Maximum Canopy Height Research Articles

Evaluation of Turfgrasses for Qualitative and Quantitative Traits in Varanasi Region of India

The present experiment was conducted at Horticulture Research Farm, Department of Horticulture, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi. This study was carried out to evaluate performance of eleven turfgrass genotypes for qualitative traits, quantitative traits and their genetic variability under same climatic condition. The experimental design was a Randomized Block Design (RBD) with three replications. Eleven genotypes consist of Bermuda grass, Centipede grass, Panama grass, Bargusto grass, Tiftdwarf 419 grass, Local grass, Palma grass, Bahia grass, Manila grass, Crow Foot grass and Tanu Variant grass. These were assessed for their suitability to be used as turfgrass under Varanasi region. All the grasses exhibited semi prostrate growth habit except local grass having upright growth habit. Panama and Local grass showed fine leaf texture whereas, Manila grass exhibited coarse texture. Dark green colour was observed in Local, Crow Foot and Panama grass whereas, Centipede grass showed light green colour. Local grass exhibited maximum canopy height, leaf length, leaf width, chlorophyll content, stolen internode diameter, culm length, shoot length and shoot diameter. The maximum genotypic (187.22) and phenotypic variance (197.80) was observed for parameters like fresh weight. Canopy height showed maximum phenotypic coefficient of variation (47.04) and genotypic coefficient of variation (46.16) over all the parameters. Genetic advance maximum (27.49) for parameter fresh weight and genetic advance as a percentage of mean was maximum also for fresh weight (93.30). The maximum heritability at broad sense was observed for the parameter dry weight (98.25).

Evaluating the applicability of high-density UAV LiDAR data for monitoring tundra grassland vegetation

ABSTRACT Grasslands are one of the most widespread biomes on Earth. The interaction of UAV LiDAR with grasses and herbaceous plants is an infrequently covered area of research, apart from its utilisation for the estimation of above ground biomass. To evaluate the applicability of LiDAR for monitoring grassland vegetation, two model plots in the arctic-alpine tundra (Krkonoše Mountains, Czech Republic) were selected. Throughout the growing season, UAV LiDAR point clouds (800 points/m2) and multispectral imagery (9 cm) were acquired in monthly intervals, along with reference botanical and terrestrial LiDAR data. The study provides insight into the analysis of compact low-lying vegetation at the species level. A set of experiments was conducted focusing on the analysis of LiDAR information loss, vertical strata, and structural metrics computed over the grass species/communities. Random forest was used to determine the importance of metrics by out-of-bag permutation of predictors and to classify vegetation species using UAV LiDAR-based metrics, as well as image-based digital surface models alone and in fusion with multispectral data. The vertical distribution of the UAV LiDAR points varied significantly between species and throughout the growing season. Loss at the canopy bottoms was apparent, with the lowest points corresponding to dry grass matter rather than relief. Grasslands had the highest penetration capability at the start of the growing season. In terms of metrics, maximum canopy height was the most important. The multitemporal LiDAR-derived structural metrics were able to differentiate (F-1 score above 90%) all the shrubs/trees and dominant grass Calamagrostis villosa. Mixed and low-abundance species were indistinguishable. The overall accuracy scores in a 9 (27) cm grid reached 75.1% (78.1) and 63.5% (66.4) for Bílá louka meadow and Úpské rašeliniště bog, respectively. Fusing the LiDAR-derived features with multispectral imagery did not enhance the classification results apart from the delineation of shrubs and trees.

