Abstract: The modeling and predicting of vegetation Leafareaindex (LAI) is an extremely important indication factor for growth status of vegetation. However, the time series of MODIS LAI include linear and nonlinear components, which cannot be accurately modeled and predicted by either linear method or nonlinear method. In this paper, SARIMA, BP neural network and a hybrid method of SARIMA-BP neural network were used for modeling and predicting MODIS LAI time series. The SARIMA-BP neural network combined both SARIMA and BP neural network, of which could model the linear component and the nonlinear component of MODIS LAI time series respectively. Thus the final result of SARIMA-BP neural network was the sum of results of the two methods. Experiments showed that the proposed SARIMA-BP neural network method performed the best in comparison with SARIMA and BP neural network，implying its well adaption to the LAI time series.
Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leafareaindex (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photo- synthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relation- ship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI > 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley.
A new airborne method is presented here for leafareaindex estimation. The basic technique is the same as used in hemispherical photo analysis [10,12]. The difference is just that the background is the snow covered terrain instead of the sky. This naturally limits the use of the technique to the areas with annual snow cover. In addition, the method is not directly suited for broadleaved canopies, which are usually without leaves at the time of the snow cover. Boreal forests are typically dominated by coniferous species and the snow covered season is mostly long. Therefore the method is well suited for LAI estimation of boreal forest and especially useful in the northernmost regions, where the roads are sparse and it is difficult to access the forests scattered between wetlands.
A method of growth analysis was used to evaluate the yield results in experiments conducted during years 19992001 on School co-operative farm in abèice. In sequential terms of sampling from two potato varieties with different duration of growing season, the effect of leafareaindex (L, LAI), on yield of tubers in soils contaminated by cadmium, arsine and beryllium, was evaluated. From a growers view the phytotoxic influence on development of assimilatory apparatus and yields during the growth of a very-early variety Rosara and a medium-early Korela were evaluated. These varieties were grown under field conditions in soils contaminated by graded levels of cadmium, arsenic and beryllium. The yields of tubers were positively influenced by duration of growing season and increased of leafareaindex during three experimental years. On the contrary, graded levels of heavy metals had negative influence on both chosen varieties. The highest phyto- toxic influence was recorded of arsine and the lowest of cadmium. Significant influence of arsenic and beryllium on size of leafareaindex in the highest applied variants was found. The influence of experimental years on tuber yields was also statistically significant.
the suspension was randomly collected and after ﬁltrating through a medium hard ﬁlter the mass of the surface runoﬀ was determined. The sediment together with the ﬁlter were dried at a temperature of 105°C, then cooled in a desiccator and weighed on an electronic analytical balance with accuracy up to 0.0001 g. The obtained result was reduced by the mass of a dry ﬁlter. The above-ground parts of the test plants were measured using apparatus for measuring the leafareaindex, manufactured by the Sun Scan Canopy Analysis System company. Measurements of the surface runoﬀ mass involved
Abstract: Remote and non-destructive estimation of leafareaindex (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.
In the present scenario, various scientific methodologies and advanced procedures are undergoing significant development to achieve an increase in crop yield and productivity,thereby reducing manual labour. Certain techniques involve the analysis and measurement of biological factors such as Tidal Volume, Germination and Survival Percentage, Plant height etc. This project proposes to measure the LeafAreaIndex for estimating the plant growth and to provide an analysis of the plant quality to check if the plant is in a good condition or not. Direct and manual measurements of LAI prove to be time-sensitive and laborious. The use of sophisticated instruments such as LAInet and MODIS prove expensive 7 . To overcome these limiting factors, a simple, cost
The leafareaindex (LAI) is an important vegetation parameter, which is used widely in many applications. Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies. During the last two decades, hyperspectral remote sensing has been employed increasingly for crop LAI estimation, which requires unique technical procedures compared with conventional multispectral data, such as denoising and dimension reduction. Thus, we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing tech- niques. First, we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation. Second, we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types: approaches based on statistical models, physical models (i.e., canopy reflectance models), and hybrid inversions. We summarize and evaluate the theoretical basis and different methods employed by these approaches (e.g., the char- acteristic parameters of LAI, regression methods for constructing statistical predictive models, commonly applied physical models, and inversion strategies for physical models). Thus, numerous models and inversion strategies are organized in a clear conceptual framework. Moreover, we highlight the technical difficulties that may hinder crop LAI estimation, such as the “curse of dimensionality” and the ill-posed problem. Finally, we discuss the prospects for future research based on the previous studies described in this review.
