Canopy Parameters Research Articles

Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning

Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that allows the trunk injection of antimicrobials such as oxytetracycline (OTC) for HLB management in Florida. The objectives of this study were to use UAS-based remote sensing to assess the effectiveness of OTC on the HLB-affected citrus trees in Texas and to differentiate the levels of HLB severity and canopy health. We also leveraged UAS-based features, along with machine learning, for HLB severity classification. The results show that UAS-based vegetation indices (VIs) were not sufficiently able to differentiate the effects of OTC treatments of HLB-affected citrus in Texas. Yet, several UAS-based features were able to determine the severity levels of HLB and canopy parameters. Among several UAS-based features, the red-edge chlorophyll index (CI) was outstanding in distinguishing HLB severity levels and canopy color, while canopy cover (CC) was the best indicator in recognizing the different levels of canopy density. For HLB severity classification, a fusion of VIs and textural features (TFs) showed the highest accuracy for all models. Furthermore, random forest and eXtreme gradient boosting were promising algorithms in classifying the levels of HLB severity. Our results highlight the potential of using UAS-based features in assessing the severity of HLB-affected citrus.

A framework for phenotyping rubber trees under intense wind stress using laser scanning and digital twin technology

Rubber trees in coastal habitats are exposed to a high degree of wind stress. An algorithm-hardware synergetic methodology was developed for investigating and predicting rubber tree phenotyping excited by strong winds. The framework includes (1) a custom-designed industrial fan that recreates a variable airflow field at wind speeds of 15, 30 and 45 m/s coupled with a terrestrial laser scanner and bundled motion sensors to acquire point clouds and vibration data; (2) a graphic model that approximates tree canopies based on foliage clumps with phenotypic traits that are derived from point clouds captured while trees are subjected to aerodynamic drag; and (3) the wind characteristic parameters of forest canopies were calculated by a developed forest-specialized k-ε turbulence model combining the constructed tree models and grid-scale subdivision of the wind fluid field. (4) A digital twin model that incorporates detailed tree phenotypic traits and considers plant mechanical characteristics was established, depicting the related wind-induced actions of target trees under various wind influences. The results show that tree crowns with spreading forms are prone to yield larger pendulum amplitudes than compact crowns, but trees directly exposed to wind exhibit greater crown volume reductions than trees in sheltered areas. Within tree canopies, a one-fold increase in inlet wind speed intensified crown compression (approximately 17 % decrease in crown volume), generated 2.1-fold pressure gradients and increased turbulence kinetic energy by approximately 60 %. Moreover, the entire scenario of the adaptation of experimental trees to wind perturbations was visually restored using digital twin techniques, serving as an integral behaviour dataset for further data-driven decision-making. In summary, this paper presents a comprehensive methodology that can decipher the phenotypic manifestations of trees' reactions to wind hazards, with potential applications in phenotyping or envirotyping studies designed to evaluate the wind resistance properties of rubber trees.

Local temperature impact of urban heat mitigation strategy based on WRF integrating urban canopy parameters and local climate zones

Urbanization has led to significant alterations in surface properties, contributing to surging urban heatwave events. The detrimental impact of urban heatwaves on public health highlights the urgency for proactive evaluations of diverse heat mitigation strategies. This study assesses the impacts of cool roof (CR), green roof (GR) and rooftop photovoltaic panel (PV) strategies on urban air temperatures within Guangzhou and Foshan agglomeration, utilizing the Weather Research and Forecasting with Urban Canopy Model (WRF-UCM). A comparative analysis of heat exchanges is conducted between two types of urban morphology datasets: local climate zone maps with categorized urban canopy parameters (LCZ-UCPs) and gridded urban canopy parameters from real building databases (gridded-UCPs). The results indicate that the discrepancies in the average temperature between LCZ-UCP and gridded-UCP is almost negligible, whereas increase to 0.39 °C and 0.54 °C for the daily maximum and minimum temperature, respectively. CR provides the most substantial cooling at an average of 0.44 °C, followed by GR and PV. Scenarios with the two types of morphology datasets reveal varying mitigation efficiencies. CRs with LCZ-UCPs show more pronounced temperature reductions, whereas GRs and PVs with gridded-UCPs in central urban regions demonstrate stronger cooling effects. CRs and PVs cool temperature by decreasing sensible heat flux, whereas GR is largely influenced by enhanced evapotranspiration especially with grass plantation. These findings elucidate the differing efficiencies of the three mitigation strategies and highlight the representation discrepancies in climatic simulations brought by the two urban canopy datasets. This emphasizes the importance of accurate urban morphological datasets in evaluating mitigation strategies in WRF-UCM, thus providing practicable insights for urban planning and climate policy-making.