Feasibility of using Ku-band helicopter-borne microwave radar for stem volume and biomass estimation in boreal forest

Under the global climate challenges, accurate assessment of forest variables is becoming increasingly important for effective forest management and monitoring. While long-wavelength satellite radar data (P-, L-band) has been widely used for this purpose, the potential of airborne Ku-band microwave radar remains underexplored. In this paper, an airborne Ku-band frequency modulated continuous wave (FMCW) profiling radar data was used to predict forest variables including plot-level basal-area weighted mean canopy height (Hg), mean diameter at breast height (DBHg), stem volume (V) and above ground biomass (AGB) using the random forest method. In addition, the maximum canopy heights were computed from the radar-derived results. The estimated results were compared with 55 manually-measured and accurately matched field plots consisting of thousands of trees. The comparison results show that the estimation accuracy is at a good level, with the relative Root Mean Square Errors (RMSE%) and the coefficient of determination (R2) of the four variables were Hg (10.04 %, 0.75), DBHg (17.25 %, 0.63), V (21.11 %, 0.67) and AGB (19.75 %, 0.63), respectively. The estimation of plot-level maximum heights resulted in a relative RMSE of 7.95 % and R2 of 0.89. Through comparing with previous similar studies, the accuracies of estimating the four variables from airborne Ku-band data are comparable to those of high-precision satellite microwave radar data. The study results indicate that the canopy gaps and adequate penetration of airborne Ku-band signals into the canopy enable the characterization of the canopy layer and the ground floor, allowing for accurate estimation of forest variables in boreal forests.

Revealing forest structural "fingerprints": An integration of LiDAR and deep learning uncovers topographical influences on Central Amazon forests

Amazon forests are characterized by rich structural diversity. However, the influence of factors such as topography, soil attributes, and external disturbances on structural variability is not always well characterized, and traditional structural metrics may be inadequate to capture this type of complexity. While LiDAR offers expanded structural insights, traditional parameters used in LiDAR analysis, such as mean or maximum canopy height, are not always well directly linked to environmental variables like topography. Emerging approaches merge LiDAR with machine learning to uncover deeper structural complexities. However, work to date may fail to fully utilize the potential of fine-scale LiDAR information. Here we introduce a novel approach, leveraging 2D point cloud images derived from a profiling canopy LiDAR (PCL). The technique targets intricate details within LiDAR point clouds by using deep learning algorithms. With a dataset from the Central Amazon comprising 18 multitemporal transects of 450 m in length, our objective was to detect structural "fingerprints" of varied topographical types along a hillslope, comprising: Riparian, White-sand, and Plateau, and to detect any gradient of structural shifts based on terrain variations here represented by the height above the nearest drainage (HAND). The dataset was trained and tested using a leave-one-group-out approach (LOGO) in which, for each iteration, a complete 450 m multitemporal transect was excluded from training and tested after each iteration. The fast.ai platform and a ResNet-34 architecture, coupled with transfer learning, were used to perform a classification to distinguish between three topographical types. Furthermore, a hybrid model combining a Convolutional Autoencoder, and Partial Least Square (PLS) regression was designed to detect forest structural gradient correlations with HAND variation. Cross-validation achieved a promising high weighted F1 score of 0.83 to classify forests based on the topographical types. Additionally, a combined Convolutional Autoencoder and PLS regression revealed a strong correlation (R2 = 0.76) between actual and predicted HAND. Innovatively combining deep learning with ground-based PCL LiDAR, our study revealed unique Amazon Forest structures connected to topographic variation. Our findings underscore the transformative potential of such integrative approaches for investigating forest dynamics and promise a powerful new tool for understanding climate-related forest structure change.

Performance of Valeriana jatamansi in Hill Zone of West Bengal Concerning Manure Sources

