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The addition of interferometric backscattering pairs to the conventional polarimetric synthetic aperture radar (SAR) data over forests and other vegetated areas increases the dimensionality of the data space, in principle enabling the estimation of a larger number of parameters.
Spanning nearly four decades, the remarkable Landsat program has continuously provided data about the Earth s surface, including detailed maps of vegetation, land use, forest extent and health, surface water, population distribution, as well as how these features have changed over time. Managed by NASA and the U.S. Geological Survey, Landsat s series of satellites obtain data through passive remote sensing, or the use of sensors to read the energy reflected or emitted from the Earth s surface. After the data from the sensors is processed and analyzed, it can be applied to create information-rich images of the planet. While the Landsat program has launched seven satellites since 1972, only Landsat 5 and 7 are currently operating. The next spacecraft in line to ensure continuity of data for years to come is the Landsat Data Continuity Mission (LDCM). Planned for launch in 2012, LDCM will take measurements of the Earth in visible, nearinfrared, shortwave infrared, and thermal infrared bands. In addition to widespread use for land use planning and monitoring on local to regional scales, support for disaster response and evaluations, as well as water use monitoring, LDCM measurements will directly serve NASA s research in the areas of climate, the carbon cycle, ecosystems, the water cycle, biogeochemistry, and Earth s surface and interior.
Pinyon-juniper woodlands (PJW) are a vital habitat and food source for several wildlife species and a source of both utility and cultural importance for Indigenous groups. In 2021, amidst a decades-long drought, an extensive juniper mortality event occurred at Wupatki National Monument (WNM) in Arizona. In response, the National Park Service (NPS) is evaluating which land management practices will be beneficial. In partnership with the NPS, the NASA DEVELOP team used remote sensing data to map PJW mortality and analyze the relation of tree mortality to stand density, climate, and topography in north-central Arizona from 2015 to 2021. To identify the extent of PJW, the team performed an unsupervised classification using National Agricultural Imagery Program (NAIP) data with validation sources including NPS-created land cover maps, Landscape Fire and Resource Management Planning Tools (LANDFIRE), NPS and United States Forest Service (USFS) vegetation maps, and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG), Soil Moisture Active Passive (SMAP), Shuttle Radar Topography Mission (SRTM), and Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) were used to analyze factors contributing to pinyon-juniper mortality. Although no relationships were found in the broader study region, PJW mortality was weakly correlated to elevation, soil moisture, and land surface temperature within WNM. Results from this study can inform NPS vegetation management that best protects natural and cultural resources.
The knowledge of vegetation dielectric behavior is important in studying the scattering properties of the vegetation canopy and radar backscatter modelling. Until now, a limited number of studies have been published on the dielectric properties in the boreal forest context. This paper presents the results of the dielectric constant as a function of depth in the trunks of two common boreal forest species: black spruce and trembling aspen, obtained from field measurements. The microwave penetration depth for the two species is estimated at C, L, and P bands and used to derive the equivalent dielectric constant for the trunk as a whole. The backscatter modelling is carried out in the case of black spruce and the results are compared with the JPL AIRSAR data. The sensitivity of the backscatter coefficient to the dielectric constant is also examined.
The study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical-based remote sensing platforms, which are often above-canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.
Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides a powerful proxy for gross primary productivity (GPP). It is particularly promising in boreal ecosystems where seasonal downregulation of photosynthesis occurs without significant changes in canopy structure or chlorophyll content. The use of SIF as a proxy for GPP is complicated by inherent non-linearities due to both physical (illumination effects) and ecophysiological (light use efficiencies) controls at fine spatial (tower/leaf) and temporal (half-hourly) scales. Here, to study the SIF-GPP relationship, we investigated the diurnal and seasonal dynamics of continuous tower-based measurements of SIF, GPP, and common vegetation indices at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA over the course of two years. We find that SIF outperforms other vegetation indices as a proxy for GPP at all temporal scales but shows a non-linear relationship with GPP at a half-hourly resolution. At small temporal scales, SIF and GPP are predominantly driven by light and non-linearity between SIF and GPP is due to the light saturation of GPP. Averaged over daily and monthly scales, the relationship between SIF and GPP is linear due to a reduction in the observed PAR range. Seasonal changes in the light responses of SIF and GPP are driven by changes in light use efficiency which co-vary with changes in temperature, while illumination and canopy structure partially linearize the SIF-GPP relationship. Additionally, we find that the SIF-GPP relationship has a seasonal dependency. Our results help clarify the utility of SIF for estimating carbon assimilation in boreal forests.
The objective of this report is to generate a shade (shadow) image of forested areas from Landsat MSS data by implementing a linear mixing model, where shadow is considered as one of the primary components in a pixel. The shade images are related to the observed variation in forest structure; i.e., the proportion of inferred shadow in a pixel is related to different forest ages, forest types, and tree crown cover. The constrained least-squares method is used to generate shade images for forest of eucalyptus and vegetation of 'cerrado' over the Itapeva study area in Brazil. The resulted shade images may explain the difference on ages for forest of eucalyptus and the difference on tree crown cover for vegetation of cerrado.
