Cover Estimates Research Articles

Winter Daytime Warming and Shift in Summer Monsoon Increase Plant Cover and Net CO2 Uptake in a Central Tibetan Alpine Steppe Ecosystem

AbstractOver the past decades, human‐induced climate change has led to a widespread wetting and warming of the Tibetan Plateau (TP), affecting both ecosystems and the carbon cycling therein. Whether the previously observed climate changes stimulate carbon uptake via enhanced photosynthesis or carbon loss via enhanced soil respiration remains unclear. Here we present 14 years of observations of carbon fluxes, meteorological variables and remotely sensed plant cover estimations from a central Tibetan alpine steppe ecosystem at Nam Co, the third largest lake on the TP. Using modified Mann‐Kendall trend tests, we found a significant increasing daily net carbon uptake of 0.5 g C m−2 decade−1, which can be explained by a widespread greening at the southern shore of lake Nam Co. The Plateau‐wide changes in temperature and precipitation are locally expressed as an increasing diurnal temperature range during winter, higher water availability during spring, higher cloud cover during early summer and less water availability during late summer. While these changes differ over the course of the year, they tend to stimulate plant growth more than microbial respiration, leading to an increased carbon uptake during all seasons. This study indicates that during the 14 years study period, a higher amplitude in winter temperatures and an earlier summer monsoon promote carbon uptake in a central Tibetan alpine steppe ecosystem.

Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.

Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State

Standardized methods for effectively and rapidly monitoring changes in the biodiversity of marine ecosystems are critical to assess status and trends in ways that are comparable between locations and over time. In intertidal and subtidal habitats, estimates of fractional cover and abundance of organisms are typically obtained with traditional quadrat-based methods, and collection of photoquadrat imagery is a standard practice. However, visual analysis of quadrats, either in the field or from photographs, can be very time-consuming. Cutting-edge machine learning tools are now being used to annotate species records from photoquadrat imagery automatically, significantly reducing processing time of image collections. However, it is not always clear whether information is lost, and if so to what degree, using automated approaches. In this study, we compared results from visual quadrats versus automated photoquadrat assessments of macroalgae and sessile organisms on rocky shores across the American continent, from Patagonia (Argentina), Galapagos Islands (Ecuador), Gorgona Island (Colombian Pacific), and the northeast coast of the United States (Gulf of Maine) using the automated software CoralNet. Photoquadrat imagery was collected at the same time as visual surveys following a protocol implemented across the Americas by the Marine Biodiversity Observation Network (MBON) Pole to Pole of the Americas program. Our results show that photoquadrat machine learning annotations can estimate percent cover levels of intertidal benthic cover categories and functional groups (algae, bare substrate, and invertebrate cover) nearly identical to those from visual quadrat analysis. We found no statistical differences of cover estimations of dominant groups in photoquadrat images annotated by humans and those processed in CoralNet (binomial generalized linear mixed model or GLMM). Differences between these analyses were not significant, resulting in a Bray-Curtis average distance of 0.13 (sd 0.11) for the full label set, and 0.12 (sd 0.14) for functional groups. This is the first time that CoralNet automated annotation software has been used to monitor “Invertebrate Abundance and Distribution” and “Macroalgal Canopy Cover and Composition” Essential Ocean Variables (EOVs) in intertidal habitats. We recommend its use for rapid, continuous surveys over expanded geographical scales and monitoring of intertidal areas globally.