The organic source of nutrients would be a promising practice for Valeriana jatamansi cultivation which maintains its curative value. A set of experiments was conducted at Hill Zone of West Bengal, Kalimpong for two consecutive cycles to standardize organic nutrient management in V. jatamansi for optimum growth and economic yield. Different organic sources of nutrients such as farm yard manure (FYM), bio-fertilizer (BF), and vermicompost (VC) were applied at various doses along with a recommended dose. We applied the treatments as follows: T1= FYM (5 t.ha-1), T2= VC (2 t.ha-1), T3= FYM (5 t.ha-1) + VC (2 t ha-1), T4= FYM (5 t.ha-1) + PSB, T5= VC (2 t.ha-1) + PSB, T6= FYM (5 t.ha-1) + VC (2 t.ha-1) + PSB, T7= Control, T8= RDF through inorganic fertilizers (50:25:25 kg.ha-1 as N, P2O5, and K2O). The use of organic manures and PSB significantly (P<0.05) increased the rhizome yield and soil-available macronutrients in V. jatamansi due to the release of nutrients from the decomposition of organic manures. The addition of T6 resulted in a maximum canopy height (18.28 cm), plant fresh biomass (16.42 t.ha-1), dry biomass (3.19 t.ha-1), rhizome fresh weight (3.16 t.ha-1), and dry weight (1.16 t.ha-1), as well as highest organic carbon content (0.75%), nitrogen (221 kg.ha-1), phosphorus (46.70 kg.ha-1), and potassium (201 kg.ha-1). Applying FYM, vermicompost, and PSB combination would be a promising strategy for growing V. jatamansi.

Salinity‐induced limits to mangrove canopy height

AbstractAimMangrove canopy height is a key metric to assess tidal forests' resilience in the face of climate change. In terrestrial forests, tree height is primarily determined by water availability, plant hydraulic design, and disturbance regime. However, the role of water stress remains elusive in tidal environments, where saturated soils are prevalent, and salinity can substantially affect the soil water potential.LocationGlobal.Time PeriodThe canopy height dataset provides a global snapshot of the maximum mangrove height geographical distribution for the year 2000. Climate and environmental variables extend over the period 1970–2018.Major Taxa StudiedMangroves.MethodsWe use global observations of maximum canopy height, species richness, air temperature, and seawater salinity—a proxy of soil water salt concentration—to explore the causal link between salinity and mangrove stature.ResultsOur findings suggest that salt stress limits mangrove height. High salinity favours more salt‐tolerant species, narrowing the spectrum of viable traits. Highly salt‐tolerant mangroves have evolved to cope with high salt concentrations in the soil, but this adaptation comes at a cost. They typically have lower rates of photosynthesis and growth, resulting in reduced productivity and smaller stature compared to more salt‐sensitive mangrove species. This suggests a causal link between salinity, biodiversity, and tree height, where high salinity selects for more salt‐tolerant species that tend to be less productive and shorter.ConclusionsWe hypothesize that the salinity‐induced limit to mangrove canopy height is the direct result of a reduction of primary productivity, an increment in the risk of xylem cavitation, and an indirect consequence of the decrease in biodiversity. As sea‐level rise enhances coastal salinisation, failure to account for these effects can lead to incorrect estimates of future carbon stocks in Tropical coastal ecosystems and endanger preservation efforts.

Understanding the natural expansion of white mangrove (Laguncularia racemosa) in an ephemeral inlet based on geomorphological analysis and remote sensing data

The interactions between local tides and river discharges are crucial in the processes related to the recruitment of mangrove propagules in estuarine systems. This investigation aimed to determine the causes of the recent natural recruitment and expansion of Laguncularia racemosa in mudflats within an ephemeral inlet in Mexico. We conducted a fluvial and coastal geomorphology assessment with spaceborne and UAV-based images. We deployed and recorded continuous data loggers in the estuarine system to assess water level and salinity. Depending on the available data, we used a combination of cloud-computing Google Earth Engine, UAV-Digital Surface Models, LiDAR, Google Earth images, and biophysical variables to monitor mangrove forests from 2005 to 2022. When the inlet is open, the estuarine system presents a full tidal range (∼1–1.5 m) with a strong salinity gradient (0–35 mS/cm), in contrast to the strong freshwater influence and minimal water level variability (<10 cm) that prevails for three months when the inlet is closed. Once the mouth of the river closes, there is considerable sediment accumulation, creating mudflat areas adjacent to the mangrove forests where Laguncularia racemosa propagules begin to establish under minimal water level variability and oligohaline conditions. After 16 years, the new forest expanded by 12.3 ha, presenting a very high density (10000 stems/ha), a considerable basal area (54–63 m2/ha), and a maximum canopy height of 15.8 m, which largely surpasses that of other semiarid Laguncularia racemosa forests within permanent open-inlet systems or even in ephemeral inlets with different hydrological conditions. Our study will help to understand the causes of natural Laguncularia racemosa recruitment in extremely dynamic systems.