Urban Canopy Parameters’ Computation and Evaluation in an Indian Context Using Multi-Platform Remote Sensing Data

Urban Canopy Parameters (UCPs) are crucial for urban microclimate modeling; however, the scarce availability of precise UCP data in developing regions limits their application for urban climates. This study investigated the use of multi-platform remote sensing data viz. very high-resolution satellite (VHRS) optical stereo and Unmanned Aerial Vehicle (UAV) datasets for the computation of UCPs in high-density urban scenarios in India, with varied development characteristics. The results demonstrated high accuracy in terms of building height and footprint extraction from both datasets, key inputs for UCP computation. However, UCPs from UAV data have displayed relatively high accuracy for building footprints (86%), building height (RMSE ~ 0.05 m), and land use/land cover classification (90%). Performance evaluation of computed UCPs against a 3D reference geodatabase showed high prediction accuracy for most UCPs, with overall biases, mean absolute error, and root-mean-square error values significantly better than 1 m, with strong correlation (0.8–0.9). It was concluded that VHRS optical stereo and UAV datasets offer a secure, reliable, and accurate solution for UCP computation in urban areas, particularly in developing regions. These findings have significant implications for urban climate research and the sustainable development of rapidly urbanizing areas facing resource and policy constraints.

GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications

We introduce University of Texas - GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 city or locales worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse-resolution urban canopy elevation data with a machine-learning model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters. Validation of mean building heights within 1-km2 grid cells, including data from Hamburg and Sydney, resulted in an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset’s utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort in Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and biometeorological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69–0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49–0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.

OptiSAIL: A system for the simultaneous retrieval of soil, leaf, and canopy parameters and its application to Sentinel-3 Synergy (OLCI+SLSTR) top-of-canopy reflectances

This paper describes the selected algorithm for the ESA climate change initiative vegetation parameters project. Multi- and hyper-spectral, multi-angular, or multi-sensor top-of-canopy reflectance data call for an efficient generic retrieval system which can improve the consistent retrieval of standard canopy parameters as albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and their uncertainties, and exploit the information to retrieve additional parameters (e.g. leaf pigments). We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL (canopy reflectance), PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (soil reflectance anisotropy, moisture effect), and a cloud contamination model. The inversion is gradient based and uses codes created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fAPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. The system is computationally efficient with a rate of 150 pixel s−1 (7 ms per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals and puts real-time processing with this kind of system into reach.

Study on Morphometrical Urban Aerodynamic Roughness Multi-Scale Exploration Using LiDAR Remote Sensing

This study proposes the use of light detection and ranging (LiDAR) remote sensing (RS) to support morphometric research for estimating the aerodynamic roughness length (z0 ) of building placement on various scales. A LiDAR three-dimensional point cloud (3DPC) data processing graphical user interface (GUI) was developed to explore the z0 and related urban canopy parameters (UCPs) in the Incheon metropolitan area in South Korea. The results show that multi-scale urban aerodynamic roughness exploration is viable and can address differences in urban building data at various spatial resolutions. Although validating morphological multi-scale UCPs using dense tall towers is challenging, emerging low-cost and efficient methods can serve as substitutes. However, further efforts are required to link the measured z0 to building form regulations, such as floor area ratio, and expand RS research to obtain more quantitative and qualitative knowledge.

