Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia

Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Shamaoma, Hastings [verfasserIn] Chirwa, Paxie W. [verfasserIn] Zekeng, Jules C. [verfasserIn] Ramoelo, Able [verfasserIn] Hudak, Andrew T. [verfasserIn] Handavu, Ferdinand [verfasserIn] Syampungani, Stephen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Above ground biomass UAS-lidar Two-phase Sampling tool

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Plant methods - BioMed Central, 2005, 20(2024), 1 vom: 08. Juni
Übergeordnetes Werk:	volume:20 ; year:2024 ; number:1 ; day:08 ; month:06

Links:	Volltext

DOI / URN:	10.1186/s13007-024-01212-4

Katalog-ID:	SPR056160151

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allfields	10.1186/s13007-024-01212-4 doi (DE-627)SPR056160151 (SPR)s13007-024-01212-4-e DE-627 ger DE-627 rakwb eng 580 570 VZ 12 ssgn BIODIV DE-30 fid 42.43 bkl Shamaoma, Hastings verfasserin aut Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. Above ground biomass (dpeaa)DE-He213 UAS-lidar (dpeaa)DE-He213 Two-phase (dpeaa)DE-He213 Sampling tool (dpeaa)DE-He213 Chirwa, Paxie W. verfasserin aut Zekeng, Jules C. verfasserin aut Ramoelo, Able verfasserin aut Hudak, Andrew T. verfasserin aut Handavu, Ferdinand verfasserin aut Syampungani, Stephen verfasserin aut Enthalten in Plant methods BioMed Central, 2005 20(2024), 1 vom: 08. Juni (DE-627)500321191 (DE-600)2203723-8 1746-4811 nnns volume:20 year:2024 number:1 day:08 month:06 https://dx.doi.org/10.1186/s13007-024-01212-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 42.43 VZ AR 20 2024 1 08 06
spelling	10.1186/s13007-024-01212-4 doi (DE-627)SPR056160151 (SPR)s13007-024-01212-4-e DE-627 ger DE-627 rakwb eng 580 570 VZ 12 ssgn BIODIV DE-30 fid 42.43 bkl Shamaoma, Hastings verfasserin aut Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. Above ground biomass (dpeaa)DE-He213 UAS-lidar (dpeaa)DE-He213 Two-phase (dpeaa)DE-He213 Sampling tool (dpeaa)DE-He213 Chirwa, Paxie W. verfasserin aut Zekeng, Jules C. verfasserin aut Ramoelo, Able verfasserin aut Hudak, Andrew T. verfasserin aut Handavu, Ferdinand verfasserin aut Syampungani, Stephen verfasserin aut Enthalten in Plant methods BioMed Central, 2005 20(2024), 1 vom: 08. Juni (DE-627)500321191 (DE-600)2203723-8 1746-4811 nnns volume:20 year:2024 number:1 day:08 month:06 https://dx.doi.org/10.1186/s13007-024-01212-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 42.43 VZ AR 20 2024 1 08 06
allfields_unstemmed	10.1186/s13007-024-01212-4 doi (DE-627)SPR056160151 (SPR)s13007-024-01212-4-e DE-627 ger DE-627 rakwb eng 580 570 VZ 12 ssgn BIODIV DE-30 fid 42.43 bkl Shamaoma, Hastings verfasserin aut Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. Above ground biomass (dpeaa)DE-He213 UAS-lidar (dpeaa)DE-He213 Two-phase (dpeaa)DE-He213 Sampling tool (dpeaa)DE-He213 Chirwa, Paxie W. verfasserin aut Zekeng, Jules C. verfasserin aut Ramoelo, Able verfasserin aut Hudak, Andrew T. verfasserin aut Handavu, Ferdinand verfasserin aut Syampungani, Stephen verfasserin aut Enthalten in Plant methods BioMed Central, 2005 20(2024), 1 vom: 08. Juni (DE-627)500321191 (DE-600)2203723-8 1746-4811 nnns volume:20 year:2024 number:1 day:08 month:06 https://dx.doi.org/10.1186/s13007-024-01212-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 42.43 VZ AR 20 2024 1 08 06
allfieldsGer	10.1186/s13007-024-01212-4 doi (DE-627)SPR056160151 (SPR)s13007-024-01212-4-e DE-627 ger DE-627 rakwb eng 580 570 VZ 12 ssgn BIODIV DE-30 fid 42.43 bkl Shamaoma, Hastings verfasserin aut Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. Above ground biomass (dpeaa)DE-He213 UAS-lidar (dpeaa)DE-He213 Two-phase (dpeaa)DE-He213 Sampling tool (dpeaa)DE-He213 Chirwa, Paxie W. verfasserin aut Zekeng, Jules C. verfasserin aut Ramoelo, Able verfasserin aut Hudak, Andrew T. verfasserin aut Handavu, Ferdinand verfasserin aut Syampungani, Stephen verfasserin aut Enthalten in Plant methods BioMed Central, 2005 20(2024), 1 vom: 08. Juni (DE-627)500321191 (DE-600)2203723-8 1746-4811 nnns volume:20 year:2024 number:1 day:08 month:06 https://dx.doi.org/10.1186/s13007-024-01212-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 42.43 VZ AR 20 2024 1 08 06
