Ground Biomass Research Articles

The Effect of Biochar on Tomato (Solanum lycopersicum) Cultivar Micro-Tom Grown under Continuous Light

AbstractContinuous lighting (CL) has the potential to increase crop yield in greenhouse production. Tomato plants, however, when exposed to CL develop inter-vascular chlorosis, a leaf injury which causes a reduction in chlorophyll content and necrosis. The application of biochar can reduce physiological stress in plants, we examine if biochar also reduces necrosis in tomatoes when grown under CL. Faecal sludge biochar was applied to an acidic soil to examine plant growth and yield in Micro-Tom tomato plants grown under continuous light. We examined soil and plant growth properties of three soil application treatments: a control soil, biochar treatment (4%w/w) (Biochar), and a combined biochar (2% w/w) and fertilizer (2% w/w) treatment (Biochar + Fert). Faecal sludge biochar addition produced plant heights 216% greater than control and above ground biomass 583% greater than control. The biochar and fertilizer treatment group produced a 487% increase in leaf number compared to biochar. The combined biochar and fertilizer treatment produced a 398% increase in dried above ground biomass and a 177% increase in dried fruit yield compared with biochar. Plants in the biochar and fertilizer treatment group showed less visual evidence of continuous light induced leaf injury.Biochar addition did not limit continuous light induced leaf chlorosis whereas combined biochar and fertilizer treatment resulted in a significant reduction in leaf injury and death. Overall, the application of biochar and biochar and fertilizer combined increased crop yield.

A Theoretical Framework to Quantify Ecosystem Pressure-Volume Relationships.

'Water potential' is the biophysically relevant measure of water status in vegetation relating to stomatal, canopy and hydraulic conductance, as well as mortality thresholds; yet, this cannot be directly related to measured and modelled fluxes of water at plot- to landscape-scale without understanding its relationship with 'water content'. The capacity for detecting vegetation water content via microwave remote sensing further increases the need to understand the link between water content and ecosystem function. In this review, we explore how the fundamental measures of water status, water potential and water content are linked at ecosystem-scale drawing on the existing theory of pressure-volume (PV) relationships. We define and evaluate the concept and limitations of applying PV relationships to ecosystems where the quantity of water can vary on short timescales with respect to plant water status, and over longer timescales and over larger areas due to structural changes in vegetation. As a proof of concept, plot-scale aboveground vegetation PV curves were generated from equilibrium (e.g., predawn) water potentials and water content of the above ground biomass of nine plots, including tropical rainforest, savanna, temperate forest, and a long-term Amazonian rainforest drought experiment. Initial findings suggest that the stored water and ecosystem capacitance scale linearly with biomass across diverse systems, while the relative values of ecosystem hydraulic capacitance and physiologically accessible water storage do not vary systematically with biomass. The bottom-up scaling approach to ecosystem water relations identified the need to characterise the distribution of water potentials within a community and also revealed the relevance of community-level plant tissue fractions to ecosystem water relations. We believe that this theory will be instrumental in linking our detailed understanding of biophysical processes at tissue-scale to the scale at which land surface models operate and at which tower-based, airborne and satellite remote sensing can provide information.

Long‐Term Impacts of Selective Logging in a Tropical Rainforest in the East Region of Cameroon

ABSTRACTAlthough selective logging has become the primary cause of degradation in many African countries, little is known about its long‐term effects. This study investigated the long‐term effects of selective logging on forest tree species diversity and dynamics in the East Region of Cameroon. Six permanent monitoring plots of 1 ha each in unlogged and logged forests were established in 2005 and a recensus in 2011. Each 1 ha plot was divided into 25, 20 × 20 m quadrats. Each 20 × 20 m quadrat was further divided into 16, 5 × 5 m subquadrats, where the diameter at breast height (DBH) of all trees ≥ 2 cm was measured. There was a decrease in plant species density and richness in all forest types. Sloetiopsis usambarensis was the most important species in the unlogged forests and forests logged 21 years ago. The most important family was Putrangivaceae, Euphorbiaceae and Violaceae in the unlogged forests and forests logged 11 and 21 years ago, respectively. The frequency distribution of stem size classes indicated a reversed J‐shape of tropical forests. The above ground biomass (AGB) recovered more than 50% in all forests, and the unlogged forest had the highest AGB (961.8 Mg/ha). Larger stems indicated a forest at a stage of recovery from disturbance. Silvicultural management should be considered.