Lianas (woody climbers) are crucial components of tropical forests and they have been increasingly recognized to have profound effects on tropical forest carbon dynamics. Despite their importance, lianas' representation in vegetation models remains limited, partly due to the complexity of liana-tree dynamics and the diversity in liana life history strategies. This paper provides a comprehensive review of advances and challenges for mechanistically representing lianas in forest ecosystem models and a proposed path towards effectively representing lianas in these models. Defining a liana plant functional type is a significant challenge because of the high morphological and physiological diversity amongst liana species, and because of their structural association with trees. Here, we identify critical liana traits that likely should contribute to establishing a liana plant functional type, along with key processes to properly represent lianas in ecosystem models. Subsequently, we discuss a variety of possible liana implementation strategies with their associated strengths, limitations, computational costs and data requirements. A fundamental redesign of the tree-centric demographic vegetation models seems appropriate to accommodate the unique growth and competition strategies of lianas. We illustrate the potential of such models with a single-site case study where we disentangle putative mechanisms of liana increasing abundance. Furthermore, we underscore the critical need for comprehensive liana demographic and functional data (including long-term, physiological, and pantropical observations) for the qualitative implementation and evaluation in the proposed modeling efforts. Currently, there is a scarcity of liana data and the data that do exist have a neotropical bias. We finally introduce a new liana functional trait database that can centralize existing liana trait data, incentivize improved data gathering and thus facilitate model development and scientific analyses.
Forests in Dynamic Global Vegetation Models (DGVMs) have historically been simulated as area-averaged plant functional types in each gridcell instead of representing communities of trees of different sizes and ages (demography). Just as the behaviour of a tree differs according to its ontogeny, so the behaviour of forests is known to differ depending on their demography. Accurately simulating demography is therefore key in order to address questions on afforestation and management strategies, as well as assessments of resilience of forests to disturbances such as drought and fire or diversity changes after a disturbance. Ultimately, demography determines the overall forest biomass in natural forests and is a key arbiter of growth and mortality rates. DGVMs are now able to simulate size and age structure of the trees in forests. However, these models have so far not been benchmarked alongside each other. We evaluate 6 DGVMs (BiomeE, CABLE-POP, FATES, LPJ-GUESS, JULES-RED, ORCHIDEE) against observations on regrowth dynamics as well as natural forests at boreal, temperate and tropical sites. We examine whether the models capture observed regrowth dynamics after disturbance, well-known stand size structure and well-established processes such as self-thinning. We outline the planned route forward towards a standardised international benchmarking framework for demographic DGVMs.
The pixel size in the present day Remote Sensing systems is large enough to include different types of land cover. Depending upon the target area, several components may be present within the pixel. In forested areas, generally, three main components are present: tree canopy, soil (understory), and shadow. The objective is to generate a shade (shadow) image of forested areas from multispectral measurements of LANDSAT MSS (Multispectral Scanner) data by implementing a linear mixing model, where shadow is considered as one of the primary components in a pixel. The shade images are related to the observed variation in forest structure, i.e., the proportion of inferred shadow in a pixel is related to different forest ages, forest types, and tree crown cover. The Constrained Least Squares (CLS) method is used to generate shade images for forest of eucalyptus and vegetation of cerrado using LANDSAT MSS imagery over Itapeva study area in Brazil. The resulted shade images may explain the difference on ages for forest of eucalyptus and the difference on three crown cover for vegetation of cerrado.
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Consideration is given to the effects of canopy closure, understory vegetation, and background reflectance on the relationship between Landsat TM data and the leaf area index (LAI) of temperate coniferous forests in the western U.S. A methodology for correcting TM data for atmospheric conditions and sun-surface-sensor geometry is discussed. Strong inverse curvilinear relationships were found between coniferous forest LAI and TM bands 3 and 5. It is suggested that these inverse relationships are due to increased reflectance of understory vegetation and background in open stands of lower LAI and decreased reflectance of the overstory in closed canopy stands with higher LAI.
Lidar altimeter observations of vegetated landscapes provide a time-resolved measure of laser pulse backscatter energy from canopy surfaces and the underlying ground. Airborne lidar altimeter data was acquired using the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) for a successional sequence of four, closed-canopy, deciduous forest stands in eastern Maryland. The four stands were selected so as to include a range of canopy structures of importance to forest ecosystem function, including variation in the height and roughness of the outer-most canopy surface and the vertical organization of canopy stories and gaps. The character of the SLICER backscatter signal is described and a method is developed that accounts for occlusion of the laser energy by canopy surfaces, transforming the backscatter signal to a canopy height profile (CHP) that quantitatively represents the relative vertical distribution of canopy surface area. The transformation applies an increased weighting to the backscatter amplitude as a function of closure through the canopy and assumes a horizontally random distribution of the canopy components. SLICER CHPs, averaged over areas of overlap where lidar ground tracks intersect, are shown to be highly reproducible. CHP transects across the four stands reveal spatial variations in vegetation, at the scale of the individual 10 m diameter laser footprints, within and between stands. Averaged SLICER CHPs are compared to analogous height profile results derived from ground-based sightings to plant intercepts measured on plots within the four stands. Tbe plots were located on the segments of the lidar ground tracks from which averaged SLICER CHPs were derived, and the ground observations were acquired within two weeks of the SLICER data acquisition to minimize temporal change. The differences in canopy structure between the four stands is similarly described by the SLICER and ground-based CHP results, however a Chi-square test of similarity documents differences that are statistically significant. The differences are discussed in terms of measurement properties that define the smoothness of the resulting CHPs and Lidar Altimeter Measurements of Canopy Structure - Harding et al. canopy properties that may vertically bias the CHP representations of canopy structure. The statistical differences are most likely due to the more noisy character of the ground-based CHPs, especially high in the canopy where ground-based sightings are rare resulting in an underestimate of canopy surface area and height, and to departures from the assumption of horizontal randomness which bias the CHPs toward the observer (upward for SLICER and downward for ground-based CHPs). The results demonstrate that the SLICER observations reliably provide a measure of canopy structure that reveals ecologically interesting structural variations such as those characterizing a successional sequence of closed-canopy, broadleaf forest stands.
Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi‐decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following stand‐replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub‐model–parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under‐predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant–soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED‐2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models.
Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.
The overarching objective of this ongoing project is to assess the role of vegetation within climate change. Forests capture carbon, a green house gas, from the atmosphere. Thus, any change, whether, natural (e.g. growth, fire, death) or due to anthropogenic activity (e.g. logging, burning, urbanization) may have a significant impact on the Earth's carbon cycle. Through the use of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and NASA's Laser Vegetation Imaging Sensor (LVIS), which are airborne Light Detection and Ranging (LIDAR) remote sensing technologies, we gather data to estimate the amount of carbon contained in forests and how the content changes over time. UAVSAR and LVIS sensors were sent all over the world with the objective of mapping out terrain to gather tree canopy height and biomass data; This data is in turn used to correlate vegetation with the global carbon cycle around the world.
Ground-level ozone at high levels poses health threats to exposed flora and fauna, including negative impacts to human health. While concern is common regarding depletion of ozone in the stratosphere, portions of the urban and rural United States periodically have high ambient levels of tropospheric ozone on the ground. Ozone pollution can cause a variety of impacts to susceptible vegetation (e.g., Ponderosa and Jeffrey pine species in the southwestern United States), such as stunted growth, alteration of growth form, needle or leaf chlorosis, and impaired ability to withstand drought-induced water stress. In addition, Southern Californian forests with high ozone exposures have been recently subject to multiyear droughts that have led to extensive forest overstory mortality from insect outbreaks and increased incidence of wildfires. Residual forests in these impacted areas may be more vulnerable to high ozone exposures and to other forest threats than ever before. NASA sensors collect a wealth of atmospheric data that have been used recently for mapping and monitoring regional tropospheric ozone levels. AIRS (Atmospheric Infrared Sounder), OMI (Ozone Monitoring Instrument), MLS (Microwave Limb Sounder), and TES (Tropospheric Emission Spectrometer) data could be used to assess forest ecosystem exposure to ozone. Such NASA data hold promise for providing better or at least complementary synoptic information on ground-level ozone levels that Federal agency partners can use to assess forest health trends and to mitigate the threats as needed in compliance with Federal laws and mandates. NASA data products on ozone concentrations may be able to aid applications of DSTs (decision support tools) adopted by the USDA FS (U.S. Department of Agriculture Forest Service) and by the NPS (National Park Service), such as the Ozone Calculator, in which ground ozone estimates are employed to assess ozone impacts to forested vegetation.
The clumping index (CI) quantifies the non-random distribution of vegetation across space, which regulates the canopy radiative transfer processes and land surface carbon, water, and energy cycles. However, its impact on global surface energy budget, particularly sensible heat fluxes and surface temperature, is not well understood. Additionally, while there have been studies showing significant seasonal variations in CI, the impacts of these variations on surface energy fluxes remain unclear. In this study, we incorporated satellite-derived spatially and temporally explicit CI data into the Community Land Model version 5 (CLM5) to evaluate the effects of CI on global land energy fluxes. Our results showed that including CI increased the global mean sensible heat flux dissipated from ground by 3.9 W m -2 (~18%), while decreasing the global mean vegetation sensible heat flux by 4.9 W m -2 (~65%), resulting in a total sensible heat decrease of 1.0 W m -2 (~3%). In contrast, CI increased the global mean latent heat flux by 0.8 W m -2 (~2%), primarily due to increased evapotranspiration (up to 11 W m -2 ) in tropical regions. We also found considerable impacts of seasonal variations in CI, particularly on sensible heat fluxes from ground and vegetation in evergreen needleleaf forests and deciduous needleleaf forests. Using constant CI rather than considering seasonal variations resulted in significant overestimation and underestimation of the sensible heat fluxes from vegetation and ground, respectively, in boreal summer. In conclusion, these changes in surface energy fluxes caused by CI and its seasonal variations led to up to 1.7 and 0.5 K differences in simulated mean ground temperature. These findings highlight the importance of including CI and considering its seasonal variations in modeling land surface energy fluxes.