Nonparametric estimation of stand volume using spectral and spatial features of aerial photographs and old inventory data

2002 ◽  
Vol 32 (10) ◽  
pp. 1849-1857 ◽  
Author(s):  
Perttu Anttila
Keyword(s):  
Nearest Neighbor ◽  
Volume Estimation ◽  
Aerial Photographs ◽  
Final Conclusion ◽  
Inventory Data ◽  
K Nearest Neighbor ◽  
Private Forests ◽  
Management Planning ◽  
Stand Volume ◽  
Forest Management Planning

The forest management planning inventories for private forests in Finland are currently carried out in stand-level field inventories. To decrease the amount of fieldwork, aerial photographs and old inventory data could be utilized. The main objectives were to test the accuracy of a method based on these data sources and the effect of stand shape on the accuracy. Median pixel values, semivariances, and old inventory data were extracted for each of the 577 stands in the study. These data were applied as indicator attributes in k-nearest-neighbor estimation of stand volume. Stand-level estimates were computed as weighted means of k most similar neighbors. When all the stands were used, a root mean square error of 29.9% was obtained. Old inventory data proved to be valuable auxiliary information. It was also found that exclusion of stands with tortuous boundaries and small area decreased the error. The accuracy of mean volume estimation just met the requirements for stand-level inventory, but the method still needs further research before the final conclusion of the applicability for management planning.

scholarly journals Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

2014 ◽  
Vol 7 (1) ◽  
pp. 378-394 ◽  
Author(s):  
Shinya Tanaka ◽  
Tomoaki Takahashi ◽  
Tomohiro Nishizono ◽  
Fumiaki Kitahara ◽  
Hideki Saito ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Satellite Image ◽  
Image Data ◽  
Volume Estimation ◽  
Inventory Data ◽  
Stand Volume ◽  
Forest Inventory Data ◽  
Satellite Image Data

scholarly journals Timber Volume Estimation Model for Merchantable Tree Species in Sarmi Regency, Papua

Jurnal Wasian ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 91
Author(s):  
Relawan Kuswandi
Keyword(s):  
Forest Management ◽  
Forest Inventory ◽  
Standing Stock ◽  
Volume Estimation ◽  
Coefficient Of Determination ◽  
Estimation Model ◽  
Timber Volume ◽  
Management Planning ◽  
Forest Management Planning ◽  
Validation Test

Precise forest inventory to estimate standing stock is needed in forest management planning.  Therefore, it is necessary to have proper and reliable tools in estimating merchantable timber volume. This research was intended to build an accurate model to estimate timber volume for  merchantable species in logging concession of PT Wapoga Mutiara Timber, Sarmi Regency.  Regression equation between diameter and length did not have a significant correlation (coefficient of determination, R2 = 6.7 %). The best equation to estimate table tree volume based on validation test in logging concession of PT Wapoga Mutiara Timber was Log V = - 3.34 + 2.16 log d.     

scholarly journals Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data

2010 ◽  
Vol 40 (2) ◽  
pp. 184-199 ◽  
Author(s):  
Michael J. Falkowski ◽  
Andrew T. Hudak ◽  
Nicholas L. Crookston ◽  
Paul E. Gessler ◽  
Edward H. Uebler ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Nearest Neighbor ◽  
Forest Growth ◽  
Tree Level ◽  
Lidar Data ◽  
Inventory Data ◽  
K Nearest Neighbor ◽  
Forest Inventory Data ◽  
Forest Growth Model ◽  
Nearest Neighbor Imputation

Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and species) across a 12 100 ha study area in northeastern Oregon, USA. The primary objective was to provide spatially explicit data to parameterize the Forest Vegetation Simulator, a tree-level forest growth model. The final imputation model utilized LiDAR-derived height measurements and topographic variables to spatially predict tree-level forest inventory data. When compared with an independent data set, the accuracy of forest inventory metrics was high; the root mean square difference of imputed basal area and stem volume estimates were 5 m2·ha–1 and 16 m3·ha–1, respectively. However, the error of imputed forest inventory metrics incorporating small trees (e.g., quadratic mean diameter, tree density) was considerably higher. Forest Vegetation Simulator growth projections based upon imputed forest inventory data follow trends similar to growth projections based upon independent inventory data. This study represents a significant improvement in our capabilities to predict detailed, tree-level forest inventory data across large areas, which could ultimately lead to more informed forest management practices and policies.