The seasonal patterns of green leafareaindex (GLAI) can be used to assess crop physiological and phenological sta- tus, to assess yield potential, and to incorporate in crop simulation models. This study focused on examining the po- tential capabilities and limitations of satellite data retrieved from the moderate resolution imaging spectroradiometer (MODIS) 8- and 16-day composite products to quantitatively estimate GLAI over maize (Zea mays L.) fields. Results, based on the nine years of data used in this study, indicated a wide variability of temporal resolution obtained from MODIS 8- and 16-day composite periods and highlighted the importance of information about day of MODIS prod- ucts pixel composite for monitoring agricultural crops. Due to high maize GLAI temporal variability, the inclusion of day of pixel composite is necessary to decrease substantial uncertainties in estimating GLAI. Results also indicated that maize GLAI can be accurately retrieved from the 250-m resolution MODIS products (MOD13Q1 and MOD09Q1) by a wide dynamic range vegetation index with root mean square error (RMSE) below 0.60 m 2 m −2 or by the enhanced
One of the most commonly used parameters for the analysis of canopy structures is leafareaindex (LAI) (Beadle 1997; López-Serrano et al. 2000; Davi et al. 2009) which is defined as the project- ed one-sided leafarea per unit ground area (De- blonde et al. 1994). This parameter determines the amount of the plant – atmosphere interface; hence, it plays a crucial role in the process of exchanging energy and mass between the canopy and the at- mosphere (Weiss et al. 2004). Methods for the esti- mation of LAI are mainly categorized as direct and indirect methods (Kussner, Mosandl 2000). It is difficult to use direct methods, including harvest- ing, allometry and litter collection (Bréda 2003). Then, to obtain the LAI of a stand, a destructive sampling should be done through harvesting the to- tal leaf biomass of the trees to generate the total dry weight of the foliage (Arias et al. 2007). On the oth- er hand, indirect methods, in which leafarea (LA) is inferred from observations of another variable,
Abstract: Plant leafareaindex (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI- 2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio. Keywords: leafareaindex; smartphone camera sensor; conifer forest; canopy gap fraction
Leafareaindex (LAI) is defined as one half of the total leaf surface area per unit ground surface area (projected on the local horizontal datum). It is a key biophysical vegetation property describing biome-specific canopy structure , and an essential variable in models of ecosystem processes and productivity [2,3], crop productivity  and hydrology . Prescribing these models with accurate LAI parameters is, however, challenging due to the scarcity of landscape scale LAI measurements for most p art of world’s vegetated biomes. Data deficiencies are especially acute in tropical regions and across degraded woodlands [6,7].
The real time data storing strategy which may causes preventing the data cost in case of server does work properly. So through by new technology of cloud the user can be provided with public access or private access. The data which collected from the GPRS module which can be access over by either can see all through by public access or to see and view through simple user can be accessed via over by private access whenever and wherever. The work which examined in this paper to store the huge data’s of the measurement of leafareaindex in far flung areas using the advanced technique of cloud computing and to retrieve the data’s as it is needed. The simulations were performed using PORTEUS working environment and the program were loaded into the controller. Fig 8(a) illustrates the snapshot of the screen PORTEUS working environment. Based on the intensity falling on the sensor the Leafareaindex value varies. In figure 8(b) the leafindexarea of the floras is displayed as 1.3 and this value is obtained by adjusting the sensors S1 and S2. Similarly by adjusting the sensors value the LAI= 3.0 is obtained. It reveals that the vegetation growth is good when the LAI value is least.
Abstract. In the present work, the role played by vegetation parameters, necessary to the hydrological distributed mod- eling, is investigated focusing on the correct use of remote sensing products for the evaluation of hydrological losses in the soil water balance. The research was carried out over a medium-sized river basin in Southern Italy, where the veg- etation status is characterised through a data-set of multi- temporal NDVI images. The model adopted uses one layer of vegetation whose status is defined by the LeafAreaIndex (LAI), which is often obtained from NDVI images. The in- herent problem is that the vegetation heterogeneity – includ- ing soil disturbances – has a large influence on the spectral bands and so the relation between LAI and NDVI is not un- ambiguous.
The measurement and estimation of LAI in the field is prone to errors on several levels. In this study, the method used for deriving LAI through hemispherical photography was in accordance with the procedure suggested by Macfarlane (2007) and other studies [62–64]. However, there are three commonly recognized discrepancies when measuring LAI through hemispherical photography. First, even with the best algorithms, LAI is derived from a two-dimensional picture and therefore it is difficult to quantify, especially in complex forest architectures. The saturation of optically measured LAI is usually reached at around LAI values of 5 . Second, the estimation of LAI includes the contribution of woody elements, a so-called plant areaindex, rather than an actual leafareaindex. Recent studies have shown that the error margin of LAI measured in a heterogeneous forest environment through hemispherical photography can reach − 46.2% to +32.6% compared to LAI estimates derived with a terrestrial laser scanner . Third, the photographic exposure affects the magnitude of canopy gap fraction, and therefore it is crucial to apply the right exposure in order to enable a clear distinction between sky and canopy pixels for later processing . The right exposure is mainly dependent on the light conditions, and therefore the best case would be to have two cameras, one in an open field and one at the plot, to be able to adapt quickly to changing light conditions. As mentioned in the description of the study area, the weather conditions in the Bavarian Forest change very quickly, and therefore it can be assumed that the results would have improved if measurements were conducted with a simultaneous two-camera setup. Nevertheless, it is a widely used method, as it is fast, the equipment needed is relatively cheap to obtain, and it is an optical method, which means it has a certain similarity to the remotely sensed data.
