Leveraging Google Earth Engine to Couple Landsat and MODIS for Detecting Phenological Changes in Semi-Arid Ecosystems

High spatial and temporal imagery are necessary to monitor phenological changes in semi-arid regions, but it is challenging to obtain this coverage using public satellites. Moderate Resolution Imaging Spectroradiometer (MODIS) has a repeat interval of one to two days, but coarse spatial resolutions up to 1000 m. Landsat has a higher spatial resolution of 30 m, but a 16-day period. StarFM algorithm combines multi-resolution satellite systems to interpolate data with MODIS temporal and Landsat spatial scales. We use Google Earth Engine (GEE) to preprocess the data. This improved imagery can be leveraged to classify vegetation species with short phenological cycles

Vegetation Mapping in a Dryland Ecosystem Using Multi-Temporal Sentinel-2 Imagery and Ensemble Learning

Remote sensing of dryland ecosystem vegetation is notably problematic due to the low canopy cover and fugacious growing seasons. Relatively high temporal, spatial, and spectral resolution of Sentinel-2 imagery can address these difficulties. In this study, we combined vegetation indices with robust field data and used a Random Forests ensemble learning model to impute landcover over the study area. The resulting vegetation map product will be used by land managers, and the mapping approaches will serve as a basis for future remote sensing projects using Sentinel-2 imagery and machine learning

Applying Cloud-Based Computing and Emerging Remote Sensing Technologies to Inform Land Management Decisions

Who: Boise State University and Mountain Home Air Force Base What: Creating a species level classification map through the use of Google Earth Engine (GEE), a cloud-based computing platform, to map invasive species When: In-situ data collected in Summer 2018, a continuation of data collected in Summer 2016. Classification was created in Fall 2018. Unmanned aerial vehicles (UAV) flights in August 2018. Where: Mountain Home Air Force Base (MHAFB) in southwest Idaho, ecosystem is in the Great Basin Range (GBR) Why: The introduction of exotic species like cheatgrass (Bromus tectorum) has drastically altered the fire cycle of the Northern Great Basin (NGB) from 50 – 100 year burn intervals to 10 year intervals (1). Factors such as soil, elevation, temperature, and precipitation can affect the resilience of a sagebrush steppe ecosystem to invasive species. Remote sensing techniques allow large scale analysis of invasive encroachment and assessment of conservation efforts and land management

Applied Soft Classes and Fuzzy Confusion in a Patchwork Semi-Arid Ecosystem: Stitching Together Classification Techniques to Preserve Ecologically-Meaningful Information

Dryland ecosystems have complex vegetation communities, including subtle transitions between communities and heterogeneous coverage of key functional groups. This complexity challenges the capacity of remote sensing to represent land cover in a meaningful way. Many remote sensing methods to map vegetation in drylands simplify fractional cover into a small number of functional groups that may overlook key ecological communities. Here, we investigate a remote sensing process that further advances our understanding of the link between remote sensing and ecologic community types in drylands. We propose a method using k-means clustering to establish soft classes of vegetation cover communities from detailed field observations. A time-series of Sentinel-2 satellite imagery and a random forest classification leverages the mixing of different phenologies over time to impute such soft community classes over the landscape. Next, we discuss the advantages of using a fuzzy confusion approach for soft classes in cases such as understanding subtle transitions in ecotones, identifying areas for targeted remediation or treatment, and in ascertaining the spatial distribution of non-dominant covers such as biological soil crusts and small native bunchgrasses which have typically been difficult to map with traditional remote sensing classifications. Our pixel-level analysis is relevant to the scale of management decisions and represents the complexity of the landscape. The combination of cloud computing with the spatial, temporal, and spectral observations from Sentinel-2 allow us to develop these ecologically-meaningful observations at large spatial extents, including complete coverage at landscape scales. Re-interpretation of large extent maps of soft classes may be helpful to land managers who need community-level information for fuel breaks, restoration, invasive plant suppression, or habitat identification

Using SamplePoint to Determine Vegetation Percent Cover in a Sagebrush Steppe Ecosystem

Multi-temporal satellite imagery can be used to map species level vegetation across large areas. This is due to the fact that plants have unique spectral signatures in the electromagnetic spectrum and satellite imagery collects data from specific areas of the electromagnetic spectrum in different wavelengths (or bands) and over different time periods. However, in order to use satellite imagery to map vegetation using spectral signatures, vegetation information from the ground is needed to “train and validate” the satellite imagery. One of the ways of collecting vegetation information is using signature plots. Signature plots are high resolution local images collected with a digital camera of ground vegetation in a specific environment. These signature plots can then be analyzed using a computer software called SamplePoint in order to produce a percent vegetation cover for different vegetation species for the area which the camera covers. The percent vegetation cover information can then be used to train and validate the satellite imagery. SamplePoint offers a unique way to expand small physical observations to large landscapes

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

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

The Potential of Citizen Science Data to Complement Satellite and Airborne Lidar Tree Height Measurements: Lessons from The GLOBE Program

