 80(2023), 1 vom: 06. Apr. (DE-627)312842457 (DE-600)2012340-1 1297-966X nnns volume:80 year:2023 number:1 day:06 month:04 https://dx.doi.org/10.1186/s13595-023-01184-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 80 2023 1 06 04
allfieldsGer	10.1186/s13595-023-01184-w doi (DE-627)SPR049972804 (SPR)s13595-023-01184-w-e DE-627 ger DE-627 rakwb eng Liu, Huaipeng verfasserin (orcid)0000-0003-0456-1545 aut Effectiveness of the spectral area index created by three algorithms for tree species recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. WorldView-3 and WorldView-2 data (dpeaa)DE-He213 Polygon area index (dpeaa)DE-He213 Tree species classification (dpeaa)DE-He213 Algorithm effectiveness evaluation (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Enthalten in Annals of forest science Paris : Springer, 1999 80(2023), 1 vom: 06. Apr. (DE-627)312842457 (DE-600)2012340-1 1297-966X nnns volume:80 year:2023 number:1 day:06 month:04 https://dx.doi.org/10.1186/s13595-023-01184-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 80 2023 1 06 04
allfieldsSound	10.1186/s13595-023-01184-w doi (DE-627)SPR049972804 (SPR)s13595-023-01184-w-e DE-627 ger DE-627 rakwb eng Liu, Huaipeng verfasserin (orcid)0000-0003-0456-1545 aut Effectiveness of the spectral area index created by three algorithms for tree species recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. WorldView-3 and WorldView-2 data (dpeaa)DE-He213 Polygon area index (dpeaa)DE-He213 Tree species classification (dpeaa)DE-He213 Algorithm effectiveness evaluation (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Enthalten in Annals of forest science Paris : Springer, 1999 80(2023), 1 vom: 06. Apr. (DE-627)312842457 (DE-600)2012340-1 1297-966X nnns volume:80 year:2023 number:1 day:06 month:04 https://dx.doi.org/10.1186/s13595-023-01184-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 80 2023 1 06 04
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Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. 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author	Liu, Huaipeng
spellingShingle	Liu, Huaipeng misc WorldView-3 and WorldView-2 data misc Polygon area index misc Tree species classification misc Algorithm effectiveness evaluation misc Random forest Effectiveness of the spectral area index created by three algorithms for tree species recognition
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topic_title	Effectiveness of the spectral area index created by three algorithms for tree species recognition WorldView-3 and WorldView-2 data (dpeaa)DE-He213 Polygon area index (dpeaa)DE-He213 Tree species classification (dpeaa)DE-He213 Algorithm effectiveness evaluation (dpeaa)DE-He213 Random forest (dpeaa)DE-He213
topic	misc WorldView-3 and WorldView-2 data misc Polygon area index misc Tree species classification misc Algorithm effectiveness evaluation misc Random forest
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title_sort	effectiveness of the spectral area index created by three algorithms for tree species recognition
title_auth	Effectiveness of the spectral area index created by three algorithms for tree species recognition
abstract	Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. © The Author(s) 2023
abstractGer	Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. © The Author(s) 2023
abstract_unstemmed	Key message Tree species identification analysis of the two images (Luoyang and Hohhot of China) shows that the polygonal area indices extracted by the specific band-constrained polygon relative area (algorithm 3, obtained accuracy was ~ 13% higher than that of other algorithms in WorldView-3 and ~ 2% higher in WorldView-2) can effectively improve the classification accuracy of tree species compared to those with a constant polygon relative area constraint (algorithm 2) and without area constraint (algorithm 1) (equal accuracy was obtained by algorithms 1 and 2 in each data). Context Solving the problem of tree species identification by remote sensing technology is an international issue. Exploring the improvement of tree species recognition accuracy through multiple methods is currently widely attempted. A previous study has indicated that mining the differential information of various tree species in images using area differences of the polygons formed by tree species spectral curves and creating the polygon area index can improve tree species recognition accuracy. However, this study only created two such indices. Thus, a general model was developed to extract more potential polygon area indices and help tree species classification. However, the improvement of this model using a constant and a specific band to constrain the relative area of polygons still needs to be fully studied. Aims To obtain new algorithms for extracting polygon area indices that can mine the differential information of tree species and determine the index that is the most effective for tree species classification. Methods By unconstraining the area of polygons and constraining the relative area of polygons with constant and specific bands, three formulations of polygon area indices were created. Polygon area indices were extracted from WorldView-3 and WorldView-2 imagery based on three algorithms and combined with textures and spectral bands to form three feature sets. Random forest was used to classify images and rank the importance of features in the feature sets, and the effectiveness of the polygon area indices extracted by each algorithm in tree species recognition was analysed in accordance with their performance in the classifications. Results The proportion of polygon area index in the optimal feature sets ranged from 36.4 to 63.1%. The polygon area indices extracted with constant constrained polygon relative area and those without area constraint have minimal effect on tree species classification accuracy. Meanwhile, the polygon area indices extracted by the algorithm of specific band-constrained polygon relative area could remarkably improve tree species recognition accuracy (compared with spectral bands, WorldView-3 and WorldView-2 improved by 9.69% and 4.19%, respectively). Conclusion The experiments confirmed that polygon area indices are beneficial for tree species classification, and polygon area indices extracted by specific band-constrained polygon relative area play an important role in tree species identification. © The Author(s) 2023
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title_short	Effectiveness of the spectral area index created by three algorithms for tree species recognition
url	https://dx.doi.org/10.1186/s13595-023-01184-w
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