Search

Researchers in Earth and environmental science can extract incredible value from high- resolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data.

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community's use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building. [ABSTRACT FROM AUTHOR]

Nitrogen (N) has been linked to different ecosystem processes, and retrieving this important foliar biochemical constituent from remote sensing observations is of widespread interest. Since N is not explicitly represented in physically based radiative transfer models, empirical methods have been used as an alternative. The spectral bands selected during the calibration of empirical methods have been interpreted in the context of light-N interactions and, consequently, ecosystem function. The low amount of leaves on shrubs and their sparse distribution in drylands create an environment, in which the canopy structure and the bright background soil play an important role in the canopy total radiation budget. In this paper, we examine the assumption that removing the impact of canopy structure and soil will result in improved N retrieval using the empirical methods. We report the inconsistencies in the selection of spectral bands among the empirical approaches. Moreover, these methods are sensitive to the scale of the study and band transformations. Using the generalized theory of canopy spectral invariants, we found that at the canopy scale, a combination of canopy structure and soil dominates the total canopy radiation. At the plot scale, soil contributes up to 95% of the total reflectance. Correction for these two confounding factors leads to no correlation between N and vegetation reflectance at both scales. We conclude that while cross-validated predictive models may be statistically achievable, caution should be taken when interpreting their results in the context of N-light interactions and ecosystem function. Our approach can be extended to all terrestrial ecosystems with multiple layers of canopy and understory. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key biophysical variable and an ecosystem condition indicator that is measured from multiple methods. In this study, LAI was measured by a terrestrial laser scanner (TLS) and Li-Cor LAI-2200 plant canopy analyzer for understanding differences. A total of six different methods that consider leaf clumping, leaf-wood separation, and orthographic and stereographic projection were compared. A reasonable agreement among all methods for LAI estimates was observed (i.e., correlations r > 0.50). The Bayesian linear regression (BLR) approach was used to scale up the six different LAI estimates and to produce continuous field surfaces for the Oak Openings Region in northwest Ohio using Landsat TM-derived spectral vegetation indices (weighted difference vegetation index [WDVI], difference vegetation index [DVI], normalized difference vegetation index [NDVI], soil-adjusted vegetation index [SAVI], and perpendicular vegetation index 3 [PVI3]). The BLR approach provides details about the parameter uncertainties that may arise from foliage and wooden biomass and can be used for model comparisons. In this study, the TLS estimates of foliage derived by orthographic projection had the lowest residual scatter and overall model uncertainty. The deviation departure from the mean BLR estimates revealed that sparse and open areas were associated with the highest error and spatial uncertainties. [ABSTRACT FROM AUTHOR]

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM's sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems. [ABSTRACT FROM AUTHOR]