Stronger effect of individual species’ traits than shading on aquatic plant community productivity and interspecific competition

Competition is one of the major factors structuring plant communities. Species with similar traits generally compete more intensely and have more similar yield than functionally dissimilar species, which often respond differently to environmental change. Little is known about how the interacting species’ traits influence the effect of environmental change on interspecific competition. However, theory predicts that environmental change should lead to more asymmetric competition, by favouring the species best adapted to the particular environmental change. Here we used a mesocosm experiment with three common aquatic plant species from the Baltic Sea (Northern Europe), to test how community productivity and competition asymmetry were affected by functional dissimilarity, individual species’ traits and a common stressor: shading. Competition asymmetry was defined as the absolute difference in reductions in yield relative to monocultures of two interacting species. Community productivity decreased and competition asymmetry increased with functional dissimilarity of the interacting species, possibly explained by the traits of the superior species, which had higher specific leaf area, maximum canopy height and primary production rate than the subordinate species. Community productivity was not affected by shading, contrary to our expectation, while competition asymmetry was higher in shaded than ambient conditions. Individual species yield depended on species identity and species combination. Only the shortest species was negatively affected by shading. Thus, by favouring tall-growing species, shading can alter interspecific competition. Together, these findings suggest that non-random species loss following environmental change can be caused by competitive exclusion, in addition to a direct effect of abiotic filtering.

Maximum canopy height is associated with community phylogenetic structure in boreal forests

AbstractUnderstanding how maximum canopy height is related to forest community assembly is essential yet largely unexplored. Maximum canopy height is affected by competition and abiotic environmental factors through different ecological processes (e.g. niche differentiation and environmental filtering), as well as historical or stochastic factors. However, little has been done to empirically examine the ecological processes that influence maximum canopy height. We set out to examine the relationship between maximum canopy height and community phylogenic structure. We surveyed maximum canopy heights from a regional dataset of forest plots (466 sites of 50 m × 50 m) from the boreal forest of northeastern Alberta, Canada. We then explored the relationships between maximum canopy height as measured by airborne LiDAR (Light Detection and Ranging) and the phylogenetic structure of seed plants, represented by net relatedness index and nearest taxa index. We found stronger phylogenetic clustering among major evolutionary clades for communities with higher maximum canopy height, which implied that environmental filtering by abiotic factors is a driving factor for boreal forests. However, we also found stronger phylogenetic overdispersion within each clade for communities with higher maximum height, indicating more intense niche differentiation. Our results suggest that communities with higher maximum canopy height may have experienced more intense historical abiotic environmental filtering and recent niche differentiation in boreal forests. These findings will contribute to the monitoring and management of forest biodiversity.

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice.

Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.

Evaluation of NASA’s GEDI Lidar Observations for Estimating Biomass in Temperate and Tropical Forests

