Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China

Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient (r) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.

Distribution of galling insects and their parasitoids on Caryocar brasiliense tree crowns

Caryocar brasiliense Camb. (Malpighiales: Caryocaraceae) is widely distributed in the Brazilian savanna and its fruits are used by humans for food, production of cosmetics, lubricants, and in the pharmaceutical industry. This plant is damaged by galling insects. Number of these galling insects and their parasitoids was recorded, in the field (galls) and in the laboratory (adults emerged from the galls), from three C. brasiliense crown heights, during three years. Numbers of adults of Eurytoma sp. (Hymenoptera: Eurytomidae), galling insect (younger attack) and Sycophila sp. (Hymenoptera: Eurytomidae) (a parasitoid of Eurytoma sp.), were greater on the apical parts of C. brasiliense tree crowns. Numbers and groups of Eurytoma sp. globoid galls (older attack) were higher in the median and basal crown parts. The numbers of Eurytoma sp. galls were higher on apical part of C. brasiliense tree crown and also of their parasitoids.

A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis

The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards.

Mapping Forest Burn Extent from Hyperspatial Imagery Using Machine Learning

Support vector machines are shown to be highly effective in mapping burn extent from hyperspatial imagery in grasslands. Unfortunately, this pixel-based method is hampered in forested environments that have experienced low-intensity fires because unburned tree crowns obstruct the view of the surface vegetation. This obstruction causes surface fires to be misclassified as unburned. To account for misclassifying areas under tree crowns, trees surrounded by surface burn can be assumed to have been burned underneath. This effort used a mask region-based convolutional neural network (MR-CNN) and support vector machine (SVM) to determine trees and burned pixels in a post-fire forest. The output classifications of the MR-CNN and SVM were used to identify tree crowns in the image surrounded by burned surface vegetation pixels. These classifications were also used to label the pixels under the tree as being within the fire’s extent. This approach results in higher burn extent mapping accuracy by eliminating burn extent false negatives from surface burns obscured by unburned tree crowns, achieving a nine percentage point increase in burn extent mapping accuracy.

Long-term effects of plant spacing on the growth and morphometry of Bertholletia excelsa

ABSTRACT Plant spacing is a potential driver of tree form and yield in forest plantations. However, its effects on the productivity of tree plantations in the Amazon are still little known. This study examined the effects of six spacing regimes (3 x 4, 4 x 4, 4 x 5, 5 x 5, 5 x 6, and 6 x 6 m) on the growth and morphometry of a 20-year-old plantation of Bertholletia excelsa. We observed high, spacing-independent survival (> 70%). For timber production purposes, intermediate and two large spacing regimes tended to higher values of yield components, mainly diameter, biomass, and volume, although some did not differ significantly from the smallest spacing. One of the intermediate spacings (5 x 5 m) tended to higher commercial height. Tree crowns tended to be wider and longer in the larger spacings, which indicates the potential of these regimes for fruit production. Tree crowns exceeded the vital growth space in all spacing regimes, which suggests the need for thinning before the age of 20 years in all spacings to reduce intraspecific competition and increase yield. We estimated that a density of 84 remaining trees per hectare would be necessary to reach an average diameter of 40 cm at the age of 20 years. Thus, B. excelsa had high survival in the tested range of spacing regimes, while the intermediate and the largest spacing regimes led to better tree growth and morphometry.

Impact of Modeling Abstractions When Estimating Leaf Mass per Area and Equivalent Water Thickness Over Sparse Forests Using a Hybrid Method

Equivalent water thickness (EWT) and leaf mass per area (LMA) are important indicators of plant processes, such as photosynthetic and potential growth rates and health status, and are also important variables for fire risk assessment. Retrieving these traits through remote sensing is challenging and often requires calibration with in situ measurements to provide acceptable results. However, calibration data cannot be expected to be available at the operational level when estimating EWT and LMA over large regions. In this study, we assessed the ability of a hybrid retrieval method, consisting of training a random forest regressor (RFR) over the outputs of the discrete anisotropic radiative transfer (DART) model, to yield accurate EWT and LMA estimates depending on the scene modeling within DART and the spectral interval considered. We show that canopy abstractions mostly affect crown reflectance over the 0.75–1.3 μm range. It was observed that excluding these wavelengths when training the RFR resulted in the abstraction level having no effect on the subsequent LMA estimates (RMSE of 0.0019 g/cm2 for both the detailed and abstract models), and EWT estimates were not affected by the level of abstraction. Over AVIRIS-Next Generation images, we showed that the hybrid method trained with a simplified scene obtained accuracies (RMSE of 0.0029 and 0.0028 g/cm2 for LMA and EWT) consistent with what had been obtained from the test dataset of the calibration phase (RMSE of 0.0031 and 0.0032 g/cm2 for LMA and EWT), and the result yielded spatially coherent maps. The results demonstrate that, provided an appropriate spectral domain is used, the uncertainties inherent to the abstract modeling of tree crowns within an RTM do not significantly affect EWT and LMA accuracy estimates when tree crowns can be identified in the images.

