Explore the vast range of titles available.

While Landsat has proved to be effective for monitoring many elements of forest condition and change, the platform has well-documented limitations in measuring forest structure, the vertical distribution of the canopy. This is important because structure determines several key ecosystem functions, including: carbon storage; habitat suitability; and timber volume. Canopy structure is directly measured by LiDAR, and it should be possible to train Landsat structure models at a highly local scale with the dense, global sample of full waveform LiDAR observations collected by NASA’s Global Ecosystem Dynamics Investigation (GEDI). Local models are expected to perform better because: (a) such models may take advantage of localized correlations between structure and canopy surface reflectance; and (b) to the extent that models revert to the mean of the calibration data due to a lack of discrimination, local models will revert to a more representative mean. We tested Landsat-based relative height predictions using a new GEDI asset on Google Earth Engine, described here. Mean prediction error declined by 23% and important prediction biases at the extremes of the range of canopy height dropped as model calibration became more local, minimizing forest structure signal saturation commonly associated with Landsat and other passive optical sensors. Our results suggest that Landsat-based maps of structural variables such as height and biomass may substantially benefit from the kind of local calibration that GEDI’s dense sample of LiDAR data supports.

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 designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

There are several new and imminent space-based sensors intended to support mapping of forest structure and biomass. These instruments, along with advancing cloud-based mapping platforms, will soon contribute to a proliferation of biomass maps. One means of differentiating the quality of different maps and estimation strategies will be comparison of results against independent field-based estimates at various scales. The Forest Inventory and Analysis Program of the US Forest Service (FIA) maintains a designed sample of uniformly measured field plots across the conterminous United States. This paper reports production of a map of statistical estimates of mean biomass, created at approximately the finest scale (64,000-hectare hexagons) allowed by FIA’s sample density. This map may be useful for assessing the accuracy of future remotely sensed biomass estimates. Equally important, fine-scale mapping of FIA estimates highlights several ways in which field- and remote sensing-based methods must be aligned to ensure comparability. For example, the biomass in standing dead trees, which may or may not be included in biomass estimates, represents a source of potential discrepancy that FIA shows to be particularly important in the Western US. Likewise, alternative allometric equations (which link measurable tree dimensions such as diameter to difficult-to-measure variables like biomass) strongly impact biomass estimates in ways that can vary over short distances. Potential mismatch in the conditions counted as forests also varies greatly over space. Field-to-map comparisons will ideally minimize these sources of uncertainty by adopting common allometry, carbon pools, and forest definitions. Our national hexagon-level benchmark estimates, provided in Supplementary Files, therefore addresses multiple pools and allometric approaches independently, while providing explicit forest area and uncertainty information. This range of information is intended to allow scientists to minimize potential discrepancies in support of unambiguous validation.

The 2001 Forest Service Roadless Rule prohibits roadbuilding in forests across an area equivalent to the combined states of New York and Maine (236 000 km2). There have been recent assertions that roads are needed to prevent fire and to keep forests healthy. Despite twenty years of ongoing forest health monitoring and the unique scope and ecological significance of this network of roadless areas, there has to date been no integrated assessment of the relationship between roads and forest health. Here, this question was addressed by synthesizing different sources of nationally consistent, longitudinal monitoring data. Agency management records show that a lack of roads has not stopped fire prevention measures; fuel management activities in roadless areas have actually been more numerous on a per-square kilometer basis than elsewhere in the National Forest System, although activities in areas with roads cover larger areas. Historical fire maps indicate that forests with and without roads have burned at similar rates since the Rule took effect. The apparent neutrality of roads with respect to fire occurrence may be due to higher rates of human caused ignition near roads offsetting advantages related to more agile positioning of fire-fighting assets. Beyond the fire dimension of forest health, analysis of over 15 000 inventory plots showed that while tree root disease is only weakly correlated with proximity to roads, roads are strongly associated with the spread of invasive plant species in national forests. Non-native plants are twice as common within 152 meters (500 feet) of a road as farther away. Speculation that eliminating road prohibitions would improve forest health is not supported by nearly twenty years of monitoring data.

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 combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches.