The Study of Post-Harvest Processing and Handling of Residues From Plants Grown Primarily For Agronomics: Soybean Stalk, Corn Stover, Tomato Vine, Cucumber, Eggplant, and Summer Squash

Information on post-harvest handling of the crop is critical to the development of new or improved plant species and traits. This paper presents a comprehensive study of the grinding characteristics and handling properties of a number of crop residues under agronomic studies. We used a laboratory-scale knife mill connected to an in-line power meter to investigate the specific energy of size reduction for each crop. The summer squash sample yielded the smallest mean particle size upon grinding (P= 0.05). The results indicate a significant correlation between the Carbon to Oxygen (C/O) ratio and the Gross Calorific Value (GCV), ash, lignin content, and net specific grinding energy consumption (NSGEC) of the samples. Among agricultural residues, the soybean stalk sample, with the highest C/O ratio (0.96), exhibited the highest GCV (17.5 MJ/kg, db) and NSGEC (31.7 kWh/t), while the summer squash sample, with the lowest C/O ratio (0.46), showed the lowest GCV (13.6 MJ/kg, db) and NSGEC (5.1 kWh/t). The flowability of the ground biomass samples varied, with cucumber showing the best free flow properties. The results also showed that there is a significant positive correlation between the lignin content and NSGEC of all samples (p= 0.05).

Improving AGB estimations by integrating tree height and crown radius from multisource remote sensing.

Precise estimation of forest above ground biomass (AGB) is essential for assessing its ecological functions and determining forest carbon stocks. It is difficult to directly obtain diameter at breast height (DBH) based on remote sensing imagery. Therefore, it is crucial to accurately estimate the AGB with features extracted directly from RS. This paper demonstrates the feasibility of estimating AGB from crown radius (R) and tree height (H) features extracted from multi-source RS data. Accurate information on tree height (H), crown radius (R), and diameter at breast height (DBH) can be obtained through point clouds generated by airborne laser scanning (ALS) and terrestrial laser scanning (TLS), respectively. Nine allometric growth equations were used to fit coniferous forests (Larix principis-rupprechtii) and broadleaf forests (Fraxinus chinensis and Sophora japonica). The fitting performance of models constructed using only "H" or "R" was compared with that of models constructed using both combined. The results showed that the quadratic polynomial model constructed with "H+R" fitted the AGB estimation better in each vegetation type, especially in the scenario of mixed tall and short coniferous forests, in which the R2 and RMSE were 0.9282 and 25.30 kg (rRMSE 17.31%), respectively. Therefore, using high-resolution data to extract crown radius and tree height can achieve high-precision, global-scale estimation of forest above ground biomass.

Evaluating Carbon Sequestration and Biodiversity in Cocoa Agroforestry Systems: A Case Study from the Man Region, Côte d'Ivoire

Aims: Cocoa-based agroforestry systems in the Man region of Côte d'Ivoire are essential for balancing agricultural productivity and environmental preservation. This study assessed these systems regarding plant biodiversity conservation and carbon sequestration. Study Design and Methodology: The research was carried out in 21 cocoa plantations, aged between 14 and 32 years, across 60 botanical inventory plots, each measuring 10 by 20 meters. Tree heights and diameters were recorded to estimate Above Ground Biomass (AGB) using the equation AGB = 0.0673 × (ρD²H)0.976, where D is diameter, H is height, and ρ is specific density. Below Ground Biomass (BGB) was calculated as BGB=AGB×0.24, while total biomass (BT) was obtained as BT=AGB+BGB. Carbon stock (C) was derived using C=BT×0.5. Statistical analysis was applied to compare mean carbon stocks across different plantation ages. Results: The plantations, ranging from 8 to 32 years old, showed a diversity of companion species. The diversity of companion species in cocoa agrosystems varies from 4 in 8–10-year-old plantations to 12 in 14- and 15-year-old plantations. Carbon sequestration was age-dependent: younger plantations (5-10 years) sequestered 19.77 ± 2.32 tonnes per hectare, plantations aged 10-15 years stored 133.70 ± 253.50 tonnes, while middle-aged plots (15-20 years) reached 398.76 ± 861.51 tonnes. The oldest plantations (30-35 years) sequestered 137.94 ± 280.31 tonnes. Effective plantation management increased both carbon sequestration and biodiversity. Conclusion: The study highlights the importance of species diversity and continuous management in cocoa-based agroforestry systems. Optimized agroecological practices enhance biodiversity conservation and carbon sequestration, providing practical insights for stakeholders involved in agricultural sustainability, biodiversity conservation, and climate change mitigation. Cocoa agroforestry aligns economic and environmental objectives, offering a sustainable agricultural model.