The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international science, citizen science, and education program through which volunteers in participating countries collect environmental data in support of Earth system science. Using the program\u27s software application, GLOBE Observer (GO), volunteers measure tree height and optional tree circumference, which may support the interpretation of NASA and other space-based satellite data such as tree height data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation instrument. This paper describes tree heights data collected through the GO application and identifies sources of error in data collection. We also illustrate how the ground-based citizen science data collected in the GO application can be used in conjunction with ICESat-2 tree height observations from two locations in the United States: Grand Mesa, Colorado, and Greenbelt, Maryland. Initial analyses indicate that data location accuracy and the scientific relevance of data density should be considered in order to align GLOBE tree height data with satellite-based data collections. These recommendations are intended to inform the improved implementation of citizen science environmental data collection in scientific work and to document a use case of the GLOBE Trees data for the science research community

Canopy structure: the link between optical and lidar remote sensing through canopy spectral invariants

Canopy structure and chemistry are the dominant factors that determine the radiation budget of vegetation. One approach to understand the role of canopy structure and disentangle it from canopy chemistry is the canopy spectral invariants theory, or p-theory. Using p-theory, the bidirectional reflectance factor (BRF) recorded by sensors can be simulated using a few spectrally-invariant variables and leaf single scattering albedo. The p-theory is originally developed for the optical domain and there are several hallenges associated with it, such as the assumption of black soil, its requirements for narrowband spectral information (e.g. hyperspectral), and limitations in very dense forests. The main question of this study is can we extend the oncepts of p-theory to lidar to overcome these limitations? To answer this question, we developed the theoretical framework in which variables associated with p-theory in the optical domain can be estimated using lidar point clouds and full-waveform information. To verify this framework, we conduct a series of experiments using the DART Monte Carlo ray-tracing model and vegetation scenes with known canopy chemistry and structure such as those offered in the Radiation Transfer Model Intercomparison (RAMI) project. Our reliminary results show that there is a strong link between information provided by optical and lidar sensors through p-theory. We show that information derived from lidar and some fixed, universal canopy chemistry (i.e. dry matter, water, and chlorophyll content) are sufficient to simulate the optical signature of a canopy with high accuracy. The results of this study advance our theoretical understanding of light interaction with canopy elements and also have significant implications for lidar-optical data fusion.Published versio

Lidar and Deep Learning Reveal Forest Structural Controls on Snowpack

Forest structure has a strong relationship with abiotic components of the environment. For example, canopy morphology controls snow depth through interception and modifies incoming thermal radiation. In turn, snow water availability affects forest growth, carbon sequestration, and nutrient cycling. We investigated how structural diversity and topography affect snow depth patterns across scales. The study site, Grand Mesa, Colorado, is representative of many areas worldwide where declining snowpack and its consequences for forest ecosystems are increasingly an environmental concern. On the basis of a convolution neural network model (R2 of 0.64; root mean squared error of 0.13 m), we found that forest structural and topographic metrics from airborne light detection and ranging (lidar) at fine scales significantly influence snow depth during the accumulation season. Moreover, complex vertically arranged foliage intercepts more snow and results in shallower snow depths below the canopy. Assessing forest structural controls on snow distribution and depth will aid efforts to improve understanding of the ecological and hydrological impacts of changing snow patterns

Estimates of Fine Fuel Litter Biomass in the Northern Great Basin Reveal Increases During Short Fire-Free Intervals Associated with Invasive Annual Grasses

Exotic annual grasses invasion across northern Great Basin rangelands has promoted a grass-fire cycle that threatens the sagebrush (Artemisia spp.) steppe ecosystem. In this sense, high accumulation rates and persistence of litter from annual species largely increase the amount and continuity of fine fuels. Here, we highlight the potential use and transferability of remote sensing-derived products to estimate litter biomass on sagebrush rangelands in southeastern Oregon, and link fire regime attributes (fire-free period) with litter biomass spatial patterns at the landscape scale. Every June, from 2018 to 2021, we measured litter biomass in 24 field plots (60 m × 60 m). Two remote sensing-derived datasets were used to predict litter biomass measured in the field plots. The first dataset used was the 30-m annual net primary production (NPP) product partitioned into plant functional traits (annual grass, perennial grass, shrub, and tree) from the Rangeland Analysis Platform (RAP). The second dataset included topographic variables (heat load index -HLI- and site exposure index -SEI-) computed from the USGS 30-m National Elevation Dataset. Through a frequentist model averaging approach (FMA), we determined that the NPP of annual and perennial grasses, as well as HLI and SEI, were important predictors of field-measured litter biomass in 2018, with the model featuring a high overall fit (R2 = 0.61). Model transferability based on extrapolating the FMA predictive relationships from 2018 to the following years provided similar overall fits (R2 ≈ 0.5). The fire-free period had a significant effect on the litter biomass accumulation on rangelands within the study site, with greater litter biomass in areas where the fire-free period was \u3c 10 years. Our findings suggest that the proposed remote sensing-derived products could be a key instrument to equip rangeland managers with additional information towards fuel management, fire management, and restoration efforts