Accurate estimation of forest aboveground biomass (AGB) is vital for informing ecosystem and carbon management. The Global Ecosystem Dynamics Investigation (GEDI) instrument—a new-generation spaceborne lidar system from NASA—provides the first global coverage of high-resolution 3D altimetry data aimed specifically for mapping Earth’s forests, but its performance is yet to be tested for large parts of the world. Here, our goal is to evaluate the accuracies of GEDI in measuring terrain, forest vertical structures, and AGB in reference to independent airborne lidar data over temperate and tropical forests in North America. We compared GEDI-derived elevations and canopy heights (e.g., relative height percentiles such as RH50 and RH100) with those from the Shuttle Radar Topography Mission (SRTM) or from two airborne lidar systems: the Laser Vegetation Imaging Sensor (LVIS) and Goddard’s Lidar, Hyperspectral and Thermal system (G-LiHT). We also estimated GEDI’s geolocation errors by matching GEDI waveforms and G-LiHT pseudo-waveforms. We assessed the predictive power of GEDI metrics in estimating AGB using Random Forests regression. Results showed that GEDI-derived ground elevations correlated strongly those from LVIS, G-LiHT, and LVIS (R2 &gt; 0.91), but with nonnegligible RMSEs of 5.7 m (G-LiHT), 3.1 m (LVIS), and 10.9 m (SRTM). GEDI canopy heights had poorer correlation with LVIS (e.g., R2 = 0.44 for RH100) than with G-LiHT (e.g., R2 = 0.60 for RH100). The estimated horizontal geolocation errors of GEDI footprints averaged 6.5 meters, comparable to the nominal accuracy of 9 m. Correction for the locational errors improved the correlation of GEDI vs G-LiHT canopy heights significantly, on average by 53% (e.g., R2 from 0.57 to 0.82 for RH50). GEDI canopy metrics were useful for predicting AGB (R2 = 0.82 and RMSE = 19.1 Mg/Ha), with the maximum canopy height RH100 being the most useful predictor. Our results highlight the importance of accommodating or correcting for GEDI geolocation errors for estimating forest characteristics and provide empirical evidence on the utility of GEDI for monitoring global biomass dynamics from space.

Megaherbivore exclusion led to more complex seagrass canopies and increased biomass and sediment Corg pools in a tropical meadow

In some regions of the Caribbean Sea, seagrasses have been negatively affected by sea turtle overgrazing. Seagrass canopy complexity has declined at a long-term monitoring site in Costa Rica. We deployed megaherbivore exclosures for 13 months and found an increase over time in seagrass cover and maximum canopy height to ~ 75% and 20 cm respectively in the exclosures; while they remained steady in controls at &amp;lt; 25% and ~ 5 cm. Following exclusion, above ground biomass was higher in exclosures (320 ± 58 g DW m-2) compared to controls (171 ± 60 g DW m-2). Leaves were longer and wider in the exclosures (8 ± 5 cm and 0.8 ± 0.2 cm) compared to controls (2 ± 2 cm and 0.5 ± 0.1 cm). Above ground biomass Corg pools in exclosures (1.2 ± 0.2 Mg ha-1) were two-times higher than in controls (0.6 ± 0.2 Mg ha-1). Meanwhile, there was no variation between treatments in seagrass shoot density (1,692 ± 803 shoots m-2), below ground biomass (246 ± 103 g DW m-2) and its Corg pool (0.8 ± 0.4 Mg ha-1). Relative sediment level increased up to 4.4 cm within exclosures revealing a net increase in sediment Corg, while surficial sediment Corg percentage was similar between exclosures and controls. Releasing these meadows from megaherbivore grazing therefore led to a clear increase within exclosures of seagrass cover, canopy complexity, above ground biomass, and Corg pools in above ground biomass and sediment. Our study reveals that the decline in canopy complexity over time at this meadow is linked to megaherbivore grazing and has most likely led to a decrease in blue carbon pools. Excessive megaherbivore grazing at this site could lead to a continued decline or potential loss of the meadow, and seagrass conservation and restoration initiatives should include consideration of trophic dynamics.

Tradeoffs and synergies among ecosystem services, biodiversity conservation, and food production in coffee agroforestry