The Global Climate Observing System, GCOS, identified the leafareaindex, LAI, as one of the main terrestrial essential climate variables, ECVs, to be monitored from systematic long-term satellite and in situ measurements (GCOS, 2011; Bojinski et al., 2014). The LAI corresponds to one-half of the total green leafarea per unit horizontal ground surface area (Watson, 1947; Chen and Black, 1992). Similarly, the plant areaindex, PAI, corresponds here to the above-ground areal extent of green vegetation. LAI or PAI is related both to the fraction of absorbed photosynthetic active radiation (0.4–0.7 µm) by the green vegetation, fAPAR (Myneni and Williams, 1994; Fensholt et al., 2006), and to the canopy green cover fraction, fCover, i.e. the amount of green veg- etation distributed in a horizontal plane (Carlson and Rip- ley, 1997). These ECVs drive the fundamental physiolog- ical processes at leaf and canopy levels such as photosyn- thesis and transpiration, as well as the energy and mass ex- changes between the surface and the atmosphere (Sellers, 1985). The seasonal variation of the ECVs, i.e. changes in the vegetation phenology, plays an important role in the mod- ulation of energy, water and gas exchanges (Jarlan et al., 2008; Boulain et al., 2009), surface properties such as sur- face albedo (Samain et al., 2008; Guichard et al., 2009), and evaporation–transpiration partitioning (Wang et al., 2014). As such, these ECVs are key variables required in most land surface and biogeochemical models (Running and Gower, 1991; Potter et al., 1993; Wang et al., 2007) or production efficiency models (Mougin et al., 1995; Running et al., 2004; Tracol et al., 2006; McCallum et al., 2009).
Logistic and exponential approaches have been used to simulate plant growth and leafareaindex (LAI) in different growing conditions. The objective of the present study was to develop and evaluate an approach to simulate maize LAI that expresses key physiological and phonological processes using a mini- mum entry requirement for Quality Protein maize (QPM) varieties grown in the southwestern region of the DR-Congo. Data for the development and testing of the model were collected manually in experimental plots using a non-destructive method. Simulation results revealed measurable variations between crop seasons (long season A and short season B) and between the two varieties (Mudishi-1 and Mudishi-3) for height, number of visible leaves, and LAI. For both seasons, Mudishi-3, a short stature variety was associated with expected stable yield based on simulation data. In general, the model simulated reliably all the parameters including the LAI. The LAI value for mudishi-1 was higher than that of Mudishi-3. There were significant differ- ences among the model parameters (K, Ti, a, b, Tf) and between the two va- rieties. In all crop conditions studied and for the two varieties, the senescence rate (a) was higher, while the growth rate (b) was lower compared to the es- timates based on the STICS model.
Yang F., Sun Y., Fang H., Yao Z., Zhang J., Zhu Y., Song K., Wang Z., Hu M. (2012): Comparison of different methods for corn LAI estimation over northeastern China. International Journal of Applied Earth Observation and Geoinformation, 18: 466–471. Xu R., Dai J., Luo W., Yin X., Li Y., Tai X., Han L., Chen Y., Lin L., Li G., Zuo C., Du W., Diao M. (2010): A photothermal model of leafareaindex for greenhouse crops. Agricultural and Forest Meteorology, 150: 541–552.
The aim of this study was to assess the leafareaindex (LAI) of tomato and cucumber using an AccuPAR-LP- 80-ceptometer to find the influence of irrigation. LAI was also determined by destructive sampling for comparison. The research was conducted at the Liaoning Water Conservancy Institute, North China in 2016. A randomized block design was used to test the influence of four treatments corresponding to field water capacity. Full irriga- tion (W 1.0 ), 15% (W 0.85 ), 25% (W 0.75 ) and 35% (W 0.65 ) water deficit were applied using the drip system. Regression model was developed to estimate LAI in response to irrigation. The results show that there is no difference between the two methods. The highest LAI obtained for tomato and cucumber was 5.21 and 3.21 m 2 /m 2 , respectively, with