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The characterization of fine temporal-resolution land surface dynamics from broadband optical satellite sensors is constrained by sparse acquisitions of high-quality imagery; interscene variation in radiometric, phenological, atmospheric, and illumination conditions; and subpixel variability in heterogeneous environments. In this letter, we address these concerns by developing and testing the automatic adaptive signature generalization and regression (AASGr) algorithm. Provided a robust reference map corresponding to the date of one image, AASGr automates the prediction of continuous fields maps from imagery time series that is adaptive to the spectral and radiometric characteristics of each target image and thereby requires neither atmospheric correction nor data normalization. We tested AASGr on a 22-year Landsat time series to quantify subannual impervious fractional cover dynamics in Houston, TX—an area characterized by a high degree of spatial heterogeneity in surface cover and high frequency in land cover change. The map time series achieved high accuracy in a three-part validation procedure and reveals spatio-temporal dynamics of urban intensification and extensification at a level of detail previously elusive in discrete classifications or coarse temporal-resolution map products. The automation of continuous fields time series is enabling a new generation of land surface products capable of characterizing precise morphologies along a continuum of spatio-temporal change. While AASGr was applied here to predict subpixel impervious fractional cover from Landsat imagery, the method is generalizable to a range of imagery and applications requiring dense continuous fields time series with uncertainty estimates of geophysical and biochemical characteristics, such as leaf area index, biomass, and albedo.

The goal of elucidating the primary mechanisms constraining the assembly and distribution of biodiversity remains among the central unresolved challenges facing the field of ecology. Simulation studies and experimental manipulations have focused on how patterns in community assembly result from bivariate relationships along productivity or environmental gradients. However, the joint influence of multiple resource gradients on the distribution of species richness in natural communities remains understudied. Using data from a large network of multiscale vegetation plots across forests and woodlands of the southeastern United States, we find significant evidence for the scale-dependent, joint constraints of forest structure and soil resources on the distribution of vascular plant species richness. In addition to their significant partial effects on species richness, understory light levels and soil fertility positively interact, suggesting a trade-off between the two limiting resources with species richness peaking both in high-light, low-fertility conditions as well as low-light, high-fertility settings. This finding provides a novel perspective on the biodiversity-productivity relationship that suggests a transition in limiting resources from soil nutrients to light availability when enhanced productivity results in reduced light resources for subordinate individuals. Results likewise have meaningful implications for our understanding of scale-dependent community assembly processes as size-asymmetric competition replaces environmental filtering as the primary assembly mechanism structuring temperate forest communities along an increasing soil fertility gradient.

In 2017, Hurricane Harvey caused substantial loss of life and property in the swiftly urbanizing region of Houston, TX. Now in its wake, researchers are tasked with investigating how to plan for an...

As one of the most dynamic aspects of global environmental change, land cover change (LCC) has a profound impact on terrestrial carbon sequestration. However, LCC-induced carbon fluxes are still the most uncertain terms in global and regional carbon budgets. Ecosystem gross primary production (GPP) is the total carbon uptake by vegetation through photosynthesis, serving as a major control on ecosystem function and land carbon balance during and after the modification of the land surface. However, accurately capturing LCC-induced GPP changes requires both high-quality land cover data and controlling for variation driven by other environmental factors such as climate. In this study, we comprehensively examined the effects of LCC on annual GPP trends over the conterminous United States (CONUS) from 2001 to 2016 using the USGS National Land Cover Database, a remote sensing-driven ecosystem model, and the Google Earth Engine cloud computing platform. We designed a series of model experiments to identify LCC effects on GPP by controlling climate effects. During the study period, LCC exerted a strong negative effect on total GPP across the CONUS ([-2.2, -1.8] Tg C yr−2), while climate had smaller positive effects ([0.17, 0. 92] Tg C yr−2). The LCC-induced reduction of GPP was mainly caused by net forest loss ([-1.98, -1.39] Tg C yr−2) and urban expansion ([-2.03, -1.92] Tg C yr−2), but was partially offset by increases in crop area ([+0.66, +0.79] Tg C yr−2). Ensemble simulations from TRENDY did not capture the strong negative LCC influences on GPP, likely due to limitations of the adopted land use/cover data. Our study provides a novel perspective on LCC-induced GPP changes, which could help to improve our understanding of ecosystem function changes and constrain the estimation of land carbon balance in the context of anthropogenic activity and climate change.

