Vegetation Canopy Research Articles

Spectral Characteristics of the Dynamic World Land Cover Classification

The Dynamic World product is a discrete land cover classification of Sentinel 2 reflectance imagery that is global in extent, retrospective to 2015, and updated continuously in near real time. The classifier is trained on a stratified random sample of 20,000 hand-labeled 5 × 5 km Sentinel 2 tiles spanning 14 biomes globally. Since the training data are based on visual interpretation of image composites by both expert and non-expert annotators, without explicit spectral properties specified in the class definitions, the spectral characteristics of the classes are not obvious. The objective of this study is to quantify the physical distinctions among the land cover classes by characterizing the spectral properties of the range of reflectance present within each of the Dynamic World classes over a variety of landscapes. This is achieved by comparing both the eight-class probability feature space (excluding snow) and the maximum probability class assignment (label) distributions to continuous land cover fraction estimates derived from a globally standardized spectral mixture model. Standardized substrate, vegetation, and dark (SVD) endmembers are used to unmix nine Sentinel 2 reflectance tiles from nine spectral diversity hotspots for comparison between the SVD land cover fraction continua and the Dynamic World class probability continua and class assignments. The variance partition for the class probability feature spaces indicates that eight of these nine hotspots are effectively five-dimensional to 95% of variance. Class probability feature spaces of the hotspots all show a tetrahedral form with probability continua spanning multiple classes. Comparison of SVD land cover fraction distributions with maximum probability class assignments (labels) and probability feature space distributions reveal a clear distinction between (1) physically and spectrally heterogeneous biomes characterized by continuous gradations in vegetation density, substrate albedo, and structural shadow fractions, and (2) more homogeneous biomes characterized by closed canopy vegetation (forest) or negligible vegetation cover (e.g., desert, water). Due to the ubiquity of spectrally heterogeneous biomes worldwide, the class probability feature space adds considerable value to the Dynamic World maximum probability class labels by offering users the opportunity to depict inherently gradational heterogeneous landscapes otherwise not generally offered with other discrete thematic classifications.

Potential of important vegetation in Vatulemo Green Open Space

Green Open Space is a space that is fully arranged with various plants. The expansion of green open space in Palu City is designed to meet the minimum minimum area of greeneries at 30% of the city area. Landscape areas with different types of vegetation have different abilities to provide thermal comfort. This research aims to analyze the vegetation structure and the ability of vegetation to reduce air temperature in Vatulemo's green open space. This research method are analysis of the vegetation structure in Vatulemo City Park's green open space based on the types of vegetation components that comprise the green open space and exploratory analysis of the ability of vegetation with the highest importance value (IVI) to reduce air temperatures that provide thermal comfort for various living things. The results showed a codominance between the vegetation was found, for instance, Cuphea sp, Samanea saman, Cynodon dactylon, Terminalia mantaly and Tabernaemontana divaricata. The average temperature under and outside the canopy of the vegetation was quite different. The temperature under the canopy is lower than outside the canopy. The difference in surface temperature under and outside the canopy is influenced by the primary factor of shading arising from the thickness of each vegetation's canopy. As a result, according to SNI 03-6572-2001, temperature under canopy vegetation with the highest importance are classified as comfortable warm standard temperature. In Vatulemo Park, planting canopy trees remains extremely scarce. Each vegetation has a different ability to create thermal comfort.

Hydrodynamics affected by submerged vegetation with different flexibility under unidirectional flow

Submerged vegetation changes the hydrodynamic characteristics of rivers, lakes, wetlands, and coastal zones. However, only a few studies have focused on the effect of flexible submerged vegetation on hydrodynamic characteristics under unidirectional flow. Therefore, laboratory experiments were conducted to study the effects of submerged vegetation with different flexibility on the flow structure and turbulence characteristics under unidirectional flow. The results showed that the reconfiguration and coordination of wave motion of flexible submerged vegetation redistribute flow velocity, Reynolds stress, and turbulent kinetic energy inside and outside of the vegetation canopy. With a gradual decrease in the deflection height of vegetation, the differences in dimensionless velocity, dimensionless mixed layer thickness, bulk drag coefficient, averaged turbulent kinetic energy, and the averaged contribution rate of its shear production term for the vegetation canopy also decrease; the trend of the penetration depth of Reynolds stress is opposite. Based on the turbulent kinetic energy budget equation, a turbulent kinetic energy model (TKE model) was established, which can be used to predict the turbulent kinetic energy and its shear production term within the vegetation canopy. Here, the scaling factor was determined by the vegetation canopy Cauchy number. The TKE model can be applied under unidirectional flow conditions for submerged vegetation with different flexibilities with high accuracy. It is a simple method to predict vegetation-induced turbulence and the characteristics of sediment and material transport under the influence of submerged vegetation with different flexibility.