A combined Remote Sensing and GIS-based method for Local Climate Zone mapping using PRISMA and Sentinel-2 imagery

In the last decade, several methods have been developed for Local Climate Zone (LCZ) mapping, encompassing Remote Sensing and Geographic Information Systems (GIS) −based procedures. Combined approaches have also been proposed to compensate for intrinsic limitations that characterized their separate application. Recent work has disclosed the potential of hyperspectral satellite imagery for improving LCZ identification. However, the use of hyperspectral data for LCZ mapping is yet to be fully unfolded. A combined Remote Sensing and GIS-based method for LCZ mapping is proposed to exploit the integration of hyperspectral PRISMA and multispectral Sentinel-2 images with ancillary urban canopy parameter layers. Random Forest algorithm is applied to the feature sets to obtain the LCZ classification. The method is tested on the Metropolitan City of Milan (Italy), for the period from February to August 2023. A spectral separability analysis is carried out to investigate the improvement in LCZ identification using PRISMA in comparison to Sentinel-2 data, as well as improvements in LCZ spectral separability on PRISMA pan-sharpened images. The resulting maps’ quality is evaluated by extracting accuracy metrics and performing inter-comparisons with maps computed from the LCZ Generator benchmark tool. Inter-comparisons yield promising results with a mean Overall Accuracy increase of 16% using PRISMA for each LCZ class. Furthermore, we find that PRISMA improves the detection of LCZs compared to Sentinel-2, with a mean Overall Accuracy increase of 5%, in line with the higher spectral separability of PRISMA spectral signatures computed on the training samples.

Coupling the PROSAIL Model and Machine Learning Approach for Canopy Parameter Estimation of Moso Bamboo Forests from UAV Hyperspectral Data

Parameters such as the leaf area index (LAI), canopy chlorophyll content (CCH), and canopy carotenoid content (CCA) are important indicators for evaluating the ecological functions of forests. Currently, rapidly developing unmanned aerial vehicles (UAV) equipped with hyperspectral technology provide advanced technical means for the real-time dynamic acquisition of regional vegetation canopy parameters. In this study, a hyperspectral sensor mounted on a UAV was used to acquire the data in the study area, and the canopy parameter estimation model of moso bamboo forests (MBF) was developed by combining the PROSAIL radiative transfer model and the machine learning regression algorithm (MLRA), inverted the canopy parameters such as LAI, CCH, and CCA. The method first utilized the extended Fourier amplitude sensitivity test (EFAST) method to optimize the global sensitivity analysis and parameters of the PROSAIL model, and the successive projections algorithm (SPA) was used to screen the characteristic wavebands for the inversion of MBF canopy parameter inversion. Then, the optimized PROSAIL model was used to construct the ‘LAI-CCH-CCA-canopy reflectance’ simulation dataset for the MBF; multilayer perceptron regressor (MLPR), extra tree regressor (ETR), and extreme gradient boosting regressor (XGBR) employed used to construct PROSAIL_MLPR, PROSAIL_ETR, and PROSAIL_XGBR, respectively, as the three hybrid models. Finally, the best hybrid model was selected and used to invert the spatial distribution of the MBF canopy parameters. The following results were obtained: Waveband sensitivity analysis reveals 400–490 and 710–1000 nm as critical for LAI, 540–650 nm for chlorophyll, and 490–540 nm for carotenoids. SPA narrows down the feature bands to 43 for LAI, 19 for CCH, and 9 for CCA. The three constructed hybrid models were able to achieve high-precision inversion of the three parameters of the MBF, the model fitting accuracy of PROSAIL_MLRA reached more than 95%, with lower RMSE values, and the PROSAIL_XGBR model yielded the best fitting results. Our study provides a novel method for the inversion of forest canopy parameters based on UAV hyperspectral data.

Evaluating the role of city representation in modelling the urban precipitation effect of Kuala Lumpur

Urbanization of Earth's land surface is one of the most impactful human interactions on the global ecosystem. Urban land cover substantially alters the transfer of heat, moisture, momentum, and aerosols between the surface and the atmosphere. These surface modifications are the first link in a complex process sequence that causes changes in clouds and precipitation, the so-called urban precipitation effect. Most studies on this topic to date have assumed a homogeneous representation of the city landscape, disregarding the potential effect of its heterogeneity on precipitation. We investigated the impact of Kuala Lumpur's urban precipitation effect through a set of sensitivity studies performed with the Weather Research and Forecasting model, in which different representations of the urban landscape were considered. We showed that including Kuala Lumpur city in the simulations causes a localised increase in accumulated rainfall, rainy day frequency and total extreme rainfall within the boundaries of the urban area. This effect is more pronounced when the city is represented as a homogeneous, high-density landscape compared to the more realistic heterogeneous case, where spatially varying urban canopy parameters are included in the model. Therefore, accurately representing urban areas in climate models is necessary to better estimate the urban precipitation effect.