In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling.

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Understanding spatial patterns of diversity in tropical forests is indispensable for their sustainable use and conservation. Recent studies have reported relationships between forest structure and α-diversity. While tree α-diversity is difficult to map via remote sensing, large-scale forest structure models are becoming more common, which would facilitate mapping the relationship between tree α-diversity and forest structure, contributing to our understanding of biogeographic patterns in the tropics. We developed a methodology to map tree α-diversity in tropical forest regions at 50 m spatial resolution using α-diversity estimates from forest inventories as response variables and forest structural metrics and environmental variables as predictors. To include forest structural metrics in our modelling, we first developed a method to map seven of these metrics integrating discrete light detection and ranging (LiDAR), multispectral, and synthetic aperture radar imagery (SAR). We evaluated this methodology in the Chocó region of Colombia, a tropical forest with high tree diversity and complex forest structure. The relative errors (REs) of the random forest models used to map the seven forest structural variables ranged from low (6%) to moderate (35%). The α-diversity maps had moderate RE; the maps of Simpson and Shannon diversity indices had the lowest RE (9% and 13%), followed by richness (17%), while Shannon and Simpson effective number of species indices had the highest RE, 27% and 47%, respectively. The highest concentrations of tree α-diversity are located along the Pacific Coast from the centre to the northwest of the Chocó Region and in non-flooded forest along the boundary between the Chocó region and the Andes. Our results reveal strong relationships between canopy structure and tree α-diversity, providing support for ecological theories that link structure to diversity via niche partitioning and environmental conditions. With modification, our methods could be applied to assess tree α-diversity of any tropical forest where tree α-diversity field observations coincident with LiDAR data.

Questions Can forest structure significantly predict tree species diversity in the forests of the North Carolina Piedmont? If so, which structural attributes are most correlated with it, and how effective are they when used in concert in a generalized predictive model of tree species diversity? Location North Carolina Piedmont, USA. Methods Using a set of geographically distributed Forest inventory and analysis (FIA) plots (n = 972), we analysed Spearman correlations between 15 measures of forest structure and five indices of tree species diversity. We predict tree species diversity based on structural predictors using support vector regression (SVR) models, assessing model fit via ten-fold cross-validation. Results Results show a consistent and significant relationship between most structural attributes and indices of tree species diversity. Among all structural predictors, maximum height, basal area size inequality (basal area Gini coefficient) and skewness of the basal area distribution (Weibull shape) exhibited the strongest correlations with indices of tree species diversity. Predictive SVR models trained solely with structural attributes explained 44–61% of the variance in tree species diversity in the full Piedmont data set, and 22–71% of the variance in subsets defined by stand origin and forest type. Conclusions Results confirm that forest structure alone was able to predict a substantial portion of the variance in tree species diversity without accounting for other known predictors of diversity in the North Carolina Piedmont, such as environment, soil conditions and site history. Beyond the theoretical implications of unravelling primary patterns underlying tree species diversity, these findings highlight the empirical basis and potential for utilizing forest structure in predictive models of tree species diversity over large geographic regions.

Urbanization results in increasing impervious surfaces with the potential to threaten fragile environments and heighten flood risks. In the United States, research on the social processes driving urbanization has tended to focus on the twenty-first century, but less is known about how temporal trends arose from the spatial layout of the urban land upon which this growth was founded. To address this gap, we present a novel interdisciplinary synthesis using neighborhood-level census data in tandem with a satellite-derived annual land cover change time series to assess the role of race, affluence, and socioeconomic status in shaping spatio-temporal urbanization in the Houston metropolitan area from 1997−2016. Results from cross-sectional and temporal regression models indicate that while social dynamics associated with historical versus recent urbanization are related, they are not identical. Thus, while temporal change in Houston's urbanization is driven primarily by socioeconomic status, the social dynamics associated with spatial disparities in urbanization relate primarily to race, regardless of socioeconomic status. These results are noteworthy as urbanization in Houston does not fully comport with existing theoretical perspectives or with empirical findings nationally. Instead, we suggest these findings reflect the city’s politics and culture surrounding land use. Thus, beyond its important social and environmental implications, this study affirms the utility of fusing socio-demographic data with satellite remote sensing of urban growth, and highlights the value of the socioenvironmental succession framework for characterizing urbanization as a recursive process in space and time.