A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone

As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built along the riverbank and within the floodplains. Sufficient data allow for flood modeling used to implement proper warning signals and evacuation plans, however, in least developed countries (LDC), the lack of field data for precipitation and river flows makes hydrologic and hydraulic modeling difficult. Within the most recent data revolution, the availability of remotely sensed data for land use/land cover (LULC), flood mapping, and precipitation estimates has increased, however, flood mapping in urban areas of LDC is still limited due to low resolution of remotely sensed data (LULC, soil properties, and terrain), cloud cover, and the lack of field data for model calibration. This study utilizes remotely sensed precipitation, LULC, soil properties, and digital elevation model data to estimate peak discharge and map simulated flood extents of urban rivers in ungauged watersheds for current and future LULC scenarios. A normalized difference vegetation index (NDVI) analysis was proposed to predict a future LULC. Additionally, return period precipitation events were calculated using the theoretical extreme value distribution approach with two remotely sensed precipitation datasets. Three calculation methods for peak discharge (curve number and lag method, curve number and graphical TR-55 method, and the rational equation) were performed and compared to a separate Soil and Water Assessment Tool (SWAT) analysis to determine the method that best represents urban rivers. HEC-RAS was then used to map the simulated flood extents from the peak discharges and ArcGIS helped to determine infrastructure and population affected by the floods. Finally, the simulated flood extents from HEC-RAS were compared to historic flood event points, images of flood events, and global surface water maximum water extent data. This analysis indicates that where field data are absent, remotely sensed monthly precipitation data from Integrated Multi-satellitE Retrievals for GPM (IMERG) where GPM is the Global Precipitation Mission can be used with the curve number and lag method to approximate peak discharges and input into HEC-RAS to represent the simulated flood extents experienced. This work contains a case study for seven urban rivers in Freetown, Sierra Leone.

Predicting canopy cover of diverse forest types from individual tree measurements

Quantifying tree canopy cover is fundamental to applications in forestry and ecology, but estimates vary substantially depending on type of field measurement, imagery, or active sensing used. Our objective was to improve estimates of stand-level canopy cover from standard tree inventory measurements, using representative data collected across diverse forest plant association groups across Oregon, USA. Canopy cover was measured with line intercept sampling on 1706 inventory plots and compared to calculations from individually tallied trees. We investigated adjustments of tree crown area equations, adjustments of crown overlap factors, and modeling from climatic variables and standard forest measurements to estimate line intercept cover. Estimates based on simple crown width equations adjusted for tree social position and caps on maximum cover, had the lowest error (RMSE = 14% cover) of crown width approaches across all vegetation types. Random crown overlap applied to unadjusted crown area only performed well in drier forest types and was unable to match high line intercept cover levels (>90%) often found in productive forest types. Although statistical models had somewhat greater precision than the simpler crown-width summation approaches (RMSE of 12%), accuracy was comparable. The greater flexibility of crown width summation approaches could make them more useful in forestry applications and beyond our study area.

Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features

BackgroundFractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products.MethodsThis study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated.Results(1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm).ConclusionThis study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland.

Informed cover measurement: Guidelines and error for point-intercept approaches.

PremiseThe point‐intercept method is one of the most commonly used approaches to measure species cover in ecosystems worldwide. In this approach, multiple points are sampled for presence/absence of a species, and the number of present points divided by the total number of sampled points provides an estimate of percent cover. Our purpose is to mathematically analyze the accuracy of the point‐intercept approach and establish guidelines for its use.MethodsWe developed formulas that analyze the point‐intercept method and confirmed their effectiveness using simulations.ResultsWe find that a point‐intercept spacing of at least 80% of the largest plant diameter provides the most reliable results. We present a user‐friendly spreadsheet that calculates the number of intercepts needed for fieldwork, as well as the standard deviation, expected deviation, and confidence interval of the collected data.DiscussionWe provide a variety of guidelines for establishing field protocols based on our results, including dealing with rare species and combining results for multiple species. Quadrat characteristics (intercept spacing, number of point intercepts) can now be easily calculated to guide research design prior to fieldwork; after fieldwork is complete, the accuracy of this technique can (and should) be reported in all future ecological studies in which it is used.

Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia, Argentina

The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions. Globally, arid and semi-arid regions are considered an important carbon sink. However, they have received limited attention and, therefore, it should be a priority to develop tools to quantify biomass at the local and regional scales. Individual plant variables, such as stem diameter and crown area, were reported to be good predictors of individual plant weight. Stand-level variables, such as plant cover and mean height, are also easy-to-measure estimators of above-ground biomass (AGB) in dry regions. In this study, we estimated the AGB in semi-arid woody vegetation in Northeast Patagonia, Argentina. We evaluated whether the AGB at the stand level can be estimated based on plant cover and to what extent the estimation accuracy can be improved by the inclusion of other field-measured structure variables. We also evaluated whether remote sensing technologies can be used to reliably estimate and map the regional mean biomass. For this purpose, we analyzed the relationships between field-measured woody vegetation structure variables and AGB as well as LANDSAT TM-derived variables. We obtained a model-based ratio estimate of regional mean AGB and its standard error. Total plant cover allowed us to obtain a reliable estimation of local AGB, and no better fit was attained by the inclusion of other structure variables. The stand-level plant cover ranged between 18.7% and 95.2% and AGB between about 2.0 and 70.8 Mg/hm2. AGB based on total plant cover was well estimated from LANDSAT TM bands 2 and 3, which facilitated a model-based ratio estimate of the regional mean AGB (approximately 12.0 Mg/hm2) and its sampling error (about 30.0%). The mean AGB of woody vegetation can greatly contribute to carbon storage in semi-arid lands. Thus, plant cover estimation by remote sensing images could be used to obtain regional estimates and map biomass, as well as to assess and monitor the impact of land-use change on the carbon balance, for arid and semi-arid regions.

