Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R² = 0.340, root-mean-square error (RMSE) = 81.89 g·m-2, and relative error of 14.1%). The improvement of multiple regressions to the R² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns.

Remote mapping of foodscapes using sUAS and a low cost BG-NIR sensor.

The assessment of landscape condition for large herbivores, also known as foodscapes, is fast gaining interest in conservation and landscape management programs worldwide. Although traditional approaches are now being replaced by satellite imagery, several technical issues still need to be addressed before full standardization of remote sensing methods for these purposes. We present a low-cost method, based on the use of a modified blue/green/near-infrared (BG-NIR) camera housed on a small-Unmanned Aircraft System (sUAS), to create foodscapes for a generalist Mediterranean ungulate: the Iberian Ibex (Capra pyrenaica) in Northeast Spain. Faecal cuticle micro-histological analyses were used to assess the dietary preferences of ibexes and then individuals of the most common plant species (n = 19) were georeferenced to use as test samples. Because of the seasonal pattern in vegetation activity, based on the NDVI (Smooth term Month = 21.5, p-value < .01, R2 = 43%, from a GAM), images were recorded in winter and spring to represent contrasting vegetation phenology using two flight heights above ground level (30 and 60 m). Additionally, the range of image pixel sizes was 3.5-30 cm with the smallest pixel size representing the highest resolution. Boosted Trees were used to classify plant taxa based on spectral reflectance and create a foodscape of the study area. The number of target species, the sampling season, the height of flight and the image resolution were analysed to determine the accuracy of mapping the foodscape. The highest classification error (70.66%) was present when classifying all plant species using a 30 cm pixel size from acquisitions at 30 m height. The lowest error (18.7%), however, was present when predicting plants preferred by ibexes, at 3.5 cm pixel size acquired at 60 m height. This methodology can help to successfully monitor food availability and seasonality and to identify individual species.

Understanding 'saturation' of radar signals over forests.

There is an urgent need to quantify anthropogenic influence on forest carbon stocks. Using satellite-based radar imagery for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest aboveground volume (AGV) above a certain 'saturation' point. The causes of saturation are debated and often inadequately addressed, posing a major limitation to mapping AGV with the latest radar satellites. Using ground- and lidar-measurements across La Rioja province (Spain) and Denmark, we investigate how various properties of forest structure (average stem height, size and number density; proportion of canopy and understory cover) simultaneously influence radar backscatter. It is found that increases in backscatter due to changes in some properties (e.g. increasing stem sizes) are often compensated by equal magnitude decreases caused by other properties (e.g. decreasing stem numbers and increasing heights), contributing to the apparent saturation of the AGV-backscatter trend. Thus, knowledge of the impact of management practices and disturbances on forest structure may allow the use of radar imagery for forest biomass estimates beyond commonly reported saturation points.

A macroecological analysis of SERA derived forest heights and implications for forest volume remote sensing.

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²-106 plants/hectare and heights 6-49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100.