Growing Stock Volume Estimation Research Articles

Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information

Airborne Light Detection and Ranging (LiDAR) and Landsat data were evaluated as auxiliary information with the intent to increase the precision of growing stock volume estimates in field-based forest inventories. The aim of the study was to efficiently utilize both wall-to-wall Landsat data and a sample of LiDAR data using model-assisted estimation. Variables derived from the Landsat 7 ETM+ satellite image were spectral values of blue, green, red, near infra-red (IR), and two shortwave IR (SWIR) bands. From the LiDAR data twenty-six height and density based metrics were extracted. Field plots were measured according to a design similar to the 10th Finnish National Forest Inventory, although with an increased number of plots per area unit. The study was performed in a 30000ha area of Kuortane, Western Finland. Three regression models based on different combinations of auxiliary data were developed, analysed, and applied in the model-assisted estimators. Our results show that adding auxiliary Landsat and LiDAR data improves estimates of growing stock volume. Very precise results were obtained for the case where wall-to-wall Landsat data, LiDAR strip samples, and field plots were combined; for simple random sampling of LiDAR strips the relative standard errors (RSE) were in the range of 1–4%, depending on the size of the sample. With only LiDAR and field data the RSEs ranged from 4% to 25%. We also showed that probability-proportional-to-size sampling of LiDAR strips (utilizing predicted volume from Landsat data as the size variable) led to more precise results than simple random sampling.

Large Area Mapping of Boreal Growing Stock Volume on an Annual and Multi-Temporal Level Using PALSAR L-Band Backscatter Mosaics

The forests of the Russian Taiga can be described as an enormous biomass and carbon reservoir. Therefore, they are of utmost importance for the global carbon cycle. Large-area forest inventories in these mostly remote regions are associated with logistical problems and high financial efforts. Remotely-sensed data from satellite platforms may have the capability to provide such huge amounts of information. This study presents an application-oriented approach to derive aboveground growing stock volume (GSV) maps using the annual large-area L-band backscatter mosaics provided by the Japan Aerospace Exploration Agency (JAXA). Furthermore, a multi-temporal map has been created to improve GSV estimation accuracy. Based on information from Russian forest inventory data, the maps were generated using the machine learning algorithm, RandomForest. The results showed the high potential of this method for an operational, large-scale and high-resolution biomass estimation over boreal forests. An RMSE from 55.2 to 63.3 m3/ha could be obtained for the annual maps. Using the multi-temporal approach, the error could be slightly reduced to 54.4 m3/ha.

Exploiting Growing Stock Volume Maps for Large Scale Forest Resource Assessment: Cross-Comparisons of ASAR- and PALSAR-Based GSV Estimates with Forest Inventory in Central Siberia

Growing stock volume is an important biophysical parameter describing the state and dynamics of the Boreal zone. Validation of growing stock volume (GSV) maps based on satellite remote sensing is challenging due to the lack of consistent ground reference data. The monitoring and assessment of the remote Russian forest resources of Siberia can only be done by integrating remote sensing techniques and interdisciplinary collaboration. In this paper, we assess the information content of GSV estimates in Central Siberian forests obtained at 25 m from ALOS-PALSAR and 1 km from ENVISAT-ASAR backscatter data. The estimates have been cross-compared with respect to forest inventory data showing 34% relative RMSE for the ASAR-based GSV retrievals and 39.4% for the PALSAR-based estimates of GSV. Fragmentation analyses using a MODIS-based land cover dataset revealed an increase of retrieval error with increasing fragmentation of the landscape. Cross-comparisons of multiple SAR-based GSV estimates helped to detect inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands.

Modeling Growing Stock Volume Using SAR Data and OBIA: Effects of Scale Parameter and Textural and Geometrical Features

This study applies an object-based image classification approach to the modeling of growing stock volume (GSV) for three test sites in boreal forests of Central Siberia. Assessing GSV is of great importance in the context of climate change and modeling of the global carbon cycle. In this study, dual-polarized (HH and HV) L-band radar data are used. The main objective of this study is to improve the model accuracy of object-based GSV estimation. Thus, the applied methodology uses backscatter intensities as well as geometrical and textural features computed using Trimble eCognition Developer. Furthermore, the impacts of these feature groups and of different scale parameters on the model accuracy are analyzed. The scale parameter is of great importance in image segmentation, defining the size of the resulting objects. For modeling GSV, the random forest algorithm is used, and is trained using forest inventory data. The application of this method yields a coefficient of correlation (R²) between 0.42 and 0.51, and a relative root mean square error (RMSE) between 27% and 37%. These results reveal that the combined use of spectral, textural, and geometrical features and a smaller scale parameter enhance the model accuracy. These findings are encouraging and indicate that the model performance of object-based GSV estimation models can still be improved.