Analysis of Forest Stand Resistance to Insect Attack According to Remote Sensing Data

Methods for analyzing the resistance of large woodlands (such as Siberian taiga forests) to insect attacks based on remote sensing data are proposed. As an indicator of woodland’s resistance, we suggest a function of normalized difference vegetative index (NDVI) susceptibility to changes in the land surface temperature (LST). Both NDVI and LST are obtained via the TERRA/AQUA satellite system. This indicator function was calculated as the spectral transfer function of the response in the integral equation connecting the changes in NDVI and LST. The analysis was carried out for two test sites, both of which are fir stands of the Krasnoyarsk region taiga zone. In the first case, the fir stands have suffered damage inflicted by Siberian silk moth caterpillars, Dendrolimus sibiricus Tschetv. since 2015. Adjacent intact fir forest areas were also analyzed. In the second case, the object of the study was a fir tree site damaged by Black Fir Sawyer Monochamus urussovii Fischer in 2013. It is demonstrated that the above-mentioned indicator function changed significantly 2–3 years prior to the pest population outbreaks, and therefore this indicator function can be used to assess the risk of pest population outbreak. Thereby, the proposed indicator compares favorably with vegetation cover estimates using NDVI, which register significant defoliation of tree stands and cannot be used for forecasting.

Refining medium resolution fractional cover for arid Australia to detect vegetation dynamics and wind erosion susceptibility on longitudinal dunes

Medium resolution satellite-derived fractional cover estimates of bare soil (fBS), photosynthetic vegetation (fPV), and non-photosynthetic vegetation (fNPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of fPV and fNPV from five case study sites from Australia's vegetated dunefields to observe (a) vegetation growth response to rainfall ‘pulses’ and subsequent transition to non-photosynthetic dormancy or senescence; (b) multiple time scales of antecedent climatic influence on vegetation cover; (c) the susceptibility of dunes to wind-blown sand drift during periods of low cover; and (d) the implications of image resolution choice when ground cover is heterogeneous.A spectral unmixing model for Australia's arid zone (termed ‘AZN’) was first developed by generating endmembers from a dataset of 1405 field surveys; Landsat time series estimates of fPV and fNPV were subject to a Seasonal-Trend decomposition by Loess (STL); Time series components were correlated with rainfall (P) and aridity at various accumulation periods; Fire maps were used to compare the climatic response of unburnt and burnt vegetation; Landform maps were used to isolate dune vegetation cover from the adjacent interdunes; and Landsat estimates of erodible area were compared with Sentinel-2 and WorlView-3 data.The new AZN model yielded Root Mean Square Error (RMSE) estimates of 14.5% (fBS), 6.5% (fPV) and 15.8% (fNPV) during cross validation. The AZN model also compared favourably to an existing continental-scale model when evaluated with independent reference data. Rainfall pulse responses of dune vegetation were detected initially as fPV, and 3–9 months later as a peak in fNPV. Components of fPV responded to P accumulated over 3–9-months (intra-annual), and 12–15-months (trend). The long-term build-up of fNPV, if left unburnt, was influenced by rainfall patterns over the preceding 45–114 months. Fires reduced both the depth and strength of antecedent rainfall's influence on vegetation, and vegetation was often more sensitive to P than to aridity. Erodibility (total cover <14%) and partial erodibility (cover <35%) were more common at the driest sites but did not universally match aridity levels, due to fires and differing vegetation. The targeting of dune crest regions highlighted their enhanced susceptibility to sand drift (in most cases), and, given their occurrence on relatively narrow ridges (~30 m), the importance of estimating cover at Landsat resolutions or better (e.g. Sentinel-2).