Disturbance monitoring is an important application of the Landsat times series, both to monitor forest dynamics and to support wise forest management at a variety of spatial and temporal scales. In the last decade, there has been an acceleration in the development of approaches designed to put the Landsat archive to use towards these causes. Forest disturbance mapping has moved from using individual change-detection algorithms, which implement a single set of decision rules that may not apply well to a range of scenarios, to compiling ensembles of such algorithms. One approach that has greatly reduced disturbance detection error has been to combine individual algorithm outputs in Random Forest (RF) ensembles trained with disturbance reference data, a process called stacking (or secondary classification). Previous research has demonstrated more robust and sensitive detection of disturbance using stacking with both multialgorithm ensembles and multispectral ensembles (which make use of a single algorithm applied to multiple spectral bands). In this paper, we examined several additional dimensions of this problem, including: (1) type of algorithm (represented by processes using one image per year vs. all historical images); (2) spectral band choice (including both the basic Landsat reflectance bands and several popular indices based on those bands); (3) number of algorithm/spectral-band combinations needed; and (4) the value of including both algorithm and spectral band diversity in the ensembles. We found that ensemble performance substantially improved per number of model inputs if those inputs were drawn from a diversity of both algorithms and spectral bands. The best models included inputs from both algorithms, using different variants of shortwave-infrared (SWIR) and near-infrared (NIR) reflectance. Further disturbance detection improvement may depend upon the development of algorithms which either interrogate SWIR and NIR in new ways or better highlight disturbance signals in the visible wavelengths.

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

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 latitudes overflown by ISS (51.6 °S to 51.6 °N). One option for using the sample of waveforms contained within an individual grid cell to produce an estimate for that cell is hybrid inference, which explicitly incorporates both sampling design and model parameter covariance into estimates of variance around the population mean. We explored statistical properties of hybrid estimators applied in the context of GEDI, using simulations calibrated with lidar and field data from six diverse sites across the United States. We found hybrid estimators of mean biomass to be unbiased and the corresponding estimators of variance appeared to be asymptotically unbiased, with under-estimation of variance by approximately 20% when data from only two clusters (footprint tracks) were available. In our study areas, sampling error contributed more to overall estimates of variance than variability due to the model, and it was the design-based component of the variance that was the source of the variance estimator bias at small sample sizes. These results highlight the importance of maximizing GEDI's sample size in making precise biomass estimates. Given a set of assumptions discussed here, hybrid inference provides a viable framework for estimating biomass at the scale of a 1 km grid cell while formally accounting for both variability due to the model and sampling error.

Background National forest resource assessments and monitoring, commonly known as National Forest Inventories (NFI’s), constitute an important national information infrastructure in many countries. Methods This study presents details about developments of the NFI in China, including sampling and plot design, and the uses of alternative data sources, and specifically • reviews the evolution of the national forest inventory in China through the 20th and 21st centuries, with some reference to Europe and the US; • highlights the emergence of some common international themes: consistency of measurement; more efficient sampling designs; implementation of improved technology; expansion of the variables monitored; scientific transparency; • presents an example of how China’s expanding NFI exemplifies these global trends. Results Main results and important changes in China’s NFI are documented, both to support continued trend analysis and to provide data users with historical perspective. Conclusions New technologies and data needs ensure that the Chinese NFI, like the national inventories in other countries, will continue to evolve. Within the context of historical change and current conditions, likely directions for this evolution are suggested.

Forests are widely recognized as critical to combating climate change due to their ability to sequester and store carbon in the form of biomass. In recent years, the combined use of data from ground-based forest inventories and remotely sensed data from light detection and ranging (lidar) has proven useful for large-scale assessment of forest biomass, but airborne lidar is expensive and data acquisition is infeasible for many countries. By contrast, the spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument has collected freely available data for most of the world’s temperate and tropical forests since 2019. GEDI’s biomass products rely on models calibrated with a global network of field plots paired with GEDI waveforms simulated from airborne lidar to predict biomass. While this calibration strategy minimizes spatial and temporal offsets between field measurements and corresponding lidar returns, calibration data are sparse in many regions. Paraguay’s forests are known to be poorly represented in GEDI’s current calibration dataset, and here we demonstrate that local models calibrated opportunistically with on-orbit GEDI data and field surveys from Paraguay’s national forest inventory can be used with GEDI’s statistical estimators of aboveground biomass density (AGBD). We specify a protocol for opportunistically matching GEDI observations with field plots to calibrate a field-to-GEDI biomass model for use in GEDI’s hybrid statistical framework. Country-specific calibration using on-orbit data resulted in relatively accurate and unbiased predictions of footprint-level biomass, and importantly, supported the assumption underlying model-based inference that the model must ‘apply’ to the area of interest. Using a locally calibrated biomass model, we estimate that the mean AGBD in Paraguay is 65.55 Mg ha −1 , which coincides well with the design-based approach employed by the national forest inventory. The GEDI estimates for individual forest strata range from 52.34 Mg ha −1 to 103.88 Mg ha −1 . On average, the standard errors are 47% lower for estimates based on GEDI than the forest inventory, representing a significant gain in precision. Our research demonstrates that GEDI can be used by national forest inventories in countries that seek reliable estimates of AGBD, and that local calibration using existing field plots may be more appropriate in some applications than using GEDI global models, especially in regions where those models are sparsely calibrated.