Leaf-on Season Research Articles

The influence of urban trees and total vegetation on asthma development in children.

We aimed to assess whether the influence of urban vegetation on asthma development in children (<13 years) varies by type (e.g., total vegetation, tree type, and grass) and season. We used a cohort of all children born in Montreal, Canada, between 2000 and 2015. Children and cases were identified from linked medico-administrative databases. Exposure to residential vegetation was estimated using the Normalized Difference Vegetation Index (NDVI) for total vegetation and using the total area covered by deciduous and evergreen crowns for trees in 250 m buffers centered on residential postal codes. Seasonal variations in vegetation were modeled by setting values to zero on days outside of pollen and leaf-on seasons. Cox models with vegetation exposures, age as a time axis, and adjusted for sex, material deprivation, and health region were used to estimate hazard ratios (HR) for asthma development. We followed 352,946 children for a total of 1,732,064 person-years and identified 30,816 incident cases of asthma. While annual vegetation (total and trees) measures did not appear to be associated with asthma development, models for pollen and leaf-on seasons yielded significant nonlinear associations. The risk of developing asthma was lower in children exposed to high levels (>33,300 m2) of deciduous crown area for the leaf-on season (HR = 0.69; 95% confidence interval [CI] = 0.67, 0.72) and increased for the pollen season (HR = 1.07; 95% CI =1.02, 1.12), compared with unexposed children. Similar results were found with the Normalized Difference Vegetation Index. The relationship between urban vegetation and childhood asthma development is nonlinear and influenced by vegetation characteristics, from protective during the leaf-on season to harmful during the pollen season.

Seasonal Effect of the Vegetation Clumping Index on Gross Primary Productivity Estimated by a Two-Leaf Light Use Efficiency Model

Recently, light use efficiency (LUE) models driven by remote sensing data have been widely employed to estimate the gross primary productivity (GPP) of different terrestrial ecosystems at global or regional scales. Furthermore, the two-leaf light use efficiency (TL-LUE) model has been reported to improve the accuracy of GPP estimation, relative to the big-leaf MOD17 model, by separating the entire canopy into sunlit and shaded leaves through the use of constant clumping index estimation (Ω). However, ignoring obvious seasonal changes in the vegetation clumping index (CI) most likely results in GPP estimation errors since the CI tends to present seasonal changes, especially with respect to the obvious presence or absence of leaves within the canopy of deciduous vegetation. Here, we propose a TL-CLUE model that considers the seasonal difference in the CI based on the TL-LUE model to characterize general changes in canopy seasonality. This method composites monthly CI values into two or three Ω values to capture the general seasonal changes in CI while attempting to reduce the potential uncertainty caused during CI inversion. In theory, CI seasonality plays an essential role in the distribution of photosynthetically active radiation absorbed by the canopy (APAR). Specifically, the seasonal difference in CI values mainly considers the state of leaf growth, which is determined by the MODIS land surface phenology (LSP) product (MCD12Q2). Therefore, the one-year cycle (OYC) of leaf life is divided into two (leaf-off and leaf-on) or three seasons (leaf-off, leaf-scattering, and leaf-gathering) according to this MODIS LSP product, and the mean CI of each corresponding season for each vegetation class is computed to smoothen the uncertainties within each seasonal section. With these two or three seasonal Ω values as inputs, the TL-CLUE model by which the seasonal differences in CI are incorporated into the TL-LUE model is run and evaluated based on observations from 84 eddy covariance (EC) tower sites across North America. The results of the analysis reveal that the TL-LUE model widely overestimates GPP for most vegetation types during the leaf-on season, particularly during the growth peak. Although the TL-LUE model shows that the temporal characteristics of GPP agree with the EC observations in terms of general trends, the TL-CLUE model further improves the accuracy of GPP estimation by considering the seasonal changes in the CI. The result of GPP estimation from the TL-CLUE model shows a lower error (RMSE = 2.46 g C m−2 d−1) than the TL-LUE model (RMSE = 2.75 g C m−2 d−1) and somewhat decreases the eight-day GPP overestimation in the TL-LUE model with a constant Ω by approximately 9.76 and 8.970% when adapting three and two Ωs from different seasons, respectively. The study demonstrates that the uncertainty of seasonal disturbance in the CI, quantified by a standard deviation of approximately 0.071 relative to the mean CI of 0.746, is diminished through simple averaging. The seasonal difference in CI should be considered in GPP estimation of terrestrial ecosystems, particularly for vegetation with obvious canopy changes, where leaves go through the complete physiological processes of germination, stretching, maturity, and falling within a year. This study demonstrates the potential of the MODIS CI application in developing ecosystem and hydrological models.

Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site

ABSTRACT Leaf area index (LAI) is defined as one half of the total green leaf area per unit ground surface area. Its vertical profile is critical for understanding the remote sensing radiative transfer processes. LAI profile has been derived from airborne and spaceborne LiDAR data, such as the Global Ecosystem Dynamics Investigation (GEDI) installed on the International Space Station. However, the capability of various algorithms for the LAI profile estimation with airborne LiDAR is not clearly evaluated, and the estimated LAI profiles, including the GEDI LAI products, are not been fully validated. This study conducted a quantitative retrieval and validation of the LAI profiles using terrestrial and airborne laser scanning (TLS and ALS) and spaceborne GEDI data over a deciduous needleleaf forest site in northern China. The vertical LAI profile was estimated in the field using an upward digital hemispherical photography (DHP) attached to a portable measurement system in 2020 and 2021. A suite of new LiDAR indices combining both LiDAR return number and return intensity was explored for the LAI profile estimation. All LAI profiles obtained from the DHP, TLS, ALS, and GEDI during the leaf-on season and leaf-off season were compared. The DHP shows a good agreement with the TLS LAI profiles (R2 = 0.97). The LAI profile derived from the ALS data using the combined light penetration index (LPIRI) agrees well (R2 ≥0.86) with the DHP, TLS, and GEDI estimates. In general, the LPIRI is advantageous for regional LAI profile mapping from ALS. The GEDI cumulative LAI corresponds well with the DHP during the leaf-on season (R2 = 0.90, RMSE = 0.23), but underestimates during the leaf-off season (R2 = 0.70, RMSE = 0.14, bias=−0.13). The underestimation is attributed to the higher canopy and ground reflectance ratio (ρv/ρg) assigned in the algorithm and the height discrepancy between the GEDI and field measurements. For the GEDI LAI profile product, further validation and improvement are necessary for other biome types and landscape conditions, especially during the leaf-off season.

Seasonal effects on aboveground biomass estimation in mountainous deciduous forests using ZY-3 stereoscopic imagery

Stereoscopic images are getting attentions in the forest remote sensing. It is essential to clarify environmental effects on detecting forest spatial structures using stereoscopic images in addition to data processing algorithms. The seasonal effects in mountainous deciduous forests is systematically explored in this study from mid-summer to late winter using 8 dates of ZY-3 stereoscopic images. This study reveals that the acquisition season is an important factor affecting the detection of forest spatial structures. In the leaf-on season, for closed forest stands, the forest canopy tops are easily to be detected; for open forest stands, the ground surface can be detected through between-canopy gaps in mid-summer, but be missed in late summer or early fall. In the leaf-off season, the stereoscopic imagery acquired in late winter or early spring was preferred for the detection of ground surfaces, while the mountain shadows limit those acquired in early and middle winter. The ratio of sunlit ground visible in stereoscopic images as the season change is the factor controlling these findings. The images with the smaller ratio are preferred for the detection of forest canopy tops, while the opposite is proper for the detection of ground surfaces. The combination of ZY-3 stereoscopic imagery acquired in mid-summer and late winter gave the best estimation accuracy of forest AGB among possible combinations.