Pola Keterkaitan Perubahan Distribusi Kepadatan Vegetasi dengan Penggunaan Lahan DAS Pesisir, Case Study : DAS Garang

<p><em>The diversity and density of vegetation is key in managing sedimentation throughout the watershed, especially in the central and downstream regions. Measures of vegetation diversity and density that can be used are the Normalized Difference Vegetation Index (NDVI) which is a measure of vegetation greenness (chlorophyll levels), Normalized Difference Water Index (NDVI) to measure vegetation wetness levels, and soil adjusted vegetation index (SAVI) for low canopy vegetation cover. For watersheds that stretch long to downstream or coastal areas, the measure of land diversity needs to be supplemented by using the Normalized Difference Built-Up Index (NDBI) indicator that is useful for land use planning. Information on this vegetation index can be obtained by processing a satellite imagery map. The benefits of processing vegetation indexes in watershed management are becoming increasingly important in the era of climate change, especially with regard to efforts to harmonization of relationships between environmental elements that include the diversity of the interests of the resident population. That's why this research was done in order to find the link between natural factors including the impacts of climate change and humans. The test began by looking for changes in the NDVI, NDWI, SAVI, and NDBI indexes from the last 5 years with the Global Indicator Spatial Association (GISA) analysis using Moran's I Global Index, followedby looking for the association with the pattern of population change and the percentage of the area of the settlement. The results showed a link between moran's I Global index change pattern of population change and the spread of residential developments. The tendency of the distribution of this settlement area is an important point in analyzing the influence of its dispersal patterns in a watershed so that the continued impact of potential erosion and sedimentation triggered will be the main consideration in watershed management.</em></p>

Impacts of rapid urbanization on spatial dynamics of land use–based carbon emission and surface temperature changes in the Semarang Metropolitan Region, Indonesia

This study explores the relationship between carbon emission patterns and the land surface temperature (LST) changes due to the rapid urbanization in the Semarang Metropolitan Region (SMR), an Indonesian area that has experienced rapid urban growth compared to other urban areas. This research used the stock-difference and gain-loss methods to calculate carbon stocks and emissions. Then, band 6 on Landsat 5 TM (2008) and band 10 on Landsat 8 OLI (2013 and 2018) were used to calculate the LST changes. These results showed that the peri-urban area had a more significant change. The correlation between carbon emissions and an increased SMR temperature correlates to 0.646. This shows that the carbon emissions pattern promotes temperature dynamics in the SMR. Furthermore, this study proved the release of carbon emissions in line with LST dynamics spatially. In this case, this study proved that rapid urbanization in the SMR promotes both carbon emission and LST. Those changes are also affected by vegetation canopy availability and other activities. As a result, the government must prioritize spatial planning in the SMR to mitigate environmental change risk. In addition, the government must develop novel strategies to deal with a wide range of fast and unpredictable potential changes in the urban area and its surroundings.

Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth

Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread, and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over past decadal timescales. Therefore, our objectives are: (1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; (2) to test different model structures to estimate LFMC from VOD; and (3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations, but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance in estimating LFMC. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status, drought conditions, and fire dynamics.

Impact of Vegetation Gradient and Land Cover Conditions on Soil Moisture Retrievals From Different Frequencies and Acquisition Times of AMSR2

Space-borne remote sensing provides great potential for soil moisture (SM) retrieval and emerged as a significant data source for research in land surface dynamics and associated applications. This study compared the error characteristics of SM estimates retrieved from the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board NASA’s Aqua satellite across different vegetation gradients and land cover conditions, at different overpass times and frequencies, to demonstrate their strengths and limitations. Results demonstrate that AMSR2 C-band products outperform AMSR2 X-band products over moderately and densely vegetated conditions due to lower attenuation by the vegetation canopy. Conversely, X-band products performed better than C-band products in barren lands possibly due to uneven sensing depth and microwave emissions from subsurface in C-band. The daytime products have a higher signal-to-noise ratio (SNR) in sparsely and moderately vegetated areas, whereas nighttime products have a higher SNR in densely vegetated areas. When these products are used selectively based on their error characteristics, the probability of obtaining SM with stronger signal than noise can be significantly improved (95%) at the expense of impaired spatial coverage (70%-pixel loss).

Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models

The effective and accurate aboveground biomass (AGB) estimation facilitates evaluating crop growth and site-specific crop management. Considering that rice accumulates AGB mainly through green leaf photosynthesis, we proposed the photosynthetic accumulation model (PAM) and its simplified version and compared them for estimating AGB. These methods estimate the AGB of various rice cultivars throughout the growing season by integrating vegetation index (VI) and canopy height based on images acquired by unmanned aerial vehicles (UAV). The results indicated that the correlation of VI and AGB was weak for the whole growing season of rice and the accuracy of the height model was also limited for the whole growing season. In comparison with the NDVI-based rice AGB estimation model in 2019 data (R2 = 0.03, RMSE = 603.33 g/m2) and canopy height (R2 = 0.79, RMSE = 283.33 g/m2), the PAM calculated by NDVI and canopy height could provide a better estimate of AGB of rice (R2 = 0.95, RMSE = 136.81 g/m2). Then, based on the time-series analysis of the accumulative model, a simplified photosynthetic accumulation model (SPAM) was proposed that only needs limited observations to achieve R2 above 0.8. The PAM and SPAM models built by using 2 years of samples successfully predicted the third year of samples and also demonstrated the robustness and generalization ability of the models. In conclusion, these methods can be easily and efficiently applied to the UAV estimation of rice AGB over the entire growing season, which has great potential to serve for large-scale field management and also for breeding.

Tree sapling vitality and recovery following the unprecedented 2018 drought in central Europe

BackgroundOngoing climate change is anticipated to increase the frequency and intensity of drought events, thereby affecting forest recovery dynamics and elevating tree mortality. The drought of 2018, with its exceptional intensity and duration, had a significant adverse impact on tree species throughout Central Europe. However, our understanding of the resistance to and recovery of young trees from drought stress remains limited. Here, we examined the recovery patterns of native deciduous tree sapling species following the 2018 drought, and explored the impact of soil depth, understory vegetation, and litter cover on this recovery. MethodsA total of 1,149 saplings of seven deciduous tree species were monitored in the understory of old-growth forests in Northern Bavaria, Central Germany. The vitality of the saplings was recorded from 2018 to 2021 on 170 plots. ResultsFagus sylvatica was the most drought-resistant species, followed by Betula pendula, Acer pseudoplatanus, Quercus spp., Corylus avellana, Carpinus betulus, and Sorbus aucuparia. Although the drought conditions persisted one year later, all species recovered significantly from the 2018 drought, albeit with a slight decrease in vitality by 2021. In 2018, the drought exhibited a more pronounced adverse effect on saplings in deciduous forests compared to mixed and coniferous forests. Conversely, sapling recovery in coniferous and mixed forests exceeded that observed in deciduous forests in 2019. The pivotal factors influencing sapling resilience to drought were forest types, soil depth, and understory vegetation, whereas litter and forest canopy cover had a negative impact. ConclusionLong-term responses of tree species to drought can be best discerned through continuous health monitoring. These findings demonstrate the natural regeneration potential of deciduous species in the context of climate change. Selective tree species planting, soil management practices, and promoting understory diversity should be considered when implementing adaptive management strategies to enhance forest resilience to drought events.

Genetic diversity based on Principal Component and cluster analysis for various characters in spring wheat genotypes under drought condition

