| 1. |
- Duncanson, Laura, et al.
(author)
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Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- 2022
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In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 270
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Journal article (peer-reviewed)abstract
- NASA's Global Ecosystem Dynamics Investigation (GEDI) 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 model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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| 2. |
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| 3. |
- Turubanova, Svetlana, et al.
(author)
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Tree canopy extent and height change in Europe, 2001–2021, quantified using Landsat data archive
- 2023
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In: Remote Sensing of Environment. - 0034-4257 .- 1879-0704. ; 298
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Journal article (peer-reviewed)abstract
- European forests are among the most extensively studied ecosystems in the world, yet there are still debates about their recent dynamics. We modeled the changes in tree canopy height across Europe from 2001 to 2021 using the multidecadal spectral data from the Landsat archive and calibration data from Airborne Laser Scanning (ALS) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidars. Annual tree canopy height was modeled using regression tree ensembles and integrated with annual tree canopy removal maps to produce harmonized tree height map time series. From these time series, we derived annual tree canopy extent maps using a ≥ 5 m tree height threshold. The root-mean-square error (RMSE) for both ALS-calibrated and GEDI-calibrated tree canopy height maps was ≤4 m. The user's and producer's accuracies estimated using reference sample data are ≥94% for the tree canopy extent maps and ≥ 80% for the annual tree canopy removal maps. Analyzing the map time series, we found that the European tree canopy extent area increased by nearly 1% overall during the past two decades, with the largest increase observed in Eastern Europe, Southern Europe, and the British Isles. However, after the year 2016, the tree canopy extent in Europe declined. Some regions reduced their tree canopy extent between 2001 and 2021, with the highest reduction observed in Fennoscandia (3.5% net decrease). The continental extent of tall tree canopy forests (≥ 15 m height) decreased by 3% from 2001 to 2021. The recent decline in tree canopy extent agrees with the FAO statistics on timber harvesting intensification and with the increasing extent and severity of natural disturbances. The observed decreasing tree canopy height indicates a reduction in forest carbon storage capacity in Europe.
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| 4. |
- Valbuena, Ruben
(author)
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A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits
- 2022
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In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14
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Journal article (peer-reviewed)abstract
- Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)-namely, vegetation height, vegetation cover, and vertical structural complexity-to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide.
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| 5. |
- Valbuena, Ruben
(author)
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A global biodiversity observing system to unite monitoring and guide action
- 2023
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In: Nature ecology & evolution. - 2397-334X. ; 7, s. 1947-1952
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Journal article (peer-reviewed)abstract
- The rate and extent of global biodiversity change is surpassing our ability to measure, monitor and forecast trends. We propose an interconnected worldwide system of observation networks - a global biodiversity observing system (GBiOS) - to coordinate monitoring worldwide and inform action to reach international biodiversity targets.
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| 6. |
- Valbuena, Ruben
(author)
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Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions
- 2023
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In: Current Forestry Reports. - 2198-6436. ; 9, s. 490-501
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Research review (peer-reviewed)abstract
- Purpose of ReviewContinuous cover forestry (CCF) is a sustainable management approach for forestry in which forest stands are manipulated to create irregular stand structures with varied species composition. This approach differs greatly from the traditional approaches of plantation-based forestry, in which uniform monocultures are maintained, and thus, traditional methods of assessment, such as productivity (yield class) calculations, are less applicable. This creates a need to identify new methods to succeed the old and be of use in operational forestry and research. By applying remote sensing techniques to CCF, it may be possible to identify novel solutions to the challenges introduced through the adoption of CCF.Recent FindingsThere has been a limited amount of work published on the applications of remote sensing to CCF in the last decade. Research can primarily be characterised as explorations of different methods to quantify the target state of CCF and monitor indices of stand structural complexity during transformation to CCF, using terrestrial and aerial data collection techniques.SummaryWe identify a range of challenges associated with CCF and outline the outstanding gaps within the current body of research in need of further investigation, including a need for the development of new inventory methods using remote sensing techniques. We identify methods, such as individual tree models, that could be applied to CCF from other complex, heterogenous forest systems and propose the wider adoption of remote sensing including information for interested parties to get started.
