Benefits of past inventory data as prior information for the current inventory

When auxiliary information in the form of airborne laser scanning (ALS) is used to assist in estimating the population parameters of interest, the benefits of prior information from previous inventories are not self-evident. In a simulation study, we compared three different approaches: 1) using only current data, 2) using non-updated old data and current data in a composite estimator and 3) using updated old data and current data with a Kalman filter. We also tested three different estimators, namely i) Horwitz-Thompson for a case of no auxiliary information, ii) model-assisted estimation and iii) model-based estimation. We compared these methods in terms of bias, precision and accuracy, as estimators utilizing prior information are not guaranteed to be unbiased.202

A Methodology for Providing Surface-Cover-Corrected Net Radiation at Heterogeneous Eddy-Covariance Sites

publishedVersio

The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass

International audienceAbstractKey messageWhen areas of interest experience little change, remote sensing-based maps whose dates deviate from ground data can still substantially enhance precision. However, when change is substantial, deviations in dates reduce the utility of such maps for this purpose.ContextRemote sensing-based maps are well-established as means of increasing the precision of estimates of forest inventory parameters. The general practice is to use maps whose dates correspond closely to the dates of ground data. However, as national forest inventories move to continuous inventories, deviations between map and ground data dates increase.AimsThe aim was to assess the degree to which remote sensing-based maps can be used to increase the precision of estimates despite differences between map and ground data dates.MethodsFor study areas in the USA and Norway, maps were constructed for each of two dates, and model-assisted regression estimators were used to estimate inventory parameters using ground data whose dates differed by as much as 11 years from the map dates.ResultsFor the Minnesota study area that had little change, 7-year differences in dates had little effect on the precision of estimates of proportion forest area. For the Norwegian study area that experienced considerable change, 11-year differences in dates had a detrimental effect on the precision of estimates of mean biomass per unit area.ConclusionsThe effects of differences in map and ground data dates were less important than temporal change in the study area

A method for continuous sub-annual mapping of forest disturbances using optical time series

Forest disturbances have a major impact on ecosystem dynamics both at local and global scales. Accordingly, it is important to acquire objective information about the location, nature and timing of such events to improve the understanding of their impact, update forest management policies and disturbance mitigation strategies. To this date, remotely sensed data have been widely used for the detection of stand replacing disturbances (SRD) such as windthrows and wildfires. In contrast, less effort has been devoted to the detection of non-stand replacing disturbances (NSRD), typically characterized by slower and gradual temporal dynamics. To address this gap, we propose a method for the automated detection of both SRD and NSRD. The proposed method can detect both past and recent disturbances, with a monthly temporal resolution, in a near real-time fashion by processing new images as they are acquired. Differently from existing approaches that handle the time series as a one-dimensional (1D) temporal trajectory, the method analyzes the sequence of images by organizing them in a two-dimensional (2D) grid-like structure. This representation allows us to model both the intra- and inter-annual variations of the time series taking advantage of the annual cyclical nature of the plant phenology. The method has been tested on study areas attacked by bark beetles achieving a user’s accuracy and producer’s accuracy of 0.91±0.08 and 0.81±0.07 (with 95% confidence intervals) for the disturbed areas, respectively

Prediction of Timber Quality Parameters of Forest Stands by Means of Small Footprint Airborne Laser Scanner Data

The aim of this study was to explore the capability of airborne laser scanner (ALS) data to explain the variation in field-measured variables representing timber quality within square 0.25 ha grid cells in a mature conifer forest in the southeast of Norway. These variables were the mean ratio between stem diameter at six m above ground and the diameter at breast height (R D6 ), the volume of saw logs (V SL ), the proportion of saw logs relative to the total volume (P SL ), the ratio between tree height and diameter at breast height (HD), mean basal area diameter (D g ), and crown height (CH). Each of these variables was modeled using a mixed modeling approach. Model fit was expressed by the Pseudo-R 2 , and were 0.85, 0.50, 0.78, 0.57, 0.74, and 0.58 for the respective quality variables. Furthermore, much of the residual error could be attributed to the different forest stands from which the grid cells originated even though we used field-observed tree species proportions as auxiliary information. It was concluded that more auxiliary information is needed to estimate models that are general across stands, but that the relationships between ALS-data and the quality variables considered here seem strong enough to be utilized for example to prioritize between stands in relation to harvest when specific quality distributions are sought

On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests

This study derives regression models for aboveground biomass (AGB) estimation in miombo woodlands of Tanzania that utilize the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restricts their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture fine-scale AGB dynamics. This becomes a sequential modeling approach, where the first regression stage has linked in situ data to ALS data and produced the AGB prediction map; we perform the subsequent stage, where this map is related to Sentinel-1 data.We develop a traditional, parametric regression model and alternative nonparametric models for this stage. The latter uses a conditional generative adversarial network (cGAN) to translate Sentinel-1 images into ALS-based AGB prediction maps. The convolution filters in the neural networks make them contextual. We compare the sequential models to traditional, nonsequential regression models, all trained on limited AGB ground reference data. Results show that our newly proposed nonsequential Sentinel-1-based regression model performs better quantitatively than the sequential models, but achieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-based sequential models best reproduce the distribution of ALS-based AGB predictions. They also reach a lower RMSE against in situ AGB data than the parametric sequential model, indicating a potential for further development

Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables

The quantification of forest ecosystems is important for a variety of purposes, including the assessment of wildlife habitat, nutrient cycles, timber yield and fire propagation. This research assesses the estimation of forest structure, composition and deadwood variables from small-footprint airborne lidar data, both discrete return (DR) and full waveform (FW), acquired under leaf-on and leaf-off conditions. The field site, in the New Forest, UK, includes managed plantation and ancient, semi-natural, coniferous and deciduous woodland. Point clouds were rendered from the FW data through Gaussian decomposition. An area-based regression approach (using Akaike Information Criterion analysis) was employed, separately for the DR and FW data, to model 23 field-measured forest variables. A combination of plot-level height, intensity/amplitude and echo-width variables (the latter for FW lidar only) generated from both leaf-on and leaf-off point cloud data were utilised, together with individual tree crown (ITC) metrics from rasterised leaf-on height data. Statistically significant predictive models (p<0.05) were generated for all 23 forest metrics using both the DR and FW lidar datasets, with R2 values for the best fit models in the range R2=0.43-0.94 for the DR data and R2=0.28-0.97 for the FW data (with normalised RMSE values being 18%-66% and 16%-48% respectively). For all but two forest metrics the difference between the NRMSE of the best performing DR and FW models was ≤7%, and there was an even split (11:12) as to which lidar dataset (DR or FW) generated the best model per forest metric. Overall, the DR data performed better at modelling structure variables, whilst the FW data performed better at modelling composition and deadwood variables. Neither showed a clear advantage at modelling variables from a particular vegetation layer (canopy, shrub or ground). Height, intensity/amplitude, and ITC-derived crown variables were shown to be important inputs across the best performing models (DR or FW), but the additional echo-width variables available from FW point data were relatively unimportant. Of perhaps greater significance to the choice between lidar data type (i.e. DR or FW) in determining the predictive power of the best performing models was the selection of leaf-on and/or leaf-off data. Of the 23 best models, 10 contained both leaf-on and leaf-off lidar variables, whilst 11 contained only leaf-on and two only leaf-off data. We therefore conclude that although FW lidar has greater vertical profile information than DR lidar, the greater complimentary information about the entire forest canopy profile that is available from both leaf-on and leaf-off data is of more benefit to forest inventory, in general, than the selection between DR or FW lidar