allfieldsSound	10.1186/s13007-024-01212-4 doi (DE-627)SPR056160151 (SPR)s13007-024-01212-4-e DE-627 ger DE-627 rakwb eng 580 570 VZ 12 ssgn BIODIV DE-30 fid 42.43 bkl Shamaoma, Hastings verfasserin aut Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. Above ground biomass (dpeaa)DE-He213 UAS-lidar (dpeaa)DE-He213 Two-phase (dpeaa)DE-He213 Sampling tool (dpeaa)DE-He213 Chirwa, Paxie W. verfasserin aut Zekeng, Jules C. verfasserin aut Ramoelo, Able verfasserin aut Hudak, Andrew T. verfasserin aut Handavu, Ferdinand verfasserin aut Syampungani, Stephen verfasserin aut Enthalten in Plant methods BioMed Central, 2005 20(2024), 1 vom: 08. Juni (DE-627)500321191 (DE-600)2203723-8 1746-4811 nnns volume:20 year:2024 number:1 day:08 month:06 https://dx.doi.org/10.1186/s13007-024-01212-4 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 42.43 VZ AR 20 2024 1 08 06
language	English
source	Enthalten in Plant methods 20(2024), 1 vom: 08. Juni volume:20 year:2024 number:1 day:08 month:06
sourceStr	Enthalten in Plant methods 20(2024), 1 vom: 08. Juni volume:20 year:2024 number:1 day:08 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Above ground biomass UAS-lidar Two-phase Sampling tool
dewey-raw	580
isfreeaccess_bool	true
container_title	Plant methods
authorswithroles_txt_mv	Shamaoma, Hastings @@aut@@ Chirwa, Paxie W. @@aut@@ Zekeng, Jules C. @@aut@@ Ramoelo, Able @@aut@@ Hudak, Andrew T. @@aut@@ Handavu, Ferdinand @@aut@@ Syampungani, Stephen @@aut@@
publishDateDaySort_date	2024-06-08T00:00:00Z
hierarchy_top_id	500321191
dewey-sort	3580
id	SPR056160151
language_de	englisch
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author	Shamaoma, Hastings
spellingShingle	Shamaoma, Hastings ddc 580 ssgn 12 fid BIODIV bkl 42.43 misc Above ground biomass misc UAS-lidar misc Two-phase misc Sampling tool Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia
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topic_title	580 570 VZ 12 ssgn BIODIV DE-30 fid 42.43 bkl Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia Above ground biomass (dpeaa)DE-He213 UAS-lidar (dpeaa)DE-He213 Two-phase (dpeaa)DE-He213 Sampling tool (dpeaa)DE-He213
topic	ddc 580 ssgn 12 fid BIODIV bkl 42.43 misc Above ground biomass misc UAS-lidar misc Two-phase misc Sampling tool
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title	Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia
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title_full	Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia
author_sort	Shamaoma, Hastings
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author_browse	Shamaoma, Hastings Chirwa, Paxie W. Zekeng, Jules C. Ramoelo, Able Hudak, Andrew T. Handavu, Ferdinand Syampungani, Stephen
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title_sort	exploring uas-lidar as a sampling tool for satellite-based agb estimations in the miombo woodland of zambia
title_auth	Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia
abstract	Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. © The Author(s) 2024
abstractGer	Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. © The Author(s) 2024
abstract_unstemmed	Abstract To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-$ R^{2} $ = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics ($ R^{2} $ = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. © The Author(s) 2024
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title_short	Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia
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Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia

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