Impact of Tree Species on Magnitude of PALSAR Interferometric Coherence over Siberian Forest at Frozen and Unfrozen Conditions

Numerous studies demonstrated the potential of the magnitude of interferometric coherence |γ| for forest growing stock volume (GSV) estimation in boreal forests. Coherence derived from images acquired under frozen conditions proved to be of specific interest. This also applies to PALSAR coherence, although affected by a comparatively large temporal baseline of at least 46 days. However, when working with spaceborne L-band data, acquired under unfrozen conditions, a large spread of |γ| was observed at all GSV levels. This scatter negatively affects the correlation of GSV and |γ|. So far, the impact of tree species on |γ| has rarely been studied in this context, although the different tree geometries are likely to have an impact on volumetric decorrelation. This paper presents the results of a study investigating the impact of tree species on PALSAR coherence employing 36 interferograms. The observations show only a small impact of the tree species on |γ| during frozen conditions. At unfrozen conditions, the impact is about three times larger. Deciduous species (aspen, birch, larch) exhibit the lowest |γ|, while coniferous species (fir, pine) feature the highest |γ|. For example, at unfrozen conditions, the |γ| of fir is 0.15 greater than the |γ| of larch, while the mean |γ| of dense forest is 0.38. Accordingly, the impact of tree species on |γ| under unfrozen conditions causes a portion of the observed spread of the GSV-|γ| relationship. Consequently, when aiming at |γ| based GSV assessment using L-band SAR data acquired during unfrozen conditions, the impact of the species on |γ| needs to be considered. For studies aiming at |γ| based GSV estimation across species, PALSAR data acquired at frozen conditions is preferable.

Investigating ALOS PALSAR interferometric coherence in central Siberia at unfrozen and frozen conditions: implications for forest growing stock volume estimation

This paper investigates the impact of freezing on the properties of the magnitude of the interferometric coherence |γ| in central Siberia and discusses its implications for forest growing stock volume (GSV) estimation in the boreal zone. Eighty-seven acquisitions were employed and approximately 300 interferograms were generated. Accordingly, a high statistical credibility of the obtained results can be presumed. The temporal baselines of the interferograms ranged from 46 days to 2.5 years. The random volume over ground model was applied to support the interpretation of the observations. Compared with unfrozen conditions, we observed increased coherence over open areas, decreased coherence over dense forest, decreased spread of coherence, and improved correlation between |γ| and GSV during a frozen state. At frozen conditions, the experimental data showed no proof that the perpendicular baseline B⊥ impacted |γ| over dense forest, whereas at unfrozen conditions an impact was detected. Consequently, at frozen conditions, the temporal decorrelation was the major source of decorrelation and obstructed the detection of volume decorrelation effects. Nevertheless, |γ| acquired at frozen state exhibits potential for GSV mapping. The relationship between GSV and |γ| can be described with an average coefficient of determination R2 of 0.6. Saturation occurs at about 250 m3/ha.

Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Québec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data

A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Québec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.3×106 km2 were mapped with a 0.01° pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1° and 0.5° was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5° was consistently within a magnitude of 20–30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available information.

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

We have investigated for forest plantations in Chile the stand-level retrieval of canopy height (CH) and growing stock volume (GSV) using Airborne Laser Scanner (ALS), ALOS PALSAR and Landsat. In a two-stage up-scaling approach, ensemble regression tree models (randomForest) were used to relate a suite of ALS canopy structure indices to stand-level in situ measurements of CH and GSV for 319 stands. The retrieval of CH and GSV with ALS yielded high accuracies with R2s of 0.93 and 0.81, respectively. A second set of randomForest models was developed using multi-temporal ALOS PALSAR intensities and repeat-pass coherences in two polarizations as well as Landsat data as predictor and stand-level ALS based estimates of CH and GSV as response variables. At three test sites, the retrieval of CH and GSV with PALSAR/Landsat reached promising accuracies with R2s in the range of 0.7 to 0.85. We show that the combined use of multi-temporal PALSAR intensity, coherence and Landsat yields higher retrieval accuracies than the retrieval with any of the datasets alone. Potential limitations for the large-area application of the fusion approach included (1) the low sensitivity of ALS first/last return data to forest horizontal structure, affecting the retrieval of GSV in less managed types of forest, and (2) the dense ALS sampling required to achieve high retrieval accuracies at larger scale.

Post-stratified estimation of forest area and growing stock volume using lidar-based stratifications

National forest inventories report estimates of parameters related to forest area and growing stock volume for geographic areas ranging in size from municipalities to entire countries. Landsat imagery has been shown to be a source of auxiliary information that can be used with stratified estimation to increase the precision of estimates, although the increase is greater for estimates of forest area than for estimates of growing stock volume. The objective of the study was to assess the utility of lidar-based stratifications for increasing the precision of mean proportion forest area and mean growing stock volume per unit area. Stratifications based on nonlinear logistic regression model predictions of volume obtained from lidar data reduced variances of mean growing stock volume estimates by factors as great as 3.2 and variances of mean proportion forest area estimates by factors as great as 1.5.

Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements

Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100 m and 1 km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Québec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300 m 3/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1 km pixel size. Larger errors were obtained at 100 m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10 × 10 pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data.

Mapping biomass variables with a multi-source forest inventory technique

Map form information on forest biomass is required for estimating bioenergy potentials and monitoring carbon stocks. In Finland, the growing stock of forests is monitored using multi-source forest inventory, where variables are estimated in the form of thematic maps and area statistics by combining information of field measurements, satellite images and other digital map data. In this study, we used the multi-source forest inventory methodology for estimating forest biomass characteristics. The biomass variables were estimated for national forest inventory field plots on the basis of measured tree variables. The plot-level biomass estimates were used as reference data for satellite image interpretation. The estimates produced by satellite image interpretation were tested by cross-validation. The results indicate that the method for producing biomass maps on the basis of biomass models and satellite image interpretation is operationally feasible. Furthermore, the accuracy of the estimates of biomass variables is similar or even higher than that of traditional growing stock volume estimates. The technique presented here can be applied, for example, in estimating biomass resources or in the inventory of greenhouse gases.

Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems

This paper describes applications of non-parametric and parametric methods for estimating forest growing stock volume using Landsat images on the basis of data measured in the field, integrated with ancillary information. Several k-Nearest Neighbors (k-NN) algorithm configurations were tested in two study areas in Italy belonging to Mediterranean and Alpine ecosystems. Field data were acquired by the regional forest inventory and forest management plans, and satellite images are from Landsat 5 TM and Landsat 7 ETM+. The paper describes the data used, the methodologies adopted and the results achieved in terms of pixel level accuracy of forest growing stock volume estimates. The results show that several factors affect estimation accuracy when using the k-NN method. For the two test areas a total of 3500 different configurations of the k-NN algorithm were systematically tested by changing the number and type of spectral and ancillary input variables, type of multidimensional distance measures, number of nearest neighbors and methods for spectral feature extraction using the leave-one-out (LOO) procedure. The best k-NN configurations were then used for pixel level estimation; the accuracy was estimated with a bootstrapping procedure; and the results were compared to estimates obtained using parametric regression methods implemented on the same data set.The best k-NN growing stock volume pixel level estimates in the Alpine area have a Root Mean Square Error (RMSE) ranging between 74 and 96 m3 ha−1 (respectively, 22% and 28% of the mean measured value) and between 106 and 135 m3 ha−1 (respectively, 44% and 63% of the mean measured value) in the Mediterranean area. On the whole, the results cast a promising light on the use of non-parametric techniques for forest attribute estimation and mapping with accuracy high enough to support forest planning activities in such complex landscapes. The results of the LOO analyses also highlight the importance of a local empirical optimization phase of the k-NN procedure before defining the best algorithm configuration. In the tests performed the pixel level accuracy increased, depending on the k-NN configuration, as much as 100%.

Classification of forest volume resources using ERS tandem coherence and JERS backscatter data

Considering recent progress in the development of techniques and methods to achieve biomass estimates and full carbon accounting, remote sensing research of forested ecosystems needs to be aimed towards the retrieval of information at global scales. In this paper, an algorithm for the estimation of growing stock volume, an important parameter for the commercial forest community and a proxy for woody biomass density, from ERS and JERS synthetic aperture radar (SAR) data is described. The algorithm is based on the information content of both ERS tandem coherence and JERS backscatter images and was developed using ground data, made available by the Russian Forestry Services. It is tested on SAR datasets of boreal forests in Siberia, a managed, temperate forest plantation in the United Kingdom and a semi-natural boreal forest at Siggefora in Sweden. Comparisons of the classified products, comprising three growing stock interval classes and one non-forest class are made with ground data. The results of this accuracy assessment exercise show that the algorithm yields, in all cases, overall classification accuracies of greater than 70%. A visual comparison is made of the algorithm performance over a tropical forest region of Brazil. The results indicate that the algorithm has the potential to retrieve growing stock volume estimates in forest ecosystems throughout the globe.

The Influence of Field Sample Data Location on Growing Stock Volume Estimation in Landsat TM-Based Forest Inventory in Eastern Finland

Forest inventory methods that utilize Landsat TM data mostly assume that all sample plots within the image can be used as ground reference data for any subarea of the image. The assumption about the suitability of the field sample data within a satellite image area was evaluated in this study. The best overall results were obtained when plots within a 20-km range were used in point estimation of plot growing stock volume within a 7×8-km field data network. The estimation procedure was also tested in the calculation of area-level statistics. When the restriction of a 20-km distance was used in volume estimation, the error was always smaller than the sample plot variation and was always smaller than results received from estimates with no distance limit. The best benefits from incorporating satellite data were obtained for the smallest areas using a “20-km distance” restriction.