Weekly carbon dioxide exchange trend predictions in deciduous broadleaf forests from site-specific influencing variables

Weekly net-ecosystem-exchange (NEE) data recorded and verified over multiple years, together with site-specific influential ecological variables, for distinct deciduous-broadleaf-forest (DBF) sites in North America can reveal useful relationships regarding their functions as long-term carbon dioxide (CO2) sources and sinks. Machine learning (ML) and regression models have greater success at predicting weekly NEE from some DBF sites than others, from the available site variables. In particular, support vector regression (SVR) and extreme gradient boosting (XGG) ML methods perform better than multi-linear regression in the weekly NEE predictions they generate using up to 24 influential variables. The DBF sites studied are distinguished into those that have followed distinctive, albeit fluctuating seasonal NEE trends, and those that are characterized by abrupt fluctuations in NEE across the leaf-on season. ML models predict weekly NEE for the former sites more reliably than for the latter sites. Consideration of the relative influence of the variables on the XGB and regression model NEE predictions identifies which variables are most influential at specific sites. Short wave radiation (in and out) and air temperature are found to be variables exerting substantial influence on the prediction models for the sites studied. From the prediction results and the relative influences of the available environmental variables, it is concluded that complex processes are involved at those sites showing rapid NEE fluctuations in the leaf-on seasons that are not readily detectable from the environmental variables currently being continuously recorded at those sites.

Inventory of close-to-nature forest stands using terrestrial mobile laser scanning

• Hand-held mobile LiDAR data of a close-to-nature forest in the leaf-on season. • Fully automatic processing using modified mean-shift and RANSAC algorithms. • 96.5 % detection and 3.5 cm DBH RMSE for trees with DBHs from 4 to 80 cm. • A comparison of different levels of low-intensity filtering is performed. • A walking trajectory consisting of concentric circles is recommended. In this work, we evaluated mobile laser scanning (MLS) technology for the acquisition of individual tree data in a close-to-natural forest structure. The data was collected during the leaf-on period in a single-tree selection forest stand. Individual tree positions and diameters at breast height (DBHs) were acquired in an automatic process. MLS data was collected on 1000 m 2 circular inventory plots. We compared three trajectories consisting of the plot perimeter and 1), two perpendicular lines, 2) an additional concentric circle, and 3) four parallel lines. We compared two algorithms for tree segmentation: 1) a density-based approach and 2) a modified mean-shift algorithm. The diameters were estimated using a modified random sample consensus (RANSAC) algorithm. We tested a series of intensity thresholds for filtering returns from green vegetation. We achieved the best results with an intensity threshold of 0.7 quantile of point intensities, and mean-shift segmentation, resulting in the correct identification of trees representing 96.5 % of basal area and an overestimation of 6.8 % of the total basal area. The algorithm omitted mainly small trees and trees at close distances. False detections mainly comprised unvalidated detections of real trees that were not field-measured as their diameter did not exceed the registration limit, or were caused by point structures representing leaves and understory vegetation. Diameters were estimated with a mean error of 0.03 cm and a root mean square error of 3.5 cm. A joinpoint regression model demonstrated that for small trees (<9 cm) the diameters were generally overestimated. Diameters above 12 cm were underestimated consistently by 1 cm. The trajectory comprising two concentric circles was the most efficient.

The association between tree canopy cover over streets and elderly pedestrian falls: A health disparity study in urban areas

Older pedestrians are vulnerable to outdoor falling while walking on streets/sidewalks, but few studies have examined the role of the street environment and tree canopy cover over streets in relation to pedestrian falls among the elderly. We used spatial analysis to examine the association between tree canopy cover over streets and pedestrian falls reported to Emergency Medical Service (EMS) providers from March 2013 to February 2020 among adults aged 65 and older living in urban areas of Marin County, CA. Tree canopy cover over streets was measured using 1-m resolution of tree canopy within street polygons. After controlling for socioeconomic status and built environments, we found an inverse association between tree canopy cover over streets and elderly pedestrian fall rates at the census block level. Specifically, with a 10-percentage point increase in tree canopy cover over streets of a block, we expected to see about an 11.2% decrease in the elderly pedestrian fall rate. We found that the inverse relationship between tree canopy cover over streets and elderly pedestrian falls was only significant during the leaf-on season in the spring and summer. Finally, sub-analysis found that the relationship between tree canopy cover over streets and elderly pedestrian falls was stronger in low-income areas, compared to high-income areas. Planting street trees is a potential evidence-based intervention to prevent pedestrian falls. However, special attention must go beyond the quantity of tree canopy cover over streets to consider biophysical factors and social conditions.

InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data

Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km2) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided.

Seasonal effects of street trees on particulate matter concentration in an urban street canyon

The net influence of trees on local air quality in urban street canyons, negative or positive, has rarely been evaluated by field investigations. To quantify the influence of street trees on particulate matter (PM) and total suspended particle (TSP) concentration near ground level, a seasonal field investigation was carried out in a street canyon with an aspect ratio of 0.52 in Shenyang, China. The greening tree species in this street were Sophora japonica and Ulmus pumila. We monitored PM1, PM2.5, PM4, PM10, TSP concentration and the microclimatic conditions at locations with or without trees from before the leaf expansion season until the end of the defoliation season. Concentration of PM2.5 was higher at the tree-stand location, whereas concentration of PM10 and TSP were higher at the tree-free location during the leaf-on season. Street trees were beneficial to coarse particle removal during the leaf-off and leaf-expansion season, but PM pollution increased during defoliation. Wind direction was more uniform at the tree-stand location. Street trees weakened the impact of the wind on PM dispersion near ground. This indicates that street trees might pose a health threat to pedestrians because fine PM can enter the human respiratory system more easily than coarse PM.

Multi-year eddy covariance measurements of net ecosystem exchange in tropical dry deciduous forest of India

Tropical deciduous forests are unique in terms of their geographical distribution, strong seasonality and their contribution to global carbon dynamics, but yet their carbon sequestration potential is poorly sampled. In the current study, we report the carbon balance of 65-year-old tropical dry deciduous forest in central India using the multi-year eddy covariance measurements from November 2011 to May 2019. Over the study period, the forest site was observed to be a net sink of atmospheric CO2 with a mean annual net ecosystem productivity (NEP) of 524 (± 40; ±1 SD across different years) g C m−2 yr−1 with a 233 (±15) day growing season. The NEP was partitioned into gross primary productivity (GPP) of 3358 (± 167) g C m−2 yr−1 and annual carbon loss due to respiration and decomposition (Reco) of 2834 (± 157) g C m−2 yr−1. The ecosystem showed a strong seasonality as a source of carbon during the leaf-off season (March to June), and as a carbon sink during the rest of the year with significant sequestration during the winter season (October to December). The intra-annual analysis suggested that CO2 flux is primarily controlled by canopy greenness with mean Reco/GPP ratio varying from 1.90 (± 0.12) to 0.79 (± 0.04) during leaf-off to leaf-on seasons. The monthly C fluxes in the growing season are found to be strongly correlated to the environmental variables rather than in the leaf-off season, while variability in monthly ecosystem respiration was better explained by air temperature during the leaf-off season than the growing season. Further, the inter-annual variability of NEP was mainly dependent on growing season length and mean annual temperature. The variations in annual GPP and Reco were directly dependent on the increase in mean annual temperature. The eddy covariance-based NEP was complemented by close independent biometric estimates, imparting confidence in our measurements. In conclusion, this tropical dry deciduous forest site is a substantial carbon sink and would add crucial information regarding carbon budgeting of tropical forests in global carbon balance models.