Effect of nitrogen fertilizer on growth, yield and quality parameters of three sugarcane (<em>Saccharum Officinarum</em> L.) varieties

This study examines the effects of nitrogen on the growth, yield and quality of three sugarcane varieties under irrigated conditions in the low country dry zone of Sri Lanka. Three sugarcane varieties (SL96-128, SL04-624 and SL8306) were tested at four nitrogen fertilizer levels (0, 100, 200, 300 kg/ha) in a split plot design with four replicates. Selected growth, yield and quality parameters were studied in this field experiment. The results of the study demonstrated that application of 100kg/ha or 300kg/ha N fertilizer has shown highest girth of stem (cm), partitioning biomass to millable stalk (PBMS), brix and pol percentage in all three varieties, SL04-624, SL8306 and SL96-128. In addition, variety SL04-624 have shown highest total above ground biomass production (dry matter) (41987.8kg/ha), partitioning biomass to millable stalk (PBMS) (74.5%), brix (19.4%), pol (17.1%) and pure obtainable cane sugar (POCS) (12.3%). Therefore 100k/ha (N2) could be concluded as the best rate of N fertilizer application for the varieties tested in this study considering the cost of production, environmental and groundwater pollution. SL04-624 could be recommended as the most suitable variety for low country wet zone of Sri Lanka responding to 100kg/ha N fertilizer.

To Assess the Toxicity of Nickel on Above and Below Ground Biomass of Raphanus Sativus Variety Pusa Chetki in Natural Conditions (Pot Culture Experiments)

To Assess the Toxicity of Nickel on Above and Below Ground Biomass of Raphanus Sativus Variety Pusa Chetki in Natural Conditions (Pot Culture Experiments)

Two Years of Cotton (Gossypium hirsutum L.) Data from the Georgia Coastal Plain, USA

The sustainable management of Earth’s complex ecosystems requires an abundance of field data to support long term stewardship. Remotely sensed satellite data provide crucial supplements to field measurements and are essential for deriving key operational products for monitoring Earth systems. However, to accurately calibrate and validate the models used to develop monitoring datasets, coincident field measurements are required. In 2018 and 2019, data related to cotton (Gossypium hirsutum L.) crops were collected from five fields in two farms located in Georgia, USA. Collections were timed to coincide with satellite overpasses to support the development of remote sensing-based crop and soil data products. Data collected include soil moisture, plant water content, above ground biomass, crop height, plant phenology, and field management practices (row direction, row spacing, and plant density). The datasets include 512 records collected in 2018 and 303 records collected in 2019. The data are archived in the National Agricultural Library Ag Data Commons repository and are available for use by researchers seeking crop and soil validation data.

UAV or satellites? How to find the balance between efficiency and accuracy in above ground biomass estimation of artificial young coniferous forest?

