Scaling wood volume estimates from inventory plots to landscapes with airborne LiDAR in temperate deciduous forest View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12

AUTHORS

Shaun R. Levick, Dominik Hessenmöller, E-Detlef Schulze

ABSTRACT

BACKGROUND: Monitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales. Grid-based forest inventory networks have provided valuable records of forest structure and dynamics at individual plot scales, but in isolation they may not represent the carbon dynamics of heterogeneous landscapes encompassing diverse land-management strategies and site conditions. Airborne LiDAR has greatly enhanced forest structural characterisation and, in conjunction with field-based inventories, it provides avenues for monitoring carbon over broader spatial scales. Here we aim to enhance the integration of airborne LiDAR surveying with field-based inventories by exploring the effect of inventory plot size and number on the relationship between field-estimated and LiDAR-predicted wood volume in deciduous broad-leafed forest in central Germany. RESULTS: Estimation of wood volume from airborne LiDAR was most robust (R2 = 0.92, RMSE = 50.57 m3 ha-1 ~14.13 Mg C ha-1) when trained and tested with 1 ha experimental plot data (n = 50). Predictions based on a more extensive (n = 1100) plot network with considerably smaller (0.05 ha) plots were inferior (R2 = 0.68, RMSE = 101.01 ~28.09 Mg C ha-1). Differences between the 1 and 0.05 ha volume models from LiDAR were negligible however at the scale of individual land-management units. Sample size permutation tests showed that increasing the number of inventory plots above 350 for the 0.05 ha plots returned no improvement in R2 and RMSE variability of the LiDAR-predicted wood volume model. CONCLUSIONS: Our results from this study confirm the utility of LiDAR for estimating wood volume in deciduous broad-leafed forest, but highlight the challenges associated with field plot size and number in establishing robust relationships between airborne LiDAR and field derived wood volume. We are moving into a forest management era where field-inventory and airborne LiDAR are inextricably linked, and we encourage field inventory campaigns to strive for increased plot size and give greater attention to precise stem geolocation for better integration with remote sensing strategies. More... »

PAGES

7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13021-016-0048-7

DOI

http://dx.doi.org/10.1186/s13021-016-0048-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019642303

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27330548


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    "description": "BACKGROUND: Monitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales. Grid-based forest inventory networks have provided valuable records of forest structure and dynamics at individual plot scales, but in isolation they may not represent the carbon dynamics of heterogeneous landscapes encompassing diverse land-management strategies and site conditions. Airborne LiDAR has greatly enhanced forest structural characterisation and, in conjunction with field-based inventories, it provides avenues for monitoring carbon over broader spatial scales. Here we aim to enhance the integration of airborne LiDAR surveying with field-based inventories by exploring the effect of inventory plot size and number on the relationship between field-estimated and LiDAR-predicted wood volume in deciduous broad-leafed forest in central Germany.\nRESULTS: Estimation of wood volume from airborne LiDAR was most robust (R2\u00a0=\u00a00.92, RMSE\u00a0=\u00a050.57\u00a0m3 ha-1\u00a0~14.13\u00a0Mg C ha-1) when trained and tested with 1\u00a0ha experimental plot data (n\u00a0=\u00a050). Predictions based on a more extensive (n\u00a0=\u00a01100) plot network with considerably smaller (0.05\u00a0ha) plots were inferior (R2\u00a0=\u00a00.68, RMSE\u00a0=\u00a0101.01\u00a0~28.09\u00a0Mg C ha-1). Differences between the 1 and 0.05\u00a0ha volume models from LiDAR were negligible however at the scale of individual land-management units. Sample size permutation tests showed that increasing the number of inventory plots above 350 for the 0.05\u00a0ha plots returned no improvement in R2 and RMSE variability of the LiDAR-predicted wood volume model.\nCONCLUSIONS: Our results from this study confirm the utility of LiDAR for estimating wood volume in deciduous broad-leafed forest, but highlight the challenges associated with field plot size and number in establishing robust relationships between airborne LiDAR and field derived wood volume. We are moving into a forest management era where field-inventory and airborne LiDAR are inextricably linked, and we encourage field inventory campaigns to strive for increased plot size and give greater attention to precise stem geolocation for better integration with remote sensing strategies.", 
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44 schema:description BACKGROUND: Monitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales. Grid-based forest inventory networks have provided valuable records of forest structure and dynamics at individual plot scales, but in isolation they may not represent the carbon dynamics of heterogeneous landscapes encompassing diverse land-management strategies and site conditions. Airborne LiDAR has greatly enhanced forest structural characterisation and, in conjunction with field-based inventories, it provides avenues for monitoring carbon over broader spatial scales. Here we aim to enhance the integration of airborne LiDAR surveying with field-based inventories by exploring the effect of inventory plot size and number on the relationship between field-estimated and LiDAR-predicted wood volume in deciduous broad-leafed forest in central Germany. RESULTS: Estimation of wood volume from airborne LiDAR was most robust (R2 = 0.92, RMSE = 50.57 m3 ha-1 ~14.13 Mg C ha-1) when trained and tested with 1 ha experimental plot data (n = 50). Predictions based on a more extensive (n = 1100) plot network with considerably smaller (0.05 ha) plots were inferior (R2 = 0.68, RMSE = 101.01 ~28.09 Mg C ha-1). Differences between the 1 and 0.05 ha volume models from LiDAR were negligible however at the scale of individual land-management units. Sample size permutation tests showed that increasing the number of inventory plots above 350 for the 0.05 ha plots returned no improvement in R2 and RMSE variability of the LiDAR-predicted wood volume model. CONCLUSIONS: Our results from this study confirm the utility of LiDAR for estimating wood volume in deciduous broad-leafed forest, but highlight the challenges associated with field plot size and number in establishing robust relationships between airborne LiDAR and field derived wood volume. We are moving into a forest management era where field-inventory and airborne LiDAR are inextricably linked, and we encourage field inventory campaigns to strive for increased plot size and give greater attention to precise stem geolocation for better integration with remote sensing strategies.
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