Concerns over the capacity of the world’s existing agricultural land to provide food for the global population under climate change and continued biodiversity loss have set the stage for a prevailing narrative of inherent tradeoffs with agricultural production. Coffee, a major export of tropical countries, offers a unique opportunity to examine how different management practices can lead to a variety of outcomes in food security, ecosystem services, and biodiversity conservation. Our study examined this intersection to identify tradeoffs and synergies using compiled data from Puerto Rico. At the island level, we analyzed data on coffee yield and planted area under shade or sun management. At the farm level, we analyzed management variables (percent shade cover, maximum canopy height, ground cover, and food crop richness), non-provisioning ecosystem services variables (total farm carbon storage, soil organic carbon storage, coffee plant carbon biomass, and hurricane resistance and resilience), and biodiversity variables (ant, bird, and lizard richness and abundance). At the island level, we found that planted area was the most significant predictor of total production, suggesting no obvious tradeoff between production and shade management in coffee farms. At the farm level, canopy cover of shade trees was negatively correlated with ground cover and positively correlated with food crop richness, suggesting a synergy between agroforestry and subsistence food production. We detected mostly synergies associated with ecosystem services, biodiversity conservation, and agroforestry management and no tradeoffs among ecosystem service and biodiversity parameters. Shade canopy cover significantly increased total carbon storage, coffee plant biomass, hurricane resistance, and bird species richness. Shade canopy height had a similar positive effect on total farm carbon storage while food crop richness had a positive effect on farm resilience following Hurricane Maria. Ground cover was positively associated with soil carbon storage and pest-controlling lizard abundance. Tradeoffs related to agroforestry management included an inverse relationship between ground cover and hurricane resistance and more dominance of an invasive ant species in farms with higher shade canopies. We discuss the implications of practicing agroforestry principles in this smallholder coffee system and highlight opportunities to contribute to more diversified food production systems that support biodiversity and ecosystem services.

Analyzing Canopy Height Patterns and Environmental Landscape Drivers in Tropical Forests Using NASA’s GEDI Spaceborne LiDAR

Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA’s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia’s tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests.

An Assessment of the GEDI Lasers’ Capabilities in Detecting Canopy Tops and Their Penetration in a Densely Vegetated, Tropical Area

The Global Ecosystem Dynamics Investigation (GEDI), specifically designed to measure vertical forest structures, has acquired, since April 2019, more than 35 billion waveforms of Earth’s surface on a nearly global scale. GEDI is equipped with 3 identical 1064 nm lasers with a power of 10 mJ per shot, where 1 laser is split into 2 lasers, resulting in two 5 mJ coverage lasers and two 10 mJ full-power lasers. In this study, we evaluate the potential of GEDI’s four lasers to penetrate through canopies and detect the ground, and their capabilities to detect the top of the canopies over a tropical forest (in French Guiana) characterized by a dense canopy cover and tall trees. The accurate detection of both of these surfaces is the first step in characterizing vertical forest structures. The SRTM Digital Elevation Model (DEM) is used as a reference point for elevations while a canopy height model (CHM), derived from airborne and spaceborne LiDAR data, is used as a reference for canopy heights. In addition, the ground and canopy-top elevations estimated from NASA’s Land, Vegetation, and Ice Sensor (LVIS, 1064 nm full-waveform LiDAR, 5 mJ per shot, ~8 km altitude) are used as a benchmark for comparison with GEDI’s lasers. Results indicate that GEDI’s coverage and full-power lasers, even after the application of a preliminary filter that removes around 50% of acquisitions, tend to underestimate tree heights in densely vegetated, tall forests. Moreover, GEDI’s coverage lasers also exhibited a lower level of performance in comparison to both the full-power lasers and LVIS. Overall, the average estimated maximum canopy heights (RH100) for a CHM greater than 30 m was 24.4 m with the coverage lasers, 32.1 m with the full-power lasers, and 36.7 m with LVIS. The analysis of shots with high-beam sensitivity (sensitivity ≥ 98%) showed that they tend to have a better probability of reaching the ground and have better detection of canopy tops for both GEDI laser types. Nonetheless, GEDI’s coverage lasers still showed an underestimation of canopy heights with an average RH100 of 29.8 m, while for GEDI’s full-power lasers and LVIS, the average RH100 was 35.2 m and 37.7 m, respectively. Finally, the assessment of the acquisition time on the detection of the ground return and the top of the canopies showed that, for the coverage lasers, solar noise could affect the detection of the ground return as acquisitions made during early mornings or late afternoons have more penetration than shots acquired between 8 a.m. and 4 p.m. The effect of acquisition time on the detection of the tops of canopies showed that solar noise slightly affected the coverage lasers. Regarding the full-power lasers, the acquisition time of the shots seem to affect neither the penetration of the lasers, nor the detection of the tops of canopies.