Genetic diversity plays an important function in the improvement of germplasm which has a direct association with the crop productivity. A number of statistical methods have been employed to investigate genetic diversity among the genotypes of various crops. Approaches like principal component and cluster analysis are useful and most frequently used for identifying plant characters individually and assisting breeders in genetically enhancing attributes in wheat genotypes. This research was carried out at the experimental field of On-farm Research Division (OFRD), Bangladesh Agricultural Research Institute (BARI), Shyampur, Rajshahi, Bangladesh, to study the genetic diversity and selection of high yielding wheat genotypes with their important agronomic and physiological traits among studied genotypes in drought condition by using principal component and cluster analyses. A total of 70 bread wheat genotypes were evaluated in 7 × 10 alpha lattice design in non-irrigated drought conditions during 2018-2019 cropping season. The first four principal components (PCs) with eigen values greater than 1.0 accounting for 82.81% of the total observed variation among genotypes. Traits with maximum values in PC1 were spikes m-2 (SPM), thousand grain weight (TGW), ground coverage (GC), normalized difference vegetation index (NDVI), grain yield (GY), biomass (BM), and harvest index (HI) while PC2 comprised heading days (HD) and BM. The major contributors to PC3 were grains spike-1 (GPS) and GC, whereas the maximum value of trait in PC4 was in relative leaf water content (RWC). The principal component biplot selected 21 high yielding genotypes than the average yield as they were distributed on the positive side of the PC1. The cluster analysis grouped 70 genotypes into six diverse clusters. Cluster Ⅱ containing same 21 genotypes previously selected by principal component biplot provided the highest SPM (257.4), GPS (42.2), TGW (40.51 g), GC (0.27), NDVI(0.73), SPAD (44.24), RWC (88.33%), grain yield (3216kg ha-1), BM (8535 Kg ha-1) and HI (0.37) belonging to the lowest canopy temperature at vegetative stage (16.14˚C) and canopy temperature at grain filling stage (24.64˚C) and moderate HD (71.65 days). Based on the results of the current study the best genotypes can be used as important breeding materials in upcoming breeding schemes for drought tolerance.

Abstract C092: Proximity to green space and inflammation among breast cancer patients

Abstract It is important to establish if exposure to green space provides a protective effect for breast cancer development and progression to advocate for expanded access to green space areas for 1) those at-risk and 2) cancer patients receiving treatment. There is an association of residential proximity to green space with decreased breast cancer incidence. Current studies have not evaluated how patient variables may affect this relationship. We sought to determine if patient-level green space proximity correlated to social determinants of health such as poverty or minority status, or patient physiologic or inflammatory molecular markers in breast cancer patients. A retrospective analysis of baseline demographic data (including addresses) and cytokine profiles from breast cancer patients who had enrolled on investigator initiated trials at our institution was performed. National Land Cover Database and Philadelphia Parks & Recreation data was used to identify green space indicators such as patient proximity and neighborhood density of green space and tree canopy and was correlated to the patient’s systemic inflammation. Pearson coefficients were performed to identify relationships between green space indicators and inflammation. Patients were accrued to investigator initiated trials between 2013-2019. All had a diagnosis of ductal carcinoma in situ or invasive breast cancer. The median age of the women on the study was 63 years old, 42% were black, and 64% were considered of low socioeconomic status. The fraction of total area within a 250-meter radius of each patient’s address that has low vegetation or tree canopy was calculated. More women with above the median percentage of low vegetation within 250 meters of their residence had elevated levels of the anti-inflammatory cytokine TGF-B (p <0.001). In addition, living with above the median percentage of low vegetation within 250 meters of their residence was associated with having higher levels of IGF-BP3 (p <0.001). In our diverse patient population who experience adverse social determinants of health, an inverse correlation was noted between residential proximity to greenspace indicators and inflammation. These results could be used to advocate for expanded access to green space, especially among those with lowest resources, as increased inflammation is associated with breast cancer incidence and progression. Citation Format: Taylor Micua, Joseph Lombardo, Alex M DesJarlais, Benjamin C. Keating, Melissa Lazar, Charnita Zeigler-Johnson, Adeseye Adekeye, Edith Mitchell, Russell K. McIntire, Nicole L. Simone. Proximity to green space and inflammation among breast cancer patients [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C092.