Optimizing Reduction Pruning of Trees Under Electrical Lines: The Influence of Intensity and Season of Pruning on Epicormic Branch Growth and Wound Compartmentalization

Reduction pruning of the main stem is commonly used during the maintenance of power lines to encourage the establishment and development of scaffold limbs away from wires. Understanding the physiology of epicormic branch initiation and growth as well as wound compartmentalization following reduction pruning are important for optimizing the pruning cycle and maintaining healthy and safe trees. In this study, the influence of both intensity and time of year of pruning on epicormic branch response and wound compartmentalization was investigated on 56 11-year-old Pennsylvania ash trees (Fraxinus pennsylvanica Marsh.) about 5 to 7 m in height within a controlled nursery environment. During the second growing season following reduction of the main stem, the number, height, and volume of epicormic branches, as well as tallest epicormic branches and the area of discolored wood, increased with pruning intensity. Pruning during the leaf-on season compared to the leaf-off season limited the establishment and development of epicormic branches without affecting wound-closure rate or the area of wood discoloration at the cutting point. Results are consistent with the known seasonal fluctuation of carbohydrates reserves. In the context of the electrical distribution network, where trees are subjected to pruning throughout the year, trees pruned in summer during a maintenance cycle could be pruned during the next cycle, in winter, and so on, to optimize the return interval of the pruning cycle.

Advances in smartphone positioning in forests: dual-frequency receivers and raw GNSS data

Abstract Smartphones with their capability to receive Global Navigation Satellite Systems (GNSS) signals can be currently considered the most common devices used for positioning tasks, including forestry applications. This study focuses on possible improvements related to two crucial changes implemented into Android smartphone positioning in the last 3 years – dual-frequency (L1/L5) GNSS receivers and the possibility of recording raw GNSS data. The study comprises three experiments: (1) real-time measurements of individual points, (2) real-time recording of trajectories, and (3) post-processing of raw GNSS data provided by the smartphone receiver. The real-time tests were conducted using final positions provided by the internal receiver, i.e. without further processing or averaging. The test on individual points has proven that the Xiaomi Mi8 smartphone with a multi-constellation, dual-frequency receiver was the only device whose accuracy was not significantly different from single-frequency mapping-grade receiver under any conditions. The horizontal accuracy of most devices was lower during leaf-on season (root mean square errors between 5.41 and 12.55 m) than during leaf-off season (4.10–11.44 m), and the accuracy was significantly better under open-area conditions (1.72–4.51 m) for all tested devices when compared with forest conditions. Results of the second experiment with track recording suggest that smartphone receivers are better suited for dynamic applications – the mean shift between reference and measured trajectories varied from 1.23 to 5.98 m under leaf-on conditions. Post-processing of the raw GNSS data in the third experiment brought very variable results. We achieved centimetre-level accuracy under open-area conditions; however, in forest, the accuracies varied from meters to tens of meters. Observed loss of the signal strength in the forest represented ~20 per cent of the open-area value. Overall, the multi-constellation, dual-frequency receiver provided more robust and accurate positional solutions compared with single-frequency smartphones. Applicability of the raw GNSS data must be further studied especially in forests, as the provided data are highly susceptible to multipath and other GNSS adverse effects.

Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data

Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it possible to assess the impact of natural disturbances in addition to other factors (such as urban development) on urban canopy changes in the classification map created by OBIA? (3) How can we use Light Detection and Ranging (LiDAR) data and technology to extract urban canopy metrics accurately and effectively? The urban forest canopy area and location within the City of St Peter, Minnesota (MN) boundary between 1938 and 2019 were defined using both OBIA and random-point-based methods with high-spatial-resolution aerial images. Impacts of natural disasters, such as the 1998 tornado and tree diseases, on the urban canopy cover area, were examined. Finally, LiDAR data was used to determine the height, density, crown area, diameter, and volume of the urban forest canopy. Both OBIA and random-point methods gave accurate results of canopy coverages. The OBIA is relatively more time-consuming and requires specialist knowledge, whereas the random-point-based method only shows the total coverage of the classes without locational information. Canopy change caused by tornado was discernible in the canopy OBIA-based classification maps while the change due to diseases was undetectable. To accurately exact urban canopy metrics besides tree locations, dense LiDAR point cloud data collected at the leaf-on season as well as algorithms or software developed specifically for urban forest analysis using LiDAR data are needed.

