Svetlana Saarela's research while affiliated with Norwegian University of Life Sciences (NMBU) and other places

Publications (30)

Article
Full-text available
The objective of this study was to explore the effects of (1) the presence/absence of snow and snow depth, (2) solar noise, i.e., day/night and sun angle observations, and (3) strong/weak beam differences on ICESat-2 data in the context of data utility for forest AGB estimation. The framework of the study is multiphase modeling, where AGB field dat...
Article
Full-text available
Data assimilation (DA) is often used for merging observations to improve the predictions of the current and future states of characteristics of interest. In forest inventory, DA has so far found limited use, although dense time series of remotely sensed (RS) data have become available for estimating forest characteristics. A problem in forest inven...
Article
Full-text available
Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically de...
Article
NASA's Global Ecosystem Dynamics Investigation (GEDI) mission offers data for temperate and pan-tropical estimates of aboveground forest biomass (AGB). The spaceborne, full-waveform LiDAR from GEDI provides sample footprints of canopy structure, expected to cover about 4% of the land area following two years of operation. Several options are availa...
Preprint
Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically de...
Article
Full-text available
NASAs Global Ecosystem Dynamics Investigation (GEDI) is collecting space-borne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDIs footprint-level (~25 m) AGBD (GEDI04_A) product, including a descript...
Preprint
Full-text available
Spaceborne lidar sensors have potential to improve the accuracy of forest above-ground biomass (AGB) estimates by providing direct measurements of 3D structure of forests over large spatial scales. The ICESat-2 (Ice, Cloud and land Elevation Satellite 2), launched in 2018, provides a good coverage of the boreal forest zone and has been previously s...
Article
is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final...
Article
Full-text available
The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest chara...
Article
The structure of contemporary managed forests is complex and deviates from experimental forests which are usually even-aged monocultures and single-storied. To apply theoretical growth and yield functions on managed forests, adjustments are required, especially for leaf area index (LAI) which is a key biophysical variable in process-based growth mo...
Article
The Global Ecosystem Dynamic Investigation (GEDI) mission has been successfully launched to the International Space Station (ISS) on December 5th, 2018. While the sampling pattern of GEDI (8 transects with about 600 m across-track spacing) is sufficient to provide accurate biomass maps at the mission's required gridded resolution of 1 km, it is of...
Article
Full-text available
NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission will collect waveform lidar data at a dense sample of ∼25 m footprints along ground tracks paralleling the orbit of the International Space Station (ISS). GEDI’s primary science deliverable will be a 1 km grid of estimated mean aboveground biomass density (Mg ha ⁻¹ ), covering the latitu...
Article
Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of c...
Article
Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of informatio...
Article
Model-based inference is an alternative to probability-based inference for small areas or remote areas for which probability sampling is difficult. Model-based mean square error estimators incorporate three components: prediction covariance, residual variance, and residual covariance. The latter two components are often considered negligible, parti...
Article
Remotely sensed (RS) data are becoming increasingly important as sources of auxiliary information in forest resource assessments. Data from several satellites providing moderate image resolution are freely available (e.g. Sentinel-2). In addition, very-high-resolution three-dimensional data are available due to the advent of unmanned aerial vehicle...
Preprint
Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of informatio...
Article
An extended theoretical framework for the continuous population approach to forest inventories is derived. Here, we treat a simultaneous selection of sample points with any prescribed sampling intensity over a continuous population. Different ways to use available auxiliary information, for example, from remote sensing, by selection of approximatel...
Article
This study presents an approach for predicting stand-level forest attributes utilizing mobile laser scanning data collected as a nonprobability sample. Firstly, recordings of stem density were made at point locations every 10th metre along a subjectively chosen mobile laser scanning track in a forest stand. Secondly, kriging was applied to predict...
Article
Context: This study concerns model-based inference for estimating growing stock volume in large-area forest inventories, combining wall-to-wall Landsat data, a sample of laser data, and a sparse subsample of field data. Aims: We develop and evaluate novel estimators and variance estimators for the population mean volume, taking into account the u...
Article
This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation framewo...
Article
Positional errors may cause problems when field and remotely sensed data are combined in connection with forest surveys. In this study we evaluated the effects of such errors on statistical estimates of growing stock volume using model-assisted and model-based estimation. With model-assisted estimation, positional errors affect the model parameter...
Conference Paper
Highlights: The study presents a new model-based estimation approach for forest resources assessments combining field sample data and full cover Landsat data and sampled LiDAR data as auxiliary information. The results are of importance for developing forest inventory methodologies under REDD+ requirements.
Article
In this study, we investigate the use of model-based inference in forest surveys in which auxiliary data are available as a probability sample. We evaluate the effects of model form and sample size on estimators of growing stock volume, based on different types of remotely sensed auxiliary data. The study was performed through Monte Carlo sampling...
Article
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...
Article
By using more sophisticated sampling designs in forest field inventories it is possible to select more representative field samples. When full cover auxiliary information is available at the planning stage of a forest inventory, an efficient strategy for sampling is formed by making sure that the sample is well spread in the space spanned by the au...