A universal canopy gap fraction model for forests with various tree distributions based on Nilson's models considering directional overlaps among crowns

An accurate estimation of forest canopy gap fraction (GF) is a prerequisite for remote sensing retrievals of several associated canopy parameters e.g. leaf area index, fAPAR (fraction of photosynthetically active radiation absorbed by the canopy), clumping index, etc. A universal canopy GF model for forests is required for various tree distribution patterns on flat and slopping terrains, yet existing models so far do not possess all these abilities simultaneously. For example, the Neyman type-A model, the Poisson model, and the hypergeometric model are suitable only to forests with clumped, random, and regular tree distributions, respectively. In this regard, the suite of Nilson's canopy GF models developed for forests with various tree distributions have the potential to become universal models. For this purpose, this study attempts to overcome the following limitations of these models to make them be true universally applicable: (1) canopy GF calculated by the Nilson's models is mostly accurate for various tree distributions at nadir; whereas at it is either underestimated for clumped tree distributions or overestimated for regular tree distributions at off-nadir; and (2) Nilson's models were developed for flat terrains and untested for sloping terrains. Herein, two Nilson's models are modified by expanding the calculation of overlaps among crowns (OAC) at nadir to the whole hemispherical space. The results show that all canopy GF simulations in the new models (New91 and New99) show higher consistency with ray-tracing results than those in original Nilson's models in all directions, highlighting the need to consider directional OAC. The modified models (especially for New99) are validated in two RAMI forest stands and a Saihanba forest stand: the relative error in canopy GF between the modified models and a ray-tracing model is less than 10 % in all forest stands. The modified models (especially New99) are suitable for various tree distributions on flat and slopping terrains and therefore increased the universality of the original Nilson's models suitable for a wide-range of remote sensing applications. New99 can be deemed as a universal canopy GF model for forests with various tree distributions.

The influence of stand composition and season on canopy structure and understory light environment in different subtropical montane Pinus massoniana forests.

Canopy structure and understory light have important effects on forest productivity and the growth and distribution of the understory. However, the effects of stand composition and season on canopy structure and understory light environment (ULE) in the subtropical mountain Pinus massoniana forest system are poorly understood. In this study, the natural secondary P. massoniana-Castanopsis eyrei mixed forest (MF) and P. massoniana plantation forest (PF) were investigated. The study utilized Gap Light Analyzer 2.0 software to process photographs, extracting two key canopy parameters, canopy openness (CO) and leaf area index (LAI). Additionally, data on the transmitted direct (Tdir), diffuse (Tdif), and total (Ttot) radiation in the light environment were obtained. Seasonal variations in canopy structure, the ULE, and spatial heterogeneity were analyzed in the two P. massoniana forest stands. The results showed highly significant (P < 0.01) differences in canopy structure and ULE indices among different P. massoniana forest types and seasons. CO and ULE indices (Tdir, Tdif, and Ttot) were significantly lower in the MF than in the PF, while LAI was notably higher in the MF than in the PF. CO was lower in summer than in winter, and both LAI and ULE indices were markedly higher in summer than in winter. In addition, canopy structure and ULE indices varied significantly among different types of P. massoniana stands. The LAI heterogeneity was lower in the MF than in the PF, and Tdir heterogeneity was higher in summer than in winter. Meanwhile, canopy structure and ULE indices were predominantly influenced by structural factors, with spatial correlations at the 10 m scale. Our results revealed that forest type and season were important factors affecting canopy structure, ULE characteristics, and heterogeneity of P. massoniana forests in subtropical mountains.