Mapping Seasonal Tree Canopy Cover and Leaf Area Using Worldview-2/3 Satellite Imagery: A Megacity-Scale Case Study in Tokyo Urban Area

This study presents a methodology for developing a high-resolution (2 m) urban tree canopy leaf area inventory in different tree phenological seasons and a subsequent application of the methodology to a 625 km2 urban area in Tokyo. Satellite remote sensing has the advantage of imaging large areas simultaneously. However, mapping the tree canopy cover and leaf area accurately is still difficult in a highly heterogeneous urban landscape. The WorldView-2/3 satellite imagery at the individual tree level (2 m resolution) was used to map urban trees based on a simple pixel-based classification method. The comparison of our mapping results with the tree canopy cover derived from aerial photography shows that the error margin is within an acceptable range of 5.5% at the 3.0 km2 small district level, 5.0% at the 60.9 km2 municipality level, and 1.2% at the 625 km2 city level. Furthermore, we investigated the relationship between the satellite data (vegetation index) and in situ tree-measurement data (leaf area index) to develop a simple model to directly map the tree leaf area from the WorldView-2/3 imagery. The estimated total leaf area in Tokyo urban area in the leaf-on season (633 km2) was twice that of the leaf-off season (319 km2). Our results also showed that the estimated total leaf area in Tokyo urban area was 1.9–6.2 times higher than the results of the moderate-resolution (30 m) satellite imagery.

Analysing the potential of UAV point cloud as input in quantitative structure modelling for assessment of woody biomass of single trees

Accurate tree metrics is essential for forest management. Quantitative Structure Model (QSM) which can reconstruct an accurate 3D model of trees, has been used with Terrestrial Laser Scanning (TLS) point cloud as input. Indeed, image-based Structure from Motion (SfM) can produce point cloud as well. Unmanned Aerial Vehicle (UAV), which can collect images of a large scale in a short period, seems like a good choice for forest study.This study aims to investigate the feasibility of UAV point cloud for QSM of individual trees. Flights were carried out during the leaf-on and leaf-off seasons with an inclined camera onboard. Four oblique camera angles were used during the leaf-on season to obtain the optimal angle for UAV data collection. The Diameter at Breast Height (DBH) derived from UAV point cloud and QSM were compared with field measured data. The accuracy of QSM-biomass estimations was assessed with reference, which was calculated using field measured DBH through the allometry.In this study, it was found that the point density of the whole scene increased with the increase of oblique camera angle. DBH extracted from the UAV-generated point cloud versus reference showed no significant difference (p > 0.05), while a significant difference was found between QSM-estimated DBH and the reference DBH. The QSM-based biomass showed 49.16% underestimation for leaf-off season. Although the QSM did not behave well with UAV data, it was found that the UAV point cloud could be used for accurate tree parameter extraction and could be a useful tool for forest management.

Ephemeral temporal partitioning may facilitate coexistence in competing species

Animals change their daily activity patterns in response to season, food availability and the presence of competitors. Competition may be an important driver of a species’ daily activity pattern, as animals manage conflict by avoiding each other temporally. We evaluated how vegetation structure and the presence of competitors changed the daily activity patterns of closely related fox squirrels, Sciurus niger, and grey squirrels, Sciurus carolinensis. We monitored squirrel activity in north and central Florida using passive game cameras at two spatial scales: local and point. To understand how seasonality and competition interact to drive behaviour, we compared squirrel activity during the leaf-off (1 January – 15 March) and leaf-on (16 March – 1 July) seasons. We tested for a relationship between squirrel activity and canopy cover by fitting a von Mises kernel distribution. To test how season and competition affected squirrel behaviour, we compared activity by computing a kernel density overlap function, ranging from 0 (no overlap: the squirrels are never active at the same time) to 1 (complete overlap: the squirrels have identical activity patterns). We found that daily squirrel behaviour was not influenced by canopy cover (P = 0.61). Fox squirrels had a single activity peak occurring around midday. In contrast, grey squirrels had a bimodal activity pattern with peaks shortly after sunrise and before sunset. The intensity of this partitioning existed on a gradient and changed with season and the presence of competitors. Fox and grey squirrel daily patterns overlapped the most when they were allopatric in the leaf-on season (overlap = 0.70, P