Missing the Reef for the Corals: Unexpected Trends Between Coral Reef Condition and the Environment at the Ecosystem Scale

It is incontrovertible that many coral reefs are in various stages of decline and may be unable to withstand the effects of global climate change, jeopardizing vital ecosystem goods and services to hundreds of millions of people around the world. An estimated 50% of the world's corals have already been lost, and those remaining may be lost by 2030 under the “business as usual” CO2 emissions scenario. However, the foundation of these predictions is a surprisingly sparse dataset, wherein ~0.01–0.1% of the world's reef area has been quantitatively surveyed. Further, the available data comprise observations at the 1–10 m scale, which are not evenly spaced across reefs, but often clustered in areas representing focused survey effort. This impedes modeling and predicting the impact of a changing environment at the ecosystem scale. Here we highlight deficiencies in our current understanding of the relationship between coral reefs and their environments. Specifically, we conduct a meta-analysis using estimates of coral cover from a variety of local surveys, quantitatively relating reef condition to a suite of biogeophysical forcing parameters. We find that readily available public data for coral cover exhibit unexpected trends (e.g., a positive correlation between coral cover and multi-year cumulative thermal stress), contrary to prevailing scientific expectations. We illustrate a significant gap in our current understanding, and thereby prediction, of coral reefs at the ecosystem scale that can only be remedied with uniform, high-density data across vast coral reef regions, such as that from remote sensing.

Snow Cover Dynamics and its Spatio-Temporal Distribution in the Central Higher Himalaya of India

Retreating glaciers, changing timber line and decreasing accumulation of snow in the Himalaya are considered the indicators of climate change. In this study, an attempt is made to observe the snow cover change in the higher reaches of the Central Himalayas. Investigation of climate change through snow cover is very important to understand the impact and adaptation in an area. Landsat thematic and multi spectral optical data with a spatial resolution of 60m and 30m are considered for the estimation and extraction of snow cover. Total 3,369 Km2 snow cover area is lost since 1972 out of total geographical area i.e. 17,227 Km2. The accumulation of snow during winter is lower than the melting rate during summer. The current study identified the decrease of 19.6 % snow cover in 47 years since 1972 to 2019. Composite satellite imageries of September to December show that the major part of the study area covered with snow lies above 3600m. Overall observation indicates that in 47 years, permanent snow cover is decreasing in Central Himalayas.

Estimating canopy cover in artificial forests using high spatial resolution GF-1 and ZY-3 images: across-sensor and across-site comparison

ABSTRACT Canopy cover is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. However, estimation of forest canopy cover from high spatial resolution images is an arduous task. To evaluate the utility of high spatial resolution images for estimation of canopy cover of artificial forests, the performance of canopy cover estimation models across sensors and across sites was evaluated. Choosing Wangyedian Forest Farm and Gaofeng Forest Farm as experimental areas, based on Chinese high spatial resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images, three models, namely multiple linear regression (MLR), generalized additive model (GAM), and Random Forest (RF) were established. The capabilities of the two data sources and three model types to estimate canopy cover of the forests were compared and the portability of the three models across sensors and sites was analysed. The ranges of the root mean square error (RMSE) and relative root mean square error (rRMSE) of the MLR, GAM, and RF models for each study area established from GF-1 and ZY-3 were 0.0632–0.1205 and 9.98–19.93%, respectively. GF-1 showed better performance than ZY-3 when using the same model type. Whether the objective was across sensors or across sites transplantation, the accuracy of the model decreased with transplantation of the model. The MLR model was non-significant when transplanted across sites and model migration failed, whereas across sensors and across sites transplantation of RF and GAM models was successful. The three evaluation indices of transplanted GAM models were significantly superior to those of transplanted RF and MLR models. Thus, the GAM model showed strong portability and the highest model stability.