A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities

Abstract. Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is nowadays seen as a standard approach for classifying any zone according to a set of urban canopy parameters. While many methods already exist to map the LCZ, only few tools are openly and freely available. This paper presents the algorithm implemented in the GeoClimate software to identify the LCZ of any place in the world based on vector data. Six types of information are needed as input: the building footprint, road and rail networks, water, vegetation, and impervious surfaces. First, the territory is partitioned into reference spatial units (RSUs) using the road and rail network, as well as the boundaries of large vegetation and water patches. Then 14 urban canopy parameters are calculated for each RSU. Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets, namely OpenStreetMap (OSM, available worldwide) and the BD TOPO® v2.2 (BDT, a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55 % of all areas have obtained the same LCZ type, with large differences when differentiating this result by city (from 30 % to 82 %). The agreement is good for large patches of forest and water, as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise and open high-rise, mainly due to the height underestimation of OSM buildings located in open areas. Through its simplicity of use, GeoClimate has great potential for new collaboration in the LCZ field. The software (and its source code) used to produce the LCZ data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2022); the scripts and data used for the purpose of this article can be freely accessed at https://doi.org/10.5281/zenodo.7687911 (Bernard et al., 2023) and are based on the R package available at https://doi.org/10.5281/zenodo.7646866 (Gousseff, 2023).

Characterizing the 3-D structure of each building in the conterminous United States

The three-dimensional building structure is crucial for understanding human activities and environmental interactions. Previous studies have estimated building height on a grid-scale, lacking height estimation results concurrently over large areas and at fine resolution. Here we mapped the height of each building in the conterminous US in 2020, using integrated multi-source datasets (i.e., satellite observations and geometry features). Our proposed model for height estimation is proven reliable (i.e., The model's R2 is 0.82, and RMSE is 3.35 m in the conterminous US) and superior in cross-validation with other existing datasets at different scales. We found an explicit pattern of buildings decaying from the urban core to rural areas, which is more noticeable in big cities (e.g., New York). Furthermore, we proposed the seamless and explicit Urban Canopy Parameters (UCPs) datasets, including six UCPs (e.g., building area fraction and height -to-width ratio) based on our height dataset. As key parameters used in climate models, our UCPs improved the fine-grained description of 3-D urban structures compared with those traditionally used. Overall, our building-by-building height dataset and UCP datasets reveal the significant heterogeneity of urban underlying surface, which can help socioeconomic and climatological urban studies.

Evaluation of drought-tolerant varieties based on root system architecture in cotton (Gossypium hirsutum L.)

BackgroundRoot system architecture (RSA) exhibits significant genetic variability and is closely associated with drought tolerance. However, the evaluation of drought-tolerant cotton cultivars based on RSA in the field conditions is still underexplored.ResultsSo, this study conducted a comprehensive analysis of drought tolerance based on physiological and morphological traits (i.e., aboveground and RSA, and yield) within a rain-out shelter, with two water treatments: well-watered (75 ± 5% soil relative water content) and drought stress (50 ± 5% soil relative water content). The results showed that principal component analysis identified six principal components, including highlighting the importance of root traits and canopy parameters in influencing drought tolerance. Moreover, the systematic cluster analysis was used to classify 80 cultivars into 5 categories, including drought-tolerant cultivars, relatively drought-tolerant cultivars, intermediate cultivars, relatively drought-sensitive cultivars, and drought-sensitive cultivars. Further validation of the drought tolerance index showed that the yield drought tolerance index and biomass drought tolerance index of the drought-tolerant cultivars were 8.97 and 5.05 times higher than those of the drought-sensitive cultivars, respectively.ConclusionsThe RSA of drought-tolerant cultivars was characterised by a significant increase in average length-all lateral roots, a significant decrease in average lateral root emergence angle and a moderate root/shoot ratio. In contrast, the drought-sensitive cultivars showed a significant decrease in average length-all lateral roots and a significant increase in both average lateral root emergence angle and root/shoot ratio. It is therefore more comprehensive and accurate to assess field crop drought tolerance by considering root performance.

A study of the impact of landscape heterogeneity on surface energy fluxes in a tropical climate using SUEWS