Assessing horizontal accuracy of inventory plots in forests with different mix of tree species composition and development stage

Global navigation satellite systems (GNSS) have a wide range of applications in forest industry, including forest inventory. In this study, the horizontal accuracy of 45 inventory plots in different forest environments and 5 inventory plots under open sky conditions were examined. The inventory plots were located using a mapping-grade GNSS receiver during leaf-on season in 2017. True coordinates of the plot centres were acquired using a survey-grade GNSS receiver during leaf-off season in 2018. A study was conducted across a range of forest conditions in the forest unit Vígľaš, which is located in Slovakia (Central Europe). Root mean square error of horizontal accuracies was 8.45 m in the plots under forest canopy and 6.61 m under open sky conditions. We note decreased positional errors in coniferous forests as well as in younger forests. However, results showed that there is no statistically significant effect of tree species composition and stand age on horizontal accuracy.

A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China

Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems.

Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia

The study considers a forest inventory for the mean volume, basal area, and coniferous/deciduous mapping of a large territory in central Siberia (Russia), employing a camera relascope at arbitrary sized sample plots and medium resolution satellite imagery Landsat 8 from the leaf-on and leaf-off seasons. The research bases are on field plots and satellite data that are acquired for the real operational forest inventory, performed for industrial purposes during summer–fall 2015. Sparse Bayesian regression was used to estimate linear regression models between field-measured variables and features derived from satellite data. Coniferous/deciduous mapping was done, applying maximum likelihood classification. The study reported the root mean square error for the mean volume and basal area under 25% for both the plot level and compartment level. The overall accuracy of the forest-type classification in coniferous, mixed coniferous/deciduous, and deciduous classes was 71.6%. The features of Landsat 8 images from both seasons were selected in almost every model, indicating that the use of satellite imagery from different seasons improved the estimation accuracy. It has been shown that the combination of camera relascope-based field data and medium-resolution satellite imagery gives accurate enough results that compare well with previous studies in that field, and provide fast and solid data about forests of large areas for efficient investment decision making.

Seasonal change of leaf and woody area profiles in a midlatitude deciduous forest canopy from classified dual-wavelength terrestrial lidar point clouds

This study demonstrates the retrieval of separate vertical height profiles of leaf and woody areas in both leaf-off and leaf-on seasons at a largely broadleaf deciduous forest site in the Harvard Forest of central Massachusetts, USA, using point clouds acquired by a terrestrial laser scanner (TLS), the Dual-Wavelength Echidna® Lidar (DWEL). Drawing on dual-wavelength information from the DWEL, we classified points as leafy or woody hits using their near-infrared (1064 nm) and shortwave infrared (1548 nm) apparent reflectance coupled with the 3-D spatial distribution patterns of points. We developed a new indirect assessment approach that quantified the accuracies (user’s, producer’s and overall) and variance of accuracies of the 3-D point classifications. The overall classification accuracy estimated by this indirect approach was 0.60 ± 0.01 – 0.77 ± 0.01 for leaf-off points and 0.71 ± 0.02 – 0.78 ± 0.01 for leaf-on points. These estimated accuracies were then utilized to adjust the proportions of separate gap probabilities to reduce the biases in the separate leaf and woody area profiles due to classification errors. Separate retrievals of leaf and woody area profiles revealed the change in their spatial heterogeneity over the 1-ha plot with season. These retrievals also allowed height-explicit estimation of the woody-to-total ratio, which is an empirical parameter often used to remove woody contributions to leaf area index retrievals made by optical methods. The estimates suggested the woody-to-total ratios generally stayed stable along height in the middle and upper canopy for this site but varied in the lower canopy. More accurate estimates of leaf area and its vertical profile are important for better measurement and modeling of the radiation regime of forest canopies, and thus their photosynthetic capacity. By separating leafy and woody materials in three dimensions, dual-wavelength TLS offers the prospect of better understanding of forest cycling of matter and energy within local and global ecosystems.