The accurate estimation of Above Ground Biomass (AGB) is the basis for plantation forest carbon trading. This study focused on Picea crassifolia artificial plantations, extracting individual tree crown diameters and heights using Unmanned Aerial Vehicles (UAV) data and calculating the individual tree biomass using allometric growth equations. These results were then used to train a satellite image AGB prediction model. In additional, satellite images were resampled to different resolutions to assess the impact of satellite image resolution on model the accuracy. Finally, the model with the highest accuracy among the deep learning algorithms was selected to predicts the AGB within the P. crassifolia plantation forest. The results indicated that the accuracy of single tree crown diameters extracted from P. crassifolia point clouds significantly surpassed those extracted from general point clouds and Crown Height Model (CHM), while the accuracy of the heights extracted from all three sources was similar; RepLKNet outperformed GoogLeNet and ResNet in identifying plantation forest; random forest slightly outperformed XGBoost in the capability of AGB prediction, while the accuracy of the AGB prediction models initially increasd and then decreasd with satellite image resolution, reaching the highest accuracy at a resolution of 50 m. This indicates that the optimal satellite image resolution for estimating the AGB in the study area was affected by scale effects of 50 m. Compared with the combination of satellite data and manual field measurements, the concurrent use of UAVs and satellites offers significant advantages in terms of efficiency and accuracy. UAVs can replace manual sampling for carbon sequestration transactions for plantations.

Estimation of carbon stocks and CO2 emissions resulting from the forest destruction in West Kalimantan, Indonesia

The issue of climate change has become a global concern, and tropical regions are strategically positioned to mitigate its effects through their forest resources. By maintaining the function of forest ecosystems to support life at local and regional levels, carbon stocks can be maintained, and emissions can be reduced. Above-ground biomass can bind and store carbon stocks. This study aims to analyse the CO2 emissions caused by deforestation and forest degradation in Kubu Raya District, West Kalimantan Province. Primary data was obtained through field observations, and secondary data was obtained through maps of forest areas and land cover for 1996–2021 from the Geospatial Information Agency and the Indonesian Ministry of Environment and Forestry. This study uses a non-destructive approach to calculate Above Ground Biomass. The results showed that Kubu Raya District, West Kalimantan Province, Indonesia, experienced deforestation of 235,597 ha and forest degradation of 9,860 ha from 1990 to 2021. This resulted in CO2 emissions of 163,702,815.53 tons. The highest level of CO2 emissions is in Batu Ampar District, and the lowest is in Sungai Kakap District. There is a positive correlation between the amount of deforestation and forest degradation and the level of CO2 emissions caused by these activities. This means that the amount of CO2 emissions depends on the area's deforestation and forest degradation. To reduce the concentration of CO2 in the atmosphere and its effects on climate change, deforestation and forest degradation activities in Kubu Raya Kalimantan should be stopped.

Evaluating Flow Characteristics of Ground and Cut Biomass for Industrial Applications

Evaluating Flow Characteristics of Ground and Cut Biomass for Industrial Applications

Combining 2D image and point cloud deep learning to predict wheat above ground biomass

Combining 2D image and point cloud deep learning to predict wheat above ground biomass

Integrating NDVI and agronomic data to optimize the variable-rate nitrogen fertilization

The success of Variable Rate Application (VRA) techniques is closely linked to the algorithm used to calculate the different fertilizer rates. In this study, we proposed an algorithm based on the integration between some estimated agronomic inputs and crop radiometric data acquired by using a multispectral sensor. Generally, VRA algorithms are evaluated by comparing the yields, but they can often be affected by factors acting in the final phase of the crop cycle and not dependent on the fertilization treatments. Therefore, we decided to compare our algorithm (ALG) versus the traditional application of fertilizer (TRD) by evaluating the crop growth 1.5 months after the fertilization time. The algorithm was tested on a sorghum crop under organic farming, managed with or without manure. The saving of N obtained with ALG was equal to 14 and 5 kg ha− 1 (-14 and − 10% for the non-manure and fertilized treatments, respectively). The NDVI values acquired after fertilization showed a remarkable reduction of relative standard deviation for ALG system (from 22 to 9% and from 34 to 14% for manured and not manured, respectively), which was not found for TRD system (from 16 to 17% and from 29 to 18% for manured and not manured, respectively). The above ground biomass produced was statistically equivalent for the two systems in the manured plots and significant higher for ALG in not-manured plots (+ 0.74 t ha− 1 of dm, equal to + 23%). Finally, the indices calculated to evaluate the Nitrogen Use Efficiency (NUE) were consistently better in the ALG theses.