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| 7. |
- Valbuena, Ruben
(author)
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Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR
- 2023
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In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 290
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Journal article (peer-reviewed)abstract
- Forest landscape restoration is a global priority to mitigate negative effects of climate change, conserve biodiversity, and ensure future sustainability of forests, with international pledges concentrated in tropical forest regions. To hold restoration efforts accountable and monitor their outcomes, traditional strategies for monitoring tree cover increase by field surveys are falling short, because they are labor-intensive and costly. Meanwhile remote sensing approaches have not been able to distinguish different forest types that result from utilizing different restoration approaches (conservation versus production focus). Unoccupied Aerial Vehicles (UAV) with light detection and ranging (LiDAR) sensors can observe forests` vertical and horizontal structural variation, which has the potential to distinguish forest types. In this study, we explored this potential of UAV-borne LiDAR to distinguish forest types in landscapes under restoration in southeastern Brazil by using a supervised classification method. The study area encompassed 150 forest plots with six forest types divided in two forest groups: conservation (remnant forests, natural regrowth, and active restoration plantings) and production (monoculture, mixed, and abandoned plantations) forests. UAV-borne LiDAR data was used to extract several Canopy Height Model (CHM), voxel, and point cloud statistic based metrics at a high resolution for analysis. Using a random forest classification model we could successfully classify conservation and production forests (90% accuracy). Classification of the entire set of six types was less accurate (62%) and the confusion matrix showed a divide between conservation and production types. Understory Leaf Area Index (LAI) and the variation in vegetation density in the upper half of the canopy were the most important classification metrics. In particular, LAI understory showed the most variation, and may help advance ecological understanding in restoration. The difference in classification success underlines the difficulty of distinguishing individual forest types that are very similar in management, regeneration dynamics, and structure. In a restoration context, we showed the ability of UAV-borne LiDAR to identify complex forest structures at a plot scale and identify groups and types widely distributed across different restored landscapes with medium to high accuracy. Future research may explore a fusion of UAV-borne LiDAR with optical sensors , include successional stages in the analyses to further characterize , distinguish forest types and their contributions to landscape restoration.
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| 8. |
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| 9. |
- Valbuena, Ruben
(author)
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Glasgow forest declaration needs new modes of data ownership
- 2022
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In: Nature Climate Change. - : Springer Science and Business Media LLC. - 1758-678X .- 1758-6798. ; 12, s. 415-417
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Journal article (peer-reviewed)abstract
- Monitoring progress in the Glasgow 'Declaration on Forests' remains impossible without open sharing of data. Three actions are required if this declaration is to succeed.
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| 10. |
- Valbuena, Ruben
(author)
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Optimizing the airborne laser scanning estimation of basal area larger than mean (BALM): An indicator of cohort balance in forests
- 2022
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In: Ecological Indicators. - : Elsevier BV. - 1470-160X .- 1872-7034. ; 142
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Journal article (peer-reviewed)abstract
- Airborne laser scanning (ALS) assisted basal area larger than mean (BALM) estimation measures the cohort balance in forests and provides adequate opportunities to describe forest structure. However, a problem still exists that how the plot size, sample size (number of trees), and ALS point density affect the BALM estimation. We tackled this question by using both field and ALS data from a typical managed boreal forest area in Finland. Various concentric circular plots (1-15 m radii) were simulated within the actual field plots (squared) and the optimal plot size and sample size were selected by observing changes in the absolute correlation between BALM estimates and various ALS metrics. Instability in the correlation was found at the smaller concentric circular plots (1-5 m radii) and sample sizes (less than 6 trees) but as the plot size and sample size increased, the correlation followed a convex curve. The maximum correlation was found between a concentric circular plot size 11-14 m radii (380-615 m2 area) and sample size 50-80 trees which could be the optimal plot size and sample size for a reliable BALM estimation. With regards to the ALS point density, no major effects were observed on the relationship between BALM estimates and various ALS metrics unless the point density is less than at least 5 points m 2. The point density of the current nationwide ALS survey is matching the minimum point density requirement obtained in this study and thus it is suitable for a reliable forest structural assessment.
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