This study employs an evaluated Surface Urban Energy and Water Balance Scheme (SUEWS) model driven by Local Climate Zone (LCZ)-derived urban canopy parameters to explore landscape heterogeneity's impacts on the partitioning of surface energy fluxes and heat stress variations in Lagos, Nigeria. The results reveal that LCZ-based SUEWS effectively replicates the diurnal patterns of air temperature (Tair), relative humidity (RH), and land surface temperature (LST). The root mean square error (RMSE) for simulated Tair and RH ranges from 0.4 °C to 1.2 °C and 1.8% to 8.0%, respectively. While a nighttime warm bias in LST is observed, it reduces during the daytime. Significant spatial variability in turbulent heat fluxes and LST among LCZs is noted, with core urban LCZs experiencing notable increases in sensible heat flux and LST, while suburban LCZs exhibit higher latent heat flux values. Anthropogenic heat flux peaks in high compact low-rise LCZ 3, reaching a maximum of 95 W/m2. Remarkably, no significant difference is found in the diurnal heat stress cycle among LCZs, despite consistently elevated heat stress levels in high compact LCZs. These findings offer valuable insights into quantifying surface energy flux variabilities and spatio-temporal heat stress patterns in a densely populated tropical city- Lagos Nigeria.

The synergistic effects of urbanization and an extreme heatwave event on urban thermal environment in Paris

In late July 2019, an extreme heatwave swept across the European regions. This study focused on examining the collective impacts of urbanization and this extreme heatwave on Paris's thermal environment. To achieve this, high-resolution simulations were performed using the urbanized version of the Weather Research and Forecasting (WRF) model. The inclusion of enhanced urban canopy parameters and improved spatial resolutions significantly improved the near-surface meteorological variables and the spatial-temporal patterns of the urban heat island in Paris. The impervious surface sensitivity study revealed that during the full-day sunlight conditions and low wind scenarios that characterized the extreme heatwave, the urban underlayer contributed to a 0.4 °C rise in daytime temperature and 1 °C increase during nighttime hours in the downtown area. Among the various representative Local Climate Zones (LCZ) types in Paris, namely compact mid-rise, large low-rise, and sparsely built, the contributions to temperature increase were about 0.3 °C (0.7 °C), 0.3 °C (0.5 °C), and 0.3 °C (0.6 °C) for daytime (nighttime) temperatures, respectively. The surface energy budget analysis further indicated that the augmented heat storage within urban impervious surfaces is one of the main causes of the enhanced urban heat island effect, particularly during the nighttime periods during heatwaves.

GIS-based revision of a WUDAPT Local Climate Zones map of Bern, Switzerland

Urban areas are particularly affected by heatwaves through the intensification of heat stress by the urban heat island effect. For effective climate change adaptation, information about microscale surface cover, structures, and human activity in cities is needed to depict the underlying causes of urban heat stress. The framework of “Local Climate Zones” (LCZs) classifies and standardizes urban areas based on such characteristics. To date, most LCZ mapping workflows use satellite imagery as input. The resulting maps may lack some important details, and thus benefit from the use of additional geodata. We introduce a novel approach that combines the geodata of urban canopy parameters with the remote sensing-based LCZ map of Bern, Switzerland. City-specific urban canopy parameters are calculated and used to adjust established value ranges, if necessary. The most common misclassification patterns are identified and misclassified pixels are corrected using a decision tree and k-nearest-neighbor algorithm. Results show that the conformity with the urban canopy parameter values markedly increased, especially in the distinction of water surfaces, non-built areas, and building height. However, for high-resolution LCZ maps, this also leads to unnecessary heterogeneity, which may require further postprocessing. Given sufficiently available urban canopy parameter data, the proposed workflow is simple and easily adaptable for other cities. It could prove useful in urban climate studies and city planning to enhance an existing LCZ map in a contextualized manner quickly.

Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method

Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction of low-quality photons exists in ICESAT-2/ATL08 products, which restricts the performance of regional canopy height estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed in this study, which efficiently filtered the unreliable canopy photons in ATL08, constructed an extrapolation model by combining multiple remote sensing data, and finally mapped the 30 m forest canopy height of Hunan Province in 2020. To verify the feasibility of this method, the canopy parameters were also filtered based on ATL08 product attributes (traditional method), and the accuracy of the two models was compared using the 10-fold cross-validation. The conclusions were as follows: (1) compared with the traditional model, the overall accuracy of the LNR-LGB model was approximately doubled, in which R2 increased from 0.46 to 0.65 and RMSE decreased from 6.11 m to 3.48 m; (2) the forest height in Hunan Province ranged from 2.53 to 50.79 m with an average value of 18.34 m. The LNR-LGB method will provide a new concept for achieving high-accuracy mapping of regional forest height.