Investigating the Effects of <i>k</i> and Area Size on Variance Estimation of Multiple Pixel Areas Using a <i>k</i>-NN Technique for Forest Parameters
Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessibl...
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2021
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small-area estimates operational forestry variance estimation model-based inferences LiDAR Science Q |
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small-area estimates operational forestry variance estimation model-based inferences LiDAR Science Q Dylan Walshe Daniel McInerney João Paulo Pereira Kenneth A. Byrne Investigating the Effects of <i>k</i> and Area Size on Variance Estimation of Multiple Pixel Areas Using a <i>k</i>-NN Technique for Forest Parameters |
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Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessible. Until recently, these estimates have been calculated without providing a measure of the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> ha<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>), stem density (n/ha<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>), and volume (m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula> ha<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>), and subsequently estimates the variance of multiple pixel areas using a <i>k</i>-NN technique. The area of interest (AOI) is the state owned commercial forests in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce (<i>Picea sitchensis</i> (Bong.) Carr.) and Lodgepole pine (<i>Pinus contorta Dougl.</i>). Field plots were measured in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured, both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> values for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22% (0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands was used to validate the modelled estimates. A comparison of measured values versus modelled estimates was carried out for a range of area sizes with results showing that estimated values in areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased, the estimated values became increasingly analogous to the measured values for all parameters. The results of the variance estimation highlighted: (i) a greater value of <i>k</i> was needed for small areas compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest managers to better understand how aspects of this variance estimation technique affect the accuracy of the uncertainty associated with parameter estimates. Utilising this information can provide forest managers with inventories of greater accuracy, therefore ensuring a more informed management decision. These results also add further weight to the applicability of the <i>k</i>-NN variance estimation technique in a range of forests landscapes. |
| format |
article |
| author |
Dylan Walshe Daniel McInerney João Paulo Pereira Kenneth A. Byrne |
| author_facet |
Dylan Walshe Daniel McInerney João Paulo Pereira Kenneth A. Byrne |
| author_sort |
Dylan Walshe |
| title |
Investigating the Effects of <i>k</i> and Area Size on Variance Estimation of Multiple Pixel Areas Using a <i>k</i>-NN Technique for Forest Parameters |
| title_short |
Investigating the Effects of <i>k</i> and Area Size on Variance Estimation of Multiple Pixel Areas Using a <i>k</i>-NN Technique for Forest Parameters |
| title_full |
Investigating the Effects of <i>k</i> and Area Size on Variance Estimation of Multiple Pixel Areas Using a <i>k</i>-NN Technique for Forest Parameters |
| title_fullStr |
Investigating the Effects of <i>k</i> and Area Size on Variance Estimation of Multiple Pixel Areas Using a <i>k</i>-NN Technique for Forest Parameters |
| title_full_unstemmed |
Investigating the Effects of <i>k</i> and Area Size on Variance Estimation of Multiple Pixel Areas Using a <i>k</i>-NN Technique for Forest Parameters |
| title_sort |
investigating the effects of <i>k</i> and area size on variance estimation of multiple pixel areas using a <i>k</i>-nn technique for forest parameters |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/dbe12295cf36483aa8f19a901e01e26b |
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AT dylanwalshe investigatingtheeffectsofikiandareasizeonvarianceestimationofmultiplepixelareasusingaikinntechniqueforforestparameters AT danielmcinerney investigatingtheeffectsofikiandareasizeonvarianceestimationofmultiplepixelareasusingaikinntechniqueforforestparameters AT joaopaulopereira investigatingtheeffectsofikiandareasizeonvarianceestimationofmultiplepixelareasusingaikinntechniqueforforestparameters AT kennethabyrne investigatingtheeffectsofikiandareasizeonvarianceestimationofmultiplepixelareasusingaikinntechniqueforforestparameters |
| _version_ |
1718410557430693888 |
| spelling |
oai:doaj.org-article:dbe12295cf36483aa8f19a901e01e26b2021-11-25T18:55:25ZInvestigating the Effects of <i>k</i> and Area Size on Variance Estimation of Multiple Pixel Areas Using a <i>k</i>-NN Technique for Forest Parameters10.3390/rs132246882072-4292https://doaj.org/article/dbe12295cf36483aa8f19a901e01e26b2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4688https://doaj.org/toc/2072-4292Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessible. Until recently, these estimates have been calculated without providing a measure of the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> ha<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>), stem density (n/ha<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>), and volume (m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula> ha<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>), and subsequently estimates the variance of multiple pixel areas using a <i>k</i>-NN technique. The area of interest (AOI) is the state owned commercial forests in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce (<i>Picea sitchensis</i> (Bong.) Carr.) and Lodgepole pine (<i>Pinus contorta Dougl.</i>). Field plots were measured in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured, both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> values for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22% (0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands was used to validate the modelled estimates. A comparison of measured values versus modelled estimates was carried out for a range of area sizes with results showing that estimated values in areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased, the estimated values became increasingly analogous to the measured values for all parameters. The results of the variance estimation highlighted: (i) a greater value of <i>k</i> was needed for small areas compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest managers to better understand how aspects of this variance estimation technique affect the accuracy of the uncertainty associated with parameter estimates. Utilising this information can provide forest managers with inventories of greater accuracy, therefore ensuring a more informed management decision. These results also add further weight to the applicability of the <i>k</i>-NN variance estimation technique in a range of forests landscapes.Dylan WalsheDaniel McInerneyJoão Paulo PereiraKenneth A. ByrneMDPI AGarticlesmall-area estimatesoperational forestryvariance estimationmodel-based inferencesLiDARScienceQENRemote Sensing, Vol 13, Iss 4688, p 4688 (2021) |