Regional estimates of a range‐extending ecosystem engineer using stereo‐imagery from ROV transects collected with an efficient, spatially balanced design

AbstractThe redistribution of marine ecosystem engineers in response to changing climate is restructuring endemic benthic communities globally. Therefore, developing and implementing efficient monitoring programs across the complete depth range of these marine ecosystem engineers is often an urgent management priority. Traditionally, many monitoring programs have been based on a systematically selected set of survey locations that, while able to track trends at those sites through time, lack inference for the overall region being monitored. This study trialled a probabilistic sampling design to address this need, taking advantage of an important prerequisite for such designs, extensive multibeam echosounder (MBES) mapping, to inform a spatially balanced sample selection. Here, we allocated 170 remotely operated vehicles (ROVs) transects based on a spatially balanced probabilistic sampling design across three locations with extensive mapping. Generalized additive models were used to estimate the density and associated barren cover of the range‐expanding ecosystem engineer, the long spined urchin (Centrostephanus rodgersii). Estimates were generated at a reef‐wide scale across three locations on the east coast of Tasmania, Australia, representing the leading edge of the species recent range extension. Model‐based estimates of urchin density and barren cover incorporated seabed structure attributes, such as depth and ruggedness, with differences in these modelled relationships being identified between locations. Estimates ranged from 0.000065 individuals m−2 and 0.018% barren cover in the Tasman Peninsula to 0.167 individuals m−2 and 2.10% barren cover at Governor Island Marine Reserve, reflecting a north to south distributional gradient. This study highlights the value of combining probabilistic sampling designs, ROV transects, stereo video, and MBES mapping to generate reliable and robust estimates of important ecosystem species needed to protect reef‐based fishery and conservation values via adaptive and informed management.

Estimation of dry vegetation cover and mass from MODIS data: Verification by roughness length and sand saltation threshold

Monitoring of dry vegetation in arid and semi-arid regions is crucial in environmental investigations such as assessments of wind erosion risk, livestock forage, and fire risk. However, there has been little research into estimating the coverage and mass of dry (non-photosynthetic) vegetation. In our study area at Tsogt-Ovoo (TsO) in the northern Gobi Desert, Mongolia, we used MODIS data to compare observed dry vegetation cover (VCd_obs) and mass (Md_obs) with values estimated from the Normalized Difference Vegetation Index (NDVI) and six other potential Short Wavelength InfraRed (SWIR) dry vegetation indices. Of the seven vegetation indices, the Soil Tillage Index (STI) and Normalized Difference Tillage Index (NDTI), both of which are calculated from SWIR bands 6 and 7, provided good estimates of VCd_obs and Md_obs. Our comparisons of observed roughness length (z0) and threshold friction velocity (u*t) for sand saltation at stony and non-stony sites with dry vegetation cover determined from STI verified its suitability for estimating VCd(STI). At both sites, the observed z0and u*tincreased with increasing estimated VCd(STI). Based on their relationships with STI, we predicted values of z0and u*tin the absence of dry vegetation that were in accordance with published data. The threshold wind speeds at 10 m height derived from u*tat both the stony and non-stony sites were reasonably close to the value that is commonly used in dust models. Thus, we verified that STI is a suitable index for estimating the amount of dry vegetation in the Gobi Desert and, potentially, in other arid and semi-arid regions. Although the relationships of z0and u*twith STI require further validation, it appears that the use of STI can improve future wind erosion risk assessments in arid and semi-arid environments.

Post‐fire conifer regeneration hinders digital estimation of understorey plant cover in subalpine forest vegetation