Forests are declining across the globe due primarily to the overutilization of forest resources, particularly in developing countries. China, the world’s largest developing country, is no exception. After the establishment of the People’s Republic of China in 1949, it experienced a severe decline in forest resources, eventually leading to devastating natural disasters in the late 1990s. In response to these disasters, the Chinese government adopted a series of new forest policies including the Conversion of Cropland to Forest Program (CCFP) and the public welfare forest (PWF) program to mitigate the effects of future extreme weather events. The CCFP targets soil and water conservation by incentivizing farmers to convert cropland on steep slopes or other ecologically sensitive areas to forests. The PWF program prohibits logging to conserve natural forests. This case study was devised to examine the potential for using multitemporal remotely sensed data to monitor forest development associated with the CCFP and PWF programs in Tiantangzhai township, Anhui province, China. Results from the multitemporal remote sensing analysis indicate that the Tiantangzhai study area experienced a net increase in forest cover during the 21-year period from 1992 to 2013, especially after the implementation of the CCFP and PWF programs in 2002 and 1999, respectively. In addition to quantifying change in the areal extent of forest cover, three remotely sensed forest succession indexes were developed to characterize the temporal trajectory of forest canopy development. The canopy closure index, the maturity index, and the synergistic succession index reveal net forest canopy development including higher density canopies in younger forests and increasing canopy complexity in mature forests. These findings confirm our hypothesis that the CCFP and PWF programs had an observable effect upon both the quantity and quality of forest development within the Tiantangzhai study area.

The central role of floristic diversity in maintaining habitat integrity and ecosystem function has propelled efforts to map and monitor its distribution across forest landscapes. While biodiversity studies have traditionally relied largely on ground-based observations, the immensity of the task of generating accurate, repeatable, and spatially-continuous data on biodiversity patterns at large scales has stimulated the development of remote-sensing methods for scaling up from field plot measurements. One such approach is through integrated LiDAR and hyperspectral remote-sensing. However, despite their efficiencies in cost and effort, LiDAR-hyperspectral sensors are still highly constrained in structurally- and taxonomically-heterogeneous forests - especially when species' cover is smaller than the image resolution, intertwined with neighboring taxa, or otherwise obscured by overlapping canopy strata. In light of these challenges, this study goes beyond the remote characterization of upper canopy diversity to instead model total vascular plant species richness in a continuous-cover North Carolina Piedmont forest landscape. We focus on two related, but parallel, tasks. First, we demonstrate an application of predictive biodiversity mapping, using nonparametric models trained with spatially-nested field plots and aerial LiDAR-hyperspectral data, to predict spatially-explicit landscape patterns in floristic diversity across seven spatial scales between 0.01-900 m2 . Second, we employ bivariate parametric models to test the significance of individual, remotely-sensed predictors of plant richness to determine how parameter estimates vary with scale. Cross-validated results indicate that predictive models were able to account for 15-70% of variance in plant richness, with LiDAR-derived estimates of topography and forest structural complexity, as well as spectral variance in hyperspectral imagery explaining the largest portion of variance in diversity levels. Importantly, bivariate tests provide evidence of scale-dependence among predictors, such that remotely-sensed variables significantly predict plant richness only at spatial scales that sufficiently subsume geolocational imprecision between remotely-sensed and field data, and best align with stand components including plant size and density, as well as canopy gaps and understory growth patterns. Beyond their insights into the scale-dependent patterns and drivers of plant diversity in Piedmont forests, these results highlight the potential of remotely-sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air- and space-borne platforms.

Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG) algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time.

Biodiversity conservation strategies around the world have been criticized when the goals of international organizations clash with the needs and traditions of local people. While the characterization of global conservation initiatives as a clash between scientifically-informed environmental policies and indigenous knowledge may retain discursive value in explaining the interaction of contending epistemologies, it is nonetheless an over-simplification of a dynamic, complicated and sometimes opaque and contradictory process. This paper sheds light on some of the conservation programs in southwest China as a case where these seemingly distinct knowledge regimes lie not in stark contrast, but in fact coexist within a localized discourse on biological and cultural diversity. In the example of the sacred site tradition of northwest Yunnan, disparate knowledge regimes have been negotiated and reinterpreted at the local, and even individual level to form dynamic and unique motivations for a conservation ethic. In this negotiation of indigenous and global epistemologies, classic distinctions separating global and local interests prove erroneous, or at the very least, unnecessary.