Svetlana Saarela

Norwegian University of Life Sciences (NMBU) · Faculty of Environmental Sciences and Natural Resource Management

The main objective of this study was to demonstrate a method for monitoring tree occupancy and height in the alpine treeline ecotone using a time series of ALS data. We applied data collected in a longitudinal survey, comprising three spatially consistent campaigns from the years 2008, 2012 and 2018, on 25 sites along the Scandinavian Mountain Rang...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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.

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...

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...

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...