Effects of Seed Treatments and Yield Potential on Infected Soybean Seed

Diaporthe seed decay is an endemic disease throughout most soybean-growing regions. A major outbreak of Diaporthe seed decay occurred in 2018 in Iowa and other parts of the United States. The most prevalent fungi associated with this outbreak in Iowa were Diaporthe longicolla and D. caulivora. We established field experiments in 2019 and 2020 in two central Iowa locations to evaluate the effects of seed treatments and yield potential of Diaporthe-infected seed lots. Seed of the same variety but originating from different sources were categorized as “good” or “bad” seed depending on the percentage of Diaporthe-infected seed. Four seed treatments were tested, and results indicated that there were differences in plant stand, plant health (normalized difference vegetation index and canopy coverage) (both years), and yield (2019) between both seed lots. The good seed lot had about 9% greater yield than the bad seed lot ( P < 0.001), whereas the seed lots had similar yield in 2020 ( P = 0.822). Seed treatments improved stand, canopy greenness, and canopy coverage but did not affect yield in either year. For the yield potential experiment, there were minimal differences in plant health and no differences in yield between good and bad seed lots thinned to the same plant population in both years. These results suggest that Diaporthe-infected seed lots with lower germination and plant stand resulted in lower yield, and seed treatments can improve soybean stand, canopy greenness, and coverage. Additionally, Diaporthe-infected seed did not affect soybean growth and development or yield once plant stand was established.

Reevaluating the Surface Energy Balance and Soil Thermal Diffusion Equations in the Noah Multi-Parameterization (Noah-MP) Scheme

The Noah multi-parameterization (Noah-MP) scheme is one of several land surface schemes implemented in the Weather Research and Forecasting (WRF) model. Our simulations by the WRF model with the Noah-MP scheme show that surface air temperatures in the morning and evening tend to be higher and lower, respectively, than observed, and that the temperatures at an urban station with snow cover increase little from 0 ℃ even in the daytime. The former depends on surface energy balance in the skin layer and the latter results from snow cover assumed to be uniform over a grid cell. These weaknesses are improved by considering the partial transmission of the solar radiation through the skin layer to the soil layer, the heat capacity of the vegetation canopy, and a mixture of soil layers with and without snow cover. The present scheme will contribute to an improvement of the Noah-MP scheme.

Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes

To improve the simulation accuracy of vegetation canopy reflectance in optical bands, the Radiosity Applicable to Porous IndiviDual objects (RAPID) model has been upgraded to better deal with branches in the latest RAPID4. Previous versions of RAPID (RAPID1 and RAPID3) neglected branches in porous objects in optical bands, while RAPID2 emphasized them in microwave bands. This inconsistency needed to be addressed to establish a unified radiosity-based simulation framework. By incorporating branches in RAPID4, we have improved several aspects of the model, including the random dynamic projection process, the equivalent reflectance or transmittance, the single scattering estimation, the multiple scattering solution, and the bidirectional reflectance factor (BRF) calculation. Three-dimensional trees from the fifth RAdiation transfer Model Intercomparison (RAMI-V) have been used to test the contribution of branches on BRF. Comparisons with a ray-tracing-based LESS model (the LargE-Scale remote sensing data and image Simulation framework) on RAMI-V scenes show a general agreement on BRF ( R 2 ≥ 0.96 and root mean square error ranging from 0.014 to 0.054). The major biases occur in a realistic scene (i.e., HET51_WWO_TLS) created from terrestrial laser scanning data. Sensitivity analysis has been conducted to show the branch contribution on BRF in optical domain. Without considering dense branches, the BRF error can exceed 0.1.

Optimizing the Protocol of Near-Surface Remote Sensing Experiments Over Heterogeneous Canopy Using DART Simulated Images

Optical canopy models that connect land surface properties and satellite-observed radiance must be validated before being used. These models include the bidirectional reflectance distribution function (BRDF) models in the visible and near-infrared domains, and directional brightness temperature (DBT) models in the thermal infrared domain. Near-surface experiments have been extensively conducted to evaluate the modeling accuracy, including ground-, tower-, and aircraft-based measurements. Indeed, it should be noted that in situ measured BRDF/DBT results are sensitive to the experiment protocol, such as sensor moving orientation, flight height, and sampling frequency. A practical tool for optimizing the in situ measurement protocols is needed in the community of remote sensing modeling. For that, we devised a virtual experiment framework based on the discrete anisotropic radiative transfer (DART) 3-D radiative transfer model that is capable of simultaneously simulating both the BRDF/DBT pattern and the images acquired by in situ cameras. Here, as an optimization case, we use it to determine the optimal sensor flight orientation over heterogeneous vegetated canopies (a row-planted scene with three solar angles and a discrete scene with three solar angles) for measuring their DBT distribution. Results showed considerable errors (i.e., image-extracted DBT minus DART-simulated DBT) exist for sensor flight orientation along the canopy rows ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> = 0.24 and root mean square error (RMSE) = 4.32 K), and they become much smaller ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> = 0.94 ~ 0.98 and RMSE = 0.82 ~ 1.03 K) in other typical orientations (e.g., cross row plane, solar principal plane, and cross solar principal plane). The critical azimuth offset relative to the row direction that can ensure an acceptable RMSE < 1 K is quantified as atan(3*Unitwidth/Scenesize) based on a series of intensive simulations by this new tool. However, the RMSE of the discrete scene is not sensitive to the flight orientation. Such accuracy differences in various protocols were experimentally verified over row-planted maize using a 4-D tower in Huailai, Hebei, China. The result highlights the great potential of this newly designed DART-based virtual experiment to optimize near-surface experiment protocols.