Determination of Nutrient Content of Guava Cv. ‘Pant Prabhat’ Affected by Different Canopy Heights and Planting Densities

The study was conducted to determine the changes in nutrient composition of both affected by canopy heights, planting densities, and their interactions in comparison with both rainy and winter seasons. The experiment was composed of 4 different canopy heights [1.25m (H1), 1.5m (H2), 1.75m (H3), and unpruned (H4)] and 3 planting densities [5555 (D1), 3333 (D2), and (D3) 4444 plants ha-1] with total 12 treatment combinations laid out in factorial RBD was conducted. Fruits from the winter season were high in the nutrient content than in the rainy season. Canopy height H1 (1.75 m) recorded the maximum N, P, K, Zn, Cu, Fe and Mn (1.43%, 0.40%, 1.22%, 2.31 ppm, 2.28 ppm, 2.50 ppm and 1.31 ppm) in the winter season while, in the rainy season, maximum (0.88%, 0.35%, 0.98%, 2.07 ppm, 1.92 ppm, 2.14 ppm, 1.05 ppm) canopy height H3 (1.75m). Planting density D2 (3333 plant ha-1) recorded the maximum nutrient content for both seasons. It is therefore concluded canopy height H1 (1.25m) with planting density D2 (3333 plant ha-1) is best for producing fruits with high nutrient content for the winter crops.

ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS

Abstract. Active remote sensing systems orbiting the Earth are only a small portion of the current constellation of satellites and will increase in number and advance in technology in the future. The launch of the GEDI sensor in December 2018, for an expected life-span period of about 2 years, is a fundamental step of this revolution, as it is the first spaceborne full-waveform lidar specifically designed for measuring the structure of ecosystems, providing information of the vertical profile of forests.Accuracy assessment of GEDI height metrics in the context of an Alpine forest environment in steep terrain scenarios has been conducted in this study. We used discrete return lidar from a recent aerial laser scanner survey as reference to analyse differences of heights of terrain elevation and maximum canopy height of the vegetation detected in each GEDI footprint. The height metrics differences between the discrete lidar and the GEDI data were then analysed to verify any correlation with the following factors: morphology (terrain slope), land cover (land cover type, fraction of canopy cover, vegetation density), GEDI laser beam characteristics (day/night-time acquisition, full power vs coverage laser beam, beam ID, laser sensitivity). Further analysis involved shifting the footprints’ location in 8 different direction and 4 distances to assess the impact of geolocation errors on accuracy and precision.Results show that what most influences accuracy in this study is the terrain slope, very likely linked to the uncertainty of geolocation of the GEDI footprints, suggesting caution in using single GEDI footprints if located in steep environments. Other than slope, terrain height accuracy varies mostly with forest type (conifer vs broadleaves), but not significantly with other factors. Canopy height instead is affected by most factors; high vegetation canopy is overestimated by ∼3 m in GEDI, and underestimated by 3 m over heath and bushes (median difference). Higher sensitivity pulses and night-time pulses provide better accuracy. Laser beams with full power also have better accuracy; beams with id 1000 and 1011 provide the most accurate canopy heights. Shifting the footprint position decreased accuracy except at 15 m and 270° with respect to orbit direction (left-looking).

The Shift from Energy to Water Limitation in Local Canopy Height from Temperate to Tropical Forests in China