AbstractQuestion: Reliable estimates of understorey (non‐tree) plant cover following fire are essential to assess early forest community recovery. Photographic digital image analysis (DIA) is frequently used in seral, single‐strata vegetation, given its greater objectivity and repeatability compared to observer visual estimation; however, its efficacy in multi‐strata forest vegetation may be compromised, where various visual obstructions (coarse downed wood [CDW], conifer regeneration, and shadows) may conceal plant cover in the digital imagery. We asked whether vegetation complexity influences plant cover estimated by DIA relative to two visual methods: plot‐level (20 m2) estimation (PLE) and quadrat‐level (1 m2) estimation (QLE)?Location: Greater Yellowstone Ecosystem, USA.Methods: We estimated understorey plant cover in subalpine forest vegetation on permanent plots (n = 141) at two study areas ~30 years after the 1988 Yellowstone fires to: (a) assess differences in visual obstructions between study areas in our digital imagery; (b) compare digital to visual estimates of plant cover; and (c) determine relationships between estimated plant cover and visual obstructions measured in situ.Results: Percent conifer regeneration pixels differed significantly (odds ratio = 8.34) between study areas which represented the greatest difference in visual obstructions. At the study area with lower conifer pixels, DIA estimated 9% (95% confidence interval [CI] = 3%–14%) and 16% (95% CI = 10%–21%) more understorey plant cover than PLE or QLE, respectively, but had comparable variability. At the study area with higher conifer pixels, DIA estimated 28% (95% CI = 24%–32%) and 22% (95% CI = 18%–26%) less understorey plant cover than PLE or QLE, respectively, and had more variability. Furthermore, plot‐level subcanopy regeneration (height&gt;137 cm) density was negatively associated with digitally derived plant cover but showed no relationship with visually derived plant cover.Conclusions: Post‐fire conifer regeneration hindered the efficacy of DIA in estimating understorey plant cover. Digital estimation is advantageous in single‐strata vegetation but should not be used in complex, multi‐strata vegetation.

Differing Fine-Scale Responses of Vegetation and Bare Soil to Moisture Variation in a Pinyon-Juniper Woodland Underlie Landscape-Scale Responses Observed from Remote Sensing

Pinyon juniper woodlands in the American southwest face an uncertain ecological future with regard to climate altered precipitation. Although satellite remote sensing will be relied upon to assess the overall health of these plant communities more fine scaled information is needed to elucidate the mechanisms shaping the broader scaled regional assessments. We conducted a study to assess the NDVI response at the plant canopy level (insitu sensors placed over the canopies) of three tree and one shrub species to changes in precipitation, reference evapotranspiration and soil volumetric water content. Landsat data was used to compare stand integrated and satellite NDVI values. We also provided supplemental water in the amount of 10.85 cm over the study period to additional trees and shrubs which also had insitu NDVI sensors placed over their canopies. NDVI at the canopy level separated statistically by species and when contrasted with bare soil (p&amp;lt;0.001). Spring early summer dry down events were inversely related to increasing ETref-precipitation with a steeper dry down slope in the first year associated with no rainfall occurring in May and June. All three-tree species did not show any significant difference in canopy NDVI based on supplemental water, however the shrub species did reveal a significant response to water (p&amp;lt;0.001). Although all of the three-tree species revealed a one-month period in which they responded to precipitation in July of the first year after 11.2 cm of precipitation, no immediate (day of or next day) response was observed to precipitation or supplemental water events. Snowberry was unique in its NDVI response during the spring green up period in the second year revealing a highly linear shift over a 40-day period with a clear separation between treatments (p&amp;lt;0.001) with those plants receiving supplemental water having a higher more positive slope. Landsat NDVI values revealed an inverse sinusoidal relationship with ETref-precipitation (R2=0.59 p=0.012). Landsat values (0.19+/- 0.01) were found to have no significant difference with bare soil NDVI (0.17+/- 0.01) but were significantly different from all four tree and shrub species. Integrated NDVI based on sensor weighted % cover estimates (0.37+/-0.03) were nearly double Landsat values (0.19+/-0.01). Both NDVI values of pinyon pine and Utah juniper were found to be linear correlated with Landsat NDVI in the second Year (R2&amp;gt;0.75, p&amp;lt;0.001). Multiple regression analysis revealed that 95% of the variation in Landsat NDVI in the second year could be accounted for based on bare soil NDVI and pinyon pine NDVI (p&amp;lt;0.001). et al., NDVI interspace (bare soil) of pinyon juniper woodlands dominated the nature of the Landsat curve. Our results demonstrate the value of ground sensors to help fill the gap between what can be inferred at the forest canopy level and what is occurring at the plant level.