Automatic Detection and Parameter Estimation of Ginkgo biloba in Urban Environment Based on RGB Images

Individual tree crown detection and morphological parameter estimation can be used to quantify the social, ecological, and landscape value of urban trees, which play increasingly important roles in densely built cities. In this study, a novel architecture based on deep learning was developed to automatically detect tree crowns and estimate crown sizes and tree heights from a set of red-green-blue (RGB) images. The feasibility of the architecture was verified based on high-resolution unmanned aerial vehicle (UAV) images using a neural network called FPN-Faster R-CNN, which is a unified network combining a feature pyramid network (FPN) and a faster region-based convolutional neural network (Faster R-CNN). Among more than 400 tree crowns, including 213 crowns of Ginkgo biloba, in 7 complex test scenes, 174 ginkgo tree crowns were correctly identified, yielding a recall level of 0.82. The precision and F -score were 0.96 and 0.88, respectively. The mean absolute error (MAE) and mean absolute percentage error (MAPE) of crown width estimation were 0.37 m and 8.71%, respectively. The MAE and MAPE of tree height estimation were 0.68 m and 7.33%, respectively. The results showed that the architecture is practical and can be applied to many complex urban scenes to meet the needs of urban green space inventory management.

The Isotopic Signature of the “Arthropod Rain” in a Temperate Forest: Wingless Are From Crowns, Winged Are From Soil

Invertebrate phyto-, sapro-, and microbophages, as well as predators and parasitoids, densely populate tree crowns. Eventually, all crown inhabitants fall from the trees and become a food source for litter-dwelling predators, scavengers, and saprophages. However, the functional significance of the arthropod rain, i.e., the flux of invertebrates falling from the crowns, remains unexplored. We collected arthropod rain in a temperate mixed forest throughout the growing season. The δ13С and δ15N values of the arthropods (730 samples in total) were compared to a large reference dataset of the isotopic composition of soil animals from temperate forests. The most numerous taxa in the arthropod rain were collembolans and mites. The most diverse orders were Diptera (18 families) and Coleoptera (29 families), which formed the major portion of the winged specimens. The total ranges of δ13С and δ15N values of individual animals forming arthropod rain reached 14‰ and 26‰, respectively. Nevertheless, invertebrates forming arthropod rain were on average depleted in 13C and 15N by 1.6‰ and 2.7‰, respectively, compared to the soil-dwelling animals. This difference can be used to detect the contribution of the arthropod rain to detrital food webs. Low average δ13С and δ15N values of the arthropod rain were primarily driven by the presence of microphytophages, represented mainly by Collembola and Psocoptera, and macrophytophages, mainly aphids, caterpillars, and heteropterans. Furthermore, wingless arthropods were depleted in heavy isotopes relative to winged specimens. Among wingless invertebrates, predators and parasitoids differed significantly in δ15N values from phytophages and microbi/saprophages. In contrast, there was no consistent difference in δ values between saprophages and predators among winged insects, all of them being similar in the isotopic composition to soil-dwelling invertebrates. This result suggests that winged insects in the arthropod rain represented a random assemblage of specimens originating in different biotopes, but most were tightly linked to soil food webs. Overall, our data suggest that invertebrates falling from the crown space and flying arthropods originating from the soil are an important channel connecting food webs in tree crowns and in the soil.

TREE CROWN MAPPING BASED ON UNMANNED AERIAL VEHICLE (UAV) TOWARDS A GREEN-SUSTAINABLE RESIDENTIAL

Tree crown plays a crucial role in creating urban characters and spatial arrangements of living environment towards a green-sustainable city. It provides the fundamental needs for human’s living quality and health conditions such as improving water quality, preserving energy, minimising greenhouse gasses, and beautification and comfortable purposes. Therefore, there is a need for urban planners to recognise its importance and plan for it wisely. This paper attempts to demonstrate a mapping tree crowns for a case of the residential neighbourhood using Unmanned Aerial Vehicle (UAV) based GIS technologies. Four main stages involved in a mapping tree crown process namely: flight planning, data acquisition, data processing and analyses and results. As a result, this paper able to show the capabilities of the technologies in measuring and mapping tree crowns for the residential neighbourhood. Moreover, it provides urban planners with informative scenario of the tree planting and clarifies its importance for future planning and benefits – in creating and promoting a green-sustainable and healthy living environment.