Canopy height greatly affects the biomass stock, carbon dynamics, and maintenance of biodiversity in forests. Previous research reported that the maximum forest canopy height (Hmax) at global and regional scales could be explained by variations in water or energy availability, that is, the water- or energy-related hypothesis. However, fundamental gaps remain in our understanding of how different drivers (i.e., water and energy) contribute to the Hmax at the local scale. In this study, we selected eight dynamic forest plots (20–30 ha) across a latitudinal gradient (from 21.6° N to 48.1° N) in China and measured the canopy structure using airborne light detection and ranging (LiDAR) data. Based on the LiDAR point cloud data, we extracted the maximum tree height (Hmax) in a 20 × 20 m quadrat as a proxy for canopy height, and the topographic wetness index (TWI) and digital terrain model-derived insolation (DTMI) were calculated as proxies for water and energy conditions. We used a linear mixed model and spatial simultaneous autoregressive error model to quantify how TWI and DTMI contributed to variations in Hmax at the local scale. We found that the positive effect of TWI was stronger in subtropical and tropical forests, highlighting that water was the main factor that drives the canopy height pattern in these regions. In contrast, although the effects of DTMI can be both positive and negative, its relative contribution was higher in temperate forest plots than in other forest types, supporting the idea that energy input is more critical for Hmax in temperate forests. Overall, our study revealed the directional change from energy to water limitation from temperate to subtropical and tropical forests. Our findings can offer important insights into forest management, especially under global climate change in the Anthropocene.

Assessing the structure of coastal forested wetland using field and remote sensing data

Coastal Swamp Oak Forest (CSOF), a supratidal wetland community dominated by Casuarina glauca, is a widely distributed coastal ecosystem along Australia's east coast. These wetland communities are highly valuable for providing ecosystem services, including carbon sequestration. Positioned within the supratidal zone of estuaries – and often abutting upper intertidal saltmarsh and/or mangrove – CSOF may be vulnerable to salinity intrusion and increased tidal inundation due to sea-level rise. To understand spatial patterns of vegetation composition and structure in CSOF, field-based (in-situ) and remote-sensing approaches were employed on the Minnamurra floodplain, New South Wales, Australia. In-situ vegetation surveys within 23 field plots located along seaward to landward transects revealed large variations in mean tree height (2.5–13.1 m) and tree densities (100–8700 trees/ha). Unmanned Aerial Vehicles with Structure from Motion (UAV-SfM), and airborne Light Detection and Ranging (LiDAR) approaches returned mean plot canopy height estimates ranging between 0.1 and 12.8 m. Comparison of vegetation metrics between remote sensors (UAV-SfM and LiDAR) demonstrated similar capacities (R2 values > 0.85) to capture CSOF vegetation height. Comparison of field and spatial metrics elucidated a moderate correlation between the datasets for maximum canopy height (R2 > 0.6) which can be partially explained by the different spatial scales of measurement among these approaches. Canopy height, Normalised Difference Vegetation Index (NDVI), and point density (i.e., vegetation density) estimates were each positively correlated with elevation above mean sea-level. This coincides with indications of plant stress and/or mortality at the seaward edge of CSOF, and in topographic depressions. These findings suggest physico-chemical gradients exert a strong control on CSOF vegetation structure and health, with implications for the current acceleration of sea-level rise. When combined, remote sensing and field-based datasets are useful to characterise and quantify CSOF structure and distribution and can therefore be employed in future assessments of this understudied ecosystem.

Revealing microhabitat requirements of an endangered specialist lizard with LiDAR

A central principle of threatened species management is the requirement for detailed understanding of species habitat requirements. Difficult terrain or cryptic behaviour can, however, make the study of habitat or microhabitat requirements difficult, calling for innovative data collection techniques. We used high-resolution terrestrial LiDAR imaging to develop three-dimensional models of log piles, quantifying the structural characteristics linked with occupancy of an endangered cryptic reptile, the western spiny-tailed skink (Egernia stokesii badia). Inhabited log piles were generally taller with smaller entrance hollows and a wider main log, had more high-hanging branches, fewer low-hanging branches, more mid- and understorey cover, and lower maximum canopy height. Significant characteristics linked with occupancy were longer log piles, an average of three logs, less canopy cover, and the presence of overhanging vegetation, likely relating to colony segregation, thermoregulatory requirements, and foraging opportunities. In addition to optimising translocation site selection, understanding microhabitat specificity of E. s. badia will help inform a range of management objectives, such as targeted monitoring and invasive predator control. There are also diverse opportunities for the application of this technology to a wide variety of future ecological studies and wildlife management initiatives pertaining to a range of cryptic, understudied taxa.