Cover-based allometric estimate of aboveground biomass of a non-native, invasive annual grass (Bromus tectorum L.) in the Great Basin, USA

Cheatgrass (Bromus tectorum L.) presence in the Great Basin is associated with an increase in fire frequency and size, likely due to increased spatial continuity of fine fuel biomass. Measurements of the extent and cover of cheatgrass are steadily improving, but the strength of the relationship between cover and aboveground biomass (AGB) is unclear. An allometric equation that can reliably convert cover to AGB of cheatgrass would allow for improved incorporation of regional estimates of cover into models of fire activity, carbon storage, and net primary productivity, all of which rely on biomass. We measured cover and AGB of cheatgrass at 60 locations in the north-central Great Basin and used these measurements to model the relationship. We found a strong, linear relationship between the percent cover and AGB, which was improved after square root transformation of both cover and AGB, and after incorporating the number of days after peak NDVI that the biomass and cover were measured. These results show that AGB of cheatgrass can be reliably estimated from cover. It is likely that allometric equations based on cover will be effective for other grass species, but care must be taken to account for phenology (e.g., peak NDVI) in the estimation.

A FULLY AUTOMATED AND FAST APPROACH FOR CANOPY COVER ESTIMATION USING SUPER HIGH-RESOLUTION REMOTE SENSING IMAGERY

Abstract. Canopy cover is a key agronomic variable for understanding plant growth and crop development status. Estimation of canopy cover rapidly and accurately through a fully automated manner is significant with respect to high throughput plant phenotyping. In this work, we propose a simple, robust and fully automated approach, namely a rule-based method, that leverages the unique spectral pattern of green vegetation at visible (VIS) and near-infrared red (NIR) spectra regions to distinguish the green vegetation from background (i.e., soil, plant residue, non-photosynthetic vegetation leaves etc.), and then derive canopy cover. The proposed method was applied to high-resolution hyperspectral and multispectral imagery collected from gantry-based scanner and Unmanned Aerial Vehicle (UAV) platforms to estimate canopy cover. Additionally, machine learning methods, i.e., Support Vector Machine (SVM) and Random Forest (RF) were also employed as bench mark methods. The results show that: the rule-based method demonstrated promising classification accuracies that are comparable to SVM and RF for both hyperspectral and multispectral datasets. Although the rule-based method is more sensitive to mixed pixels and shaded canopy region, which potentially resulted in classification errors and underestimation of canopy cover in some cases; it showed better performance to detect smaller leaves than SVM and RF. Most importantly, the rule-based method substantially outperformed machine learning methods with respect to processing speed, indicating its greater potential for high-throughput plant phenotyping applications.

Implications of 2D versus 3D surveys to measure the abundance and composition of benthic coral reef communities

A paramount challenge in coral reef ecology is to estimate the abundance and composition of the communities residing in such complex ecosystems. Traditional 2D projected surface cover estimates neglect the 3D structure of reefs and reef organisms, overlook communities residing in cryptic reef habitats (e.g., overhangs, cavities), and thus may fail to represent biomass estimates needed to assess trophic ecology and reef function. Here, we surveyed the 3D surface cover, biovolume, and biomass (i.e., ash-free dry weight) of all major benthic taxa on 12 coral reef stations on the island of Curaçao (Southern Caribbean) using structure-from-motion photogrammetry, coral point counts, in situ measurements, and elemental analysis. We then compared our 3D benthic community estimates to corresponding estimates of traditional 2D projected surface cover to explore the differences in benthic community composition using different metrics. Overall, 2D cover was dominated (52 ± 2%, mean ± SE) by non-calcifying phototrophs (macroalgae, turf algae, benthic cyanobacterial mats), but their contribution to total reef biomass was minor (3.2 ± 0.6%). In contrast, coral cover (32 ± 2%) more closely resembled coral biomass (27 ± 6%). The relative contribution of erect organisms, such as gorgonians and massive sponges, to 2D cover was twofold and 11-fold lower, respectively, than their contribution to reef biomass. Cryptic surface area (3.3 ± 0.2 m2 m−2planar reef) comprised half of the total reef substrate, rendering two thirds of coralline algae and almost all encrusting sponges (99.8%) undetected in traditional assessments. Yet, encrusting sponges dominated reef biomass (35 ± 18%). Based on our quantification of exposed and cryptic reef communities using different metrics, we suggest adjustments to current monitoring approaches and highlight ramifications for evaluating the ecological contributions of different taxa to overall reef function. To this end, our metric conversions can complement other benthic assessments to generate non-invasive estimates of the biovolume, biomass, and elemental composition (i.e., standing stocks of organic carbon and nitrogen) of Caribbean coral reef communities.