A Method for Retrieving Coarse-Resolution Leaf Area Index for Mixed Biomes Using a Mixed-Pixel Correction Factor

The leaf area index (LAI) is a key structural parameter of vegetation canopies. Accordingly, several moderate-resolution global LAI products have been produced and widely used in the field of remote sensing. However, the accuracy of the current moderate-resolution global LAI products cannot satisfy the requirements recommended by the LAI application communities, especially in heterogeneous areas composed of mixed land cover types. In this study, we propose a mixed-pixel correction (MPC) method to improve the accuracy of LAI retrievals over heterogeneous areas by considering the influence of heterogeneity caused by the mixture of different biome types with the help of high-resolution land cover maps. The DART-simulated LAI, the aggregated Landsat LAI, and the site-based high-resolution LAI reference maps are used to evaluate the performance of the MPC method. The results indicate that the MPC method can reduce the influences of spatial heterogeneity and biome misclassification to obtain the LAI with much better accuracy than the Moderate Resolution Imaging Spectroradiometer (MODIS) main algorithm, given that the high-resolution land cover map is accurate. The root mean square error (RMSE) (bias) decreases from 0.749 (0.486) to 0.414 (0.087), while the R2 increases from 0.084 to 0.524, and the proportion of pixels that fulfill the uncertainty requirement of the GCOS increases from 38.2% to 84.6% for the results of site-based high-resolution LAI reference maps. Spatially explicit information about vegetation fractional cover can further reduce uncertainties induced by variations in canopy density for the results of DART simulated data. The proposed method shows potential for improving global moderate-resolution LAI products.

Evidence of NOx and O3 concentration reduction by kudzu (Pueraria lobata) invasion at a Japanese highway

Generally, urban green infrastructure aids in the mitigation of air pollution. However, little is known about the role of densely packed vegetation canopies, such as Pueraria lobata (Willd.) Ohwi (kudzu) in air pollution mitigation. Here, we conducted field measurements of tropospheric ozone (O3) and nitrogen oxides (NOx) concentrations on a highway embankment slope with and without kudzu canopies in Japan. We measured O3 and NOx concentrations above and within the kudzu canopies by using a passive sampler several times during the growing season in 2019. The results demonstrated that both O3 and NOx concentrations reduced within the kudzu canopy throughout the growing season, suggesting that kudzu generally acts as “vegetation barrier” for both gas pollutants along the highway roadside. Depending on meteorological and vegetative surface conditions, the reduction rate within the canopy ranged from 11% to 64% and from −2% to 33% for O3 and NOx concentrations, respectively. Considering lower canopy height but higher leaf area density of kudzu than those for urban tree plantations or forest patches along the roadside, kudzu can be useful as green infrastructure depending on situations. We also found that the higher the leaf area index of kudzu canopies, the higher the reduction rates of O3 and NOx. Depending on the maintenance cost for roadside tree planting, the minimal management without targeting the eradication of kudzu over a certain area can be an option for realizing green infrastructure at least for maintaining local air quality during the growing season.

Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests

Vegetation optical depth (VOD) is seasonally sensitive to plant water content and aboveground biomass. This index has a strong penetrability within the vegetation canopy and is less impacted by atmosphere aerosol contamination effects, clouds and sun illumination than optical vegetation indices. VOD is thus increasingly applied in ecological applications, e.g., carbon stock, phenology and vegetation monitoring. However, VOD retrieval over dense forests is subject to uncertainties caused by the thick canopy and complex multiple scattering effects. Thus, a comprehensive evaluation of VOD products over dense forests is needed for effective and accurate applications. This study evaluated the seasonal variations of eight recently developed/reprocessed VOD products at different frequencies (e.g., Ku-, X-, C- and L-band) over Amazon intact forests, supported by the ORCHIDEE-CAN-NHA model-simulated vegetation water content. Furthermore, we also explored the potential causes of VOD retrieval issues, in terms of retrieval algorithm uncertainties.We first confirmed that soil water availability dominated seasonal dynamics of vegetation water content over Amazon intact forests. This was verified by model-simulated vegetation water content and by C-band radar backscatter observations. Generally, evening or midday vegetation water content shows higher correlations with soil moisture than morning or midnight vegetation water content. In terms of ability of morning or midnight VOD products to follow the seasonality of soil moisture, active microwave ASCAT-IB C-VOD (median seasonal correlation with soil moisture (R) ∼ 0.50) outperforms the passive microwave VOD products, followed by passive microwave AMSR2 X-VOD (R ∼ 0.26) and VODCA X-VOD (R ∼ 0.16). However, SMOS-IC L-VOD (R ∼ −0.15) and AMSR2 C1-VOD (R ∼ −0.20) show obviously negative seasonal correlations with soil moisture across most pixels. This implausible behavior is likely to be caused by the inappropriate setting of time-invariant scattering effects in the passive microwave VOD retrieval algorithms, which could lead to an overestimation of the VOD amplitude during dry seasons. Thus, we recommend that the seasonal scattering effects be considered in the passive microwave VOD retrieval algorithms. These findings can contribute to the improvement of VOD retrieval algorithms and help with the development of their ecological applications over Amazon dense forests.

Flow velocity evolution through a floating rigid cylinder array under unidirectional flow

Floating canopies with unanchored roots are often distributed in streams and wetlands, which alters the evolution of the flow velocity inside the canopy interiors. Based on the flow continuity equation and layer-averaged momentum equations, an analytical model was proposed to predict the velocity evolution through floating canopies under unidirectional flow. Laboratory experiments showed that the velocity evolution within the canopy interior was impacted by the mean channel velocity, canopy density and ratio of the height of the free flow region below the floating canopy to the flow depth. Inside a floating canopy, the flow adjustment distance, XD, was defined as the distance from the canopy leading edge to the position at which the velocity decreased to a constant value. A new estimator of XD was proposed and validated for floating canopies. The shear parameter (C = 0.076 ± 0.025) was determined and used to characterize the vertical momentum exchange between the in-canopy and below-canopy flows. The velocities of the proposed model predicted using the new estimator (XD) and shear parameter (C) were in good agreement with fourteen groups of measurements from different sources. The vegetation rigidity and flexibility had a weak influence on the flow velocity evolution inside the floating canopy. This indicated that the proposed model was capable of accurately predicting the velocity evolution through floating canopies of vegetation. In addition, the proposed model can be applied to predict the velocity evolution inside the root zones of floating vegetation islands (FVIs). Compared to that of the drag of the root zones, the influence of the drag of the floating island on the velocity evolution inside the root zones was negligible.

Spatial capture-recapture and LiDAR-derived vegetation metrics reveal high densities of ocelots on Texas ranchlands

Reliable estimates of population density and size are crucial to wildlife conservation, particularly in the context of the Endangered Species Act. In the United States, ocelots (Leopardus pardalis pardalis) were listed as endangered in 1982, and to date, only one population density estimate has been reported in Texas. In this study, we integrated vegetation metrics derived from LiDAR and spatial capture-recapture models to discern factors of ocelot encounter rates and estimated localized population estimates on private ranchlands in coastal southern Texas. From September 2020 to May 2021, we conducted a camera trap study across 42 camera stations on the East Foundation’s El Sauz Ranch, which was positioned within a larger region of highly suitable woody and herbaceous cover for ocelots. We observed a high density of ocelots (17.6 ocelots/100 km2) and a population size of 36.3 ocelots (95% CI: 26.1–58.6) with the 206.25 km2 state space area of habitat. The encounter probability of ocelots increased with greater canopy cover at 1-2 m height and decreasing proximity to woody cover. These results suggest that the incorporation of LiDAR-derived vegetative canopy metrics allowed us to understand where ocelots are likely to be detected, which may aid in current and future population monitoring efforts. These population estimates reflect the first spatially explicit and most recent estimates in a portion of the northernmost population of ocelots in southern Texas. This study further demonstrates the importance of private working lands for the recovery of ocelots in Texas.