Evaluation of GEDI footprint level biomass models in Southern African Savannas using airborne LiDAR and field measurements

Savannas cover more than 20% of the Earth and account for the third largest stock of global aboveground biomass yet estimates of their above ground biomass density (AGBD) are very inaccurate. The Global Ecosystem Dynamic Investigation (GEDI) sensor provides near-global full-waveform LiDAR data with 25 m footprints, from which various structural metrics are derived that are used to predict footprint level AGBD. The current GEDI L4A AGBD product uses a comprehensive Forest Structure and Biomass Database (FSBD) to develop models for specific plant functional types and geographic regions, but southern African savannas have been underrepresented in the reference data. The objectives of this study were to (i) validate GEDI L4A AGBD in South African savannas using field measurements and ALS datasets and (ii) develop and evaluate local GEDI footprint-level AGBD estimates from multiple L2A and L2B metrics. The local GEDI AGBD models outperformed GEDI L4A AGBD (R2 = 0.42, RMSE = 12 Mg/ha, %RMSE = 79.5%) with higher R2 and smaller error measures. The local GEDI AGBD using a random forest model (RF) had the highest R2 of 0.71 and lowest %RMSE of 53.3%, while the generalized linear model (GLM) results provided the lowest Relative Mean Systematic Deviation (RMSD) of 9.2%, which was half that of RF model. L4A significantly underestimated AGBD with an RMSD up to −37%. This highlights the importance and benefits of local calibration of biomass models to unlock the full potential of GEDI metrics for estimating AGBD. The field and ALS data have subsequently been contributed to the GEDI FSBD and should be used in calibration of future versions of GEDI L4A AGBD product. This research paves the way for the integration of the local GEDI AGBD estimates with other sensors, notable the eminent NISAR mission, to derive regional to global gridded AGBD products that will enable the monitoring of savanna carbon stocks.

Effect of Sowing Date on Some Agronomical Characteristics of Rye Cultivars in Iraq

The introduction of rye cultivation in Iraq necessitates the implementation of agrotechnological experiments. Two-year irrigated field experiments were carried out in Al-Muthanna Governorate (in the southwestern region of Iraq) in 2021/2022 and 2022/2023 to evaluate the performance of three European rye cultivars introduced to Iraq, focusing on the most significant agronomical and morphological characteristics. Three sowing dates (01 November, 15 November and 01 December) were tested in a split plot, randomized complete block design. We observed that both the cultivar and sowing date, but not the crop year, influenced the studied characteristics. In general, the early sowing dates enhanced the growth and development of rye and resulted in a higher yield compared to the later sowing dates. We observed that all evaluated rye cultivars can be grown safely in the agroclimatic and soil characteristics of this region. The grain yield was 3.1, 4.2 and 6.9 t ha−1 on average for all the sowing dates, and the above ground biomass results were 13.6, 12.0 and 22.9 on average for all sowing dates in ‘Krzyca’, ‘Dańkowskie złote’ and ‘Horyzo’, respectively. In addition, the highest grain yield (8.8 t ha−1) was harvested in ‘Horyzo’ when it was sown on 01 November; thus, we recommend choosing ‘Horyzo’ for cultivation in Iraq and sowing it in early November. Although further study is required to improve agro-technology (such as the nutrient supply) by using a larger number of cultivars, we can conclude that rye can be grown safely in Iraq under irrigated conditions.

Community structure, species diversity and biomass of trees in Tranum Forest Reserve, Raub, Pahang

This study was conducted at the Tranum Forest Reserve, Raub, Pahang to determine the community structure, species diversity and biomass of trees in the area. Four study plots, each measuring 25 m × 20 m, were established at the study site, which covered a total area of 0.2 ha. All trees with diameter at breast height (DBH) of 5 cm and above were measured and recorded. A total of 175 trees was enumerated in the study plots, comprising 106 species and 79 genera from 40 families. The largest family was Lauraceae, represented by 11 species from six genera, followed by Myrtaceae with eight species from one genus. Tree species diversity of the study area was high, as reflected by a Shannon Diversity Index (H’) value of 4.49 (Hmax = 4.66). Margalef Richness Index (R’) and Shannon Evenness Index (E) showed values of 20.33 and 0.84, respectively. The most important family, based on the highest Family Important Value Index (FIVi), was Dipterocarpaceae, with a value 17.31%. Shorea platyclados was the most important species with Important Value Index (IVi) of 17.04%. The tree density of the study area was 875 indidivuals per hectare (ind/ha), with Lauraceae being the densest family at 85 ind/ha. Vitex gamosepala was the densest species at 40 ind/ha. The total basal area of trees was 66.23 m2/ha, with Dipterocarpaceae showed the highest basal area of 31.69 m2/ha and Shorea platyclados had the highest basal area of 31.69 m2/ha at species level. The total biomass of trees was estimated at 1,307.56 t/ha, of which the above ground biomass and the below ground biomass were 1,091.33 t/ha and 216.23 t/ha, respectively. Dipterocarpaceae contributed the highest biomass with an estimate of 743.83 t/ha and Shorea platyclados showed the highest biomass at species level with 743.83 t/ha.