scholarly journals Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

2020 ◽  
Vol 12 (3) ◽  
pp. 360 ◽  
Author(s):  
Bo Xie ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Barjeece Bashir ◽  
Ramesh P. Singh ◽  
...  
Keyword(s):  
Satellite Data ◽  
Nearest Neighbor ◽  
Field Measurements ◽  
Absolute Error ◽  
Timber Harvesting ◽  
Volume Estimation ◽  
Accurate Information ◽  
K Nearest Neighbor ◽  
Entire Area ◽  
Regional Forest

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution

1998 ◽  
Vol 28 (8) ◽  
pp. 1107-1115 ◽  
Author(s):  
Matti Maltamo ◽  
Annika Kangas
Keyword(s):  
Nearest Neighbor ◽  
Basal Area ◽  
Nearest Neighbors ◽  
Volume Estimation ◽  
Diameter Distribution ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Stand Growth ◽  
Weighted Averages ◽  
Growing Stock

In the Finnish compartmentwise inventory systems, growing stock is described with means and sums of tree characteristics, such as mean height and basal area, by tree species. In the calculations, growing stock is described in a treewise manner using a diameter distribution predicted from stand variables. The treewise description is needed for several reasons, e.g., for predicting log volumes or stand growth and for analyzing the forest structure. In this study, methods for predicting the basal area diameter distribution based on the k-nearest neighbor (k-nn) regression are compared with methods based on parametric distributions. In the k-nn method, the predicted values for interesting variables are obtained as weighted averages of the values of neighboring observations. Using k-nn based methods, the basal area diameter distribution of a stand is predicted with a weighted average of the distributions of k-nearest neighbors. The methods tested in this study include weighted averages of (i)Weibull distributions of k-nearest neighbors, (ii)distributions of k-nearest neighbors smoothed with the kernel method, and (iii)empirical distributions of the k-nearest neighbors. These methods are compared for the accuracy of stand volume estimation, stand structure description, and stand growth prediction. Methods based on the k-nn regression proved to give a more accurate description of the stand than the parametric methods.

scholarly journals Evaluating k-Nearest Neighbor (kNN) Imputation Models for Species-Level Aboveground Forest Biomass Mapping in Northeast China

2019 ◽  
Vol 11 (17) ◽  
pp. 2005 ◽  
Author(s):  
Yuanyuan Fu ◽  
Hong S. He ◽  
Todd J. Hawbaker ◽  
Paul D. Henne ◽  
Zhiliang Zhu ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Environmental Variables ◽  
Northeast China ◽  
Nearest Neighbor ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Species Level ◽  
Estimation Accuracy ◽  
Inventory Data ◽  
K Nearest Neighbor

Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale.

scholarly journals Sustainable forest management planning in Serbia

2014 ◽  
pp. 9-23
Author(s):  
Milan Medarevic ◽  
Biljana Sljukic ◽  
Snezana Obradovic
Keyword(s):  
Forest Management ◽  
Forest Cover ◽  
Sustainable Forest Management ◽  
Adaptive System ◽  
Climatic Changes ◽  
Private Forests ◽  
Management Planning ◽  
Forest Management Planning ◽  
Basic Assumptions ◽  
Management Plans

The forest cover of Serbia occupies around 29% of its territory, which puts it among fairly well wooded countries in Europe. The forests of Serbia are characterized by both state and private forests, medium preservation status, i.e. 27% of area that is covered by insufficiently stocked stands. Coppice forests cover about 50% of the area, and private forests are additionally burdened by fragmented plots. Forest management planning in Serbia is older than 200 years (The Plan of Deliblato Sands Afforestation 1806). There are two basic assumptions that define forest management planning: sustainability and multifunctionality. Today, forest management planning in Serbia is regulated by the Law on forests and it has the characteristics of a system. The planning also has the characteristics of an integral, integrated and adaptive system. The latter is particularly important in terms of pronounced climatic changes. For the forests in protected objects of nature, there are also other types of plans that complement sector plans in forestry (e.g. management plans in protected areas).

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