Citations

... GEDI became operational in March 2019 and is the first space mission designed to produce three-dimensional metrics of forest canopy height and structure along with robust but straightforward estimates of AGB (i.e. forest carbon stocks), including accuracy and uncertainty metrics using established statistical inference (Dubayah et al 2022). GEDI was specifically designed to penetrate even the densest tropical forest canopies and retrieve a ground return beneath them. ...
... Our contingency approach, Generalized Hierarchical Model-Based inference (GHMB) [50,51], uses two levels of models: one linking ground data and footprint scale lidar metrics (i.e. the footprint biomass calibration models) and one linking those footprint biomass predictions to wall-to-wall ancillary data. The GHMB framework uses probability theory under the model-based paradigm to appropriately combine uncertainty from the two models, as wall-to-wall predictions form the basis of a large-area estimate of biomass [51][52][53]. Thus, the theory upon which GEDI's estimation of uncertainty is built can be extended to sensor fusion. ...
... No measurements are perfect and therefore should always be accompanied by some information on uncertainty that provides confidence bounds. If these uncertainties are provided then there is a wealth of Earth observation data that could be brought to bear on this challenge of monitoring carbon uptake, particularly over the tropics where there are very few alternative measurements, including estimates of aboveground biomass inferred from lidar and radar wavelengths [140][141][142] and fluxes of CO 2 inferred from atmospheric measurements of CO 2 30,143,144 . Adoption of these data into the emerging market requires dialogue between the regulators and the science community to improve access to emerging scientific findings, infrastructure, and quality-assured datasets. ...
... Optical remote sensing is sensitive to the green foliar component thereby well suited for estimating biomass of herbaceous vegetation, but not for forest ecosystems where the leaf component only accounts for a small fraction of the biomass carbon pool. Aerial and spaceborne light detection and ranging (LiDAR), lowfrequency synthetic aperture radar (SAR), and global ecosystem dynamics investigation (GEDI) provide information on tree height and thus biomass carbon in forests, but yet at limited spatial and temporal coverage (Baccini et al., 2012;Dubayah et al., 2020;Duncanson et al., 2022;Lang et al., 2022;Liu et al., 2015;Ni-Meister et al., 2022;Yu and Saatchi, 2016). ...
... It displays that the share of female students vary from 15% in Norway to almost 50% in Iceland, with the other countries being placed in between, with a similar trend in scientific staff. With the support of IUFRO, an important study of the impacts of gender in forestry research education has recently been initiated (Saarela 2020). Outside academia, the research stresses a conservative, dominant masculine culture (Follo 2011;Lidestav and Sjölander 2007). ...
... Remotely sensed data have been widely used in recent years for mapping and estimating forest inventory variables and biomass (White et al. 2016;Chirici et al. 2020;Saarela et al. 2020). Among remotesensing based techniques, active remote sensing is widely used nowadays due to its capacity to precisely describe the three-dimensional (3D) structure of forests (Wulder et al. 2012;McRoberts, Andersen, and Naesset 2014). ...
... Early multispectral time series approaches were published by Oindo 2002 [97] and Oindo & Skidmore 2002 [77] based on AVHRR data to investigate the species richness in Kenya. Since the year 2009, more and more studies integrated remote sensing time series data for the analysis of forest biodiversity using spectral diversity concepts [54,62,85,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116]. Studies that are solely based on LiDAR data have become specifically popular since the year 2020: on the one hand, all studies are based on mono-temporal remote sensing data, and on the other hand, the LiDAR data was derived from an airborne sensor [79,[117][118][119][120]. ...
... Each GEDI laser pulse illuminates ~25 m diameter footprint, and the reflected pulses are stored as a full waveform that records vertical objects continuously from the ground to the top of the canopy (Qi et al. 2019). The GEDI instrument consists of three lasers, in which two lasers are used at full power (power beams). ...
... These methods typically use Gaussian random fields or spatial smooths to capture spatial dependence within the data. A recent example of this is the 1 km AGB map created with GEDI data using a generalised hierarchical model [52,53]. ...
... One common application involves developing models that are applied over the entire forest area of interest (Guerra-Hernández, Tomé, and González-Ferreiro 2016;Guerra-Hernández et al. 2019) and the subsequent use of model-based or model-assisted inference approaches to produce estimates at the desired scale. The increasing availability of remotely sensed data has recently challenged the traditional method of performing forest inventories and has led to interest in these inference techniques (Ståhl et al. 2011;McRoberts, Naesset, and Gobakken 2013;Ståhl et al. 2016;Saarela et al. 2018). Like traditional designbased inference (Saarela et al. 2015), model-based inference provides regional estimates of total and mean values and also large scale mapping of forest characteristics. ...