Effect of Nitrogen Fertilizer and Seed rates on Yield and Yield Components of Bread Wheat in the Irrigated Condition of South West Shewa, Central Highland of Ethiopia

Inappropriate seed density and fertilizer management can lead to unstable crop yields. Excessive fertilizer application can potentially cause yield loss and nitrogen (N) leaching that leads to environmental pollution. The aim of this study was to explore the optimal N application rate and seed rate on bread wheat with different nitrogen responding under irrigation condition at two experimental sites in the South West Shewa, Ethiopia. A year field experiment was conducted to explore the effects of five N application rates (N0, N23, N46, N69, and N92) and three seed rates on bread wheat yield components like; - aboveground biomass, harvest index, number of tillers per plant, spike length, number of kernels per plant, grain yield and net return. The results showed that N application rate and seed rate were significantly interaction (P< 0.05) effect on aboveground biomass, harvest index, number of tillers per plant, spike length, number of kernels per plant and grain yield. Generally, spike length, number of tillers per plant and number of kernels per spike of wheat were increased with increased at some level in nitrogen fertilizer rate for the three seed rate at both locations. Highest number of kernels per spike (42.00, 44.00) and the highest mean above ground biomass yield (13.70 t ha<sup>-1</sup>, 7.5 t ha<sup>-1</sup>) were obtained for 150 kg ha <sup>-1</sup> seed rate with 69 N kg ha<sup>-1</sup> application at Ameya and Woliso sites respectively. The highest net benefit of 171531.88 EB ha<sup>-1</sup> with marginal rate of return of 351.14% was obtained from 175 kg ha<sup>-1</sup> seed rate with application of 69 kg N ha<sup>-1</sup>. Therefore, 175 kg ha<sup>-1</sup> seed rate with application of 69 kg N ha<sup>-1</sup> is economical feasible for bread wheat production at Ameya area and also the highest net benefit of 51675 EB ha<sup>-1</sup> with marginal rate of return of 115.22% was obtained from 150 kg ha <sup>-1</sup> seed rate with application of 69 kg N ha<sup>-1</sup> at Woliso area.

Machine learning and fluorosensing for estimation of maize nitrogen status at early growth-stages

Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluorescence channels and indices as predictive variables for estimating plant nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) often fails to deliver a good range of prediction accuracies. Hence, machine learning regression techniques are utilized in this study to estimate N status indicators i.e., %N, above ground biomass, and N uptake at V6 and V9 growth stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex®3 (FORCE-A) portable active fluorescence system was used to capture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlorophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as predictors. The independent site data were first utilized in a Support Vector Regression (SVR) model to assess the training and test accuracies in estimation of N status indicators considering a comparative analysis between V6 and V9 growth stages. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performance in a cross-site experimental setting. Subsequently, cross-site comparisons of nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regression, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random Forest Regression, and (5) Artificial Neural Network. The comparative investigation demonstrated promising accuracy in estimating plant nitrogen content, above-ground biomass, and nitrogen uptake at the V6 stages of maize, with correlation coefficients in the moderate range (r = 0.72 ± 0.03) and Root Mean Square Error. Superior prediction accuracies were obtained at V9 growth stages than at V6. Among the various machine learning models assessed, Support Vector Regression consistently outperformed the others across the three site-year datasets, delivering error estimates well within acceptable ranges and demonstrating the lowest RMSE values for maize nitrogen indicators.

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.