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African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAI W ) and herbaceous LAI (LAI H ), respectively, derived from aggregate leaf area index (LAI A ), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAI A ); and (2) partitioned biomass model (LAI P ) comprising LAI H and LAI W . By integrating forage estimates with ancillary environmental variables, we found that LAI P , with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAI P yielded the lowest RMSE (5.9 TLUs) and higher R 2 (0.83), surpassing NDVI and LAI A models RMSE (9.3 TLUs) and R 2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAI P has the potential to reduce basis risk by enhancing IBLI index accuracy.

Carbon (C) uptake rates in many forests are sustained, or decline only briefly, following disturbances that partially defoliate the canopy. The mechanisms supporting such functional resistance to moderate forest disturbance are largely unknown. We used a large-scale experiment, in which >6700 Populus (aspen) and Betula (birch) trees were stem-girdled within a 39-ha area, to identify mechanisms sustaining C uptake through partial canopy defoliation. The Forest Accelerated Succession Experiment in northern Michigan, USA, employs a suite of C-cycling measurements within paired treatment and control meteorological flux tower footprints. We found that enhancement of canopy light-use efficiency and maintenance of light absorption maintained net ecosystem production (NEP) and aboveground wood net primary production (NPP) when leaf-area index (LAI) of the treatment forest temporarily declined by nearly half its maximum value. In the year following peak defoliation, redistribution of nitrogen (N) in the treatment forest from senescent early successional aspen and birch to non-girdled later successional species facilitated the recovery of total LAI to pre-disturbance levels. Sustained canopy physiological competency following disturbance coincided with a downward shift in maximum canopy height, indicating that compensatory photosynthetic C uptake by undisturbed, later successional subdominant and subcanopy vegetation supported C-uptake resistance to disturbance. These findings have implications for ecosystem management and modeling, demonstrating that forests may tolerate considerable leaf-area losses without diminishing rates of C uptake. We conclude that the resistance of C uptake to moderate disturbance depends not only on replacement of lost leaf area, but also on rapid compensatory photosynthetic C uptake during defoliation by emerging later successional species.

In the present study, an attempt was made to produce a precise 3D image of a tomato canopy using a portable high-resolution scanning lidar. The tomato canopy was scanned by the lidar from three positions surrounding it. Through the scanning, the point cloud data of the canopy were obtained and they were co-registered. Then, points corresponding to leaves were extracted and converted into polygon images. From the polygon images, leaf areas were accurately estimated with a mean absolute percent error of 4.6%. Vertical profile of leaf area density (LAD) and leaf area index (LAI) could be also estimated by summing up each leaf area derived from the polygon images. Leaf inclination angle could be also estimated from the 3-D polygon image. It was shown that leaf inclination angles had different values at each part of a leaf.

A field-based experiment was conducted to know the relevance of potassium solubilizing bacteria (KSB), and Osmo-priming mediated morphological changes and yielded recovery in green gram (Vigna radiata L.) under water-limiting conditions. Hence, the experiment was carried out at the research farm of Lovely Professional University. The characters like plant height, number of leaves, leaf area plant-1, and LAI were considered to track the morphological changes, while the primary branches, nodules, pods plant-1, seeds pod-1, the average length of the pod, test weight, biological yield, grain yield, and harvest index (HI) were used to determine the recovery of yield as compared to control. Among the treatments, T8 was recorded as one of the best treatments for all the morphological parameters studied, i.e., plant height (51.80 cm), number of leaves (42 plant-1), leaf area (577.27 cm2.plant-1) and LAI (1.92) while most of the yield contributing characters were found better in T6 i.e. nodules (8.3 plant-1), seeds pod-1 (10) and length of the pod (7.65 cm) except for the primary branches and the number of pods plant-1 which was remain recorded maximum in T8 (6.0 and 22). The yield of green gram and its biological yield were recorded as highest in T6 and T2 (6.83 and 24.23 g.plant-1), while HI and test weight were also noted in T6 (32.0% and 5.90 g). This study has concluded that the KSB, combined with KNO3, showed a strong potential to modify the morphological structure while the yield of green gram was in KSB + Ca(NO3)2 under water scarcity.

Understory trees occupy a spatially heterogeneous light environment owing to light interception by patchily distributed canopy leaves. We examined the spatial distribution of canopy leaves and the spatial structure of the understory tree community (height < 5 m) and their relationships in a beech forest in Nagano, Japan. We measured the canopy leaf area index (LAI) at 10 m intervals (n = 81) in a permanent research plot (1 ha). We established a circular subplot centered on each LAI measurement point, and determined the species composition and the aboveground net primary production of wood (ANPPW) of the understory tree community by using tree size data from an open database in the Monitoring Sites 1000 project. There was a significant negative correlation between canopy LAI and the ANPPW of understory trees and a significant positive correlation between the ANPPW of understory and understory tree density. The dominant species of understory trees differed between subplots with high and low LAI values. Our results suggest that niche differentiation allows trees in the understory community to make use of various light conditions, thereby enhancing the primary productivity of the entire community.

How plants respond to climate change is of major concern, as plants will strongly impact future ecosystem functioning, food production and climate. Here, we investigated how vegetation structure and functioning may be influenced by predicted increases in annual temperatures and atmospheric CO₂ concentration, and modeled the extent to which local plant–plant interactions may modify these effects. A canopy model was developed, which calculates photosynthesis as a function of light, nitrogen, temperature, CO₂ and water availability, and considers different degrees of light competition between neighboring plants through canopy mixing; soybean (Glycine max) was used as a reference system. The model predicts increased net photosynthesis and reduced stomatal conductance and transpiration under atmospheric CO₂ increase. When CO₂ elevation is combined with warming, photosynthesis is increased more, but transpiration is reduced less. Intriguingly, when competition is considered, the optimal response shifts to producing larger leaf areas, but with lower stomatal conductance and associated vegetation transpiration than when competition is not considered. Furthermore, only when competition is considered are the predicted effects of elevated CO₂ on leaf area index (LAI) well within the range of observed effects obtained by Free air CO₂ enrichment (FACE) experiments. Together, our results illustrate how competition between plants may modify vegetation responses to climate change.

Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

The Leaf Area Index (LAI) is an ecophysiology key parameter characterising the canopy-atmosphere interface where most of the energy fluxes are exchanged. However, producing maps for managing the spatial and temporal variability of LAI in large croplands with traditional techniques is typically laborious and expensive. The objective of this paper is to evaluate the reliability of LAI estimation by processing dense 3D point clouds as a cost-effective alternative to traditional LAI assessments. This would allow for high resolution, extensive and fast mapping of the index, even in hilly and not easily accessible regions. In this setting, the 3D point clouds were generated from UAV-based multispectral imagery and processed by using an innovative methodology presented here. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for canopy thickness, height and leaf density distribution along the wall. For the validation of the estimated LAI, an experiment was conducted in a vineyard in Piedmont: the leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images. The vineyard LAI estimated by the proposed methodology showed to be correlated with the ones obtained by the traditional manual method. Indeed, the obtained R2 value of 0.82 can be considered fully adequate, compatible to the accuracy of the reference LAI manual measurement.

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R2) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth.

Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R 2 ) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.