scholarly journals Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data

2014 ◽  
Vol 7 (1) ◽  
pp. 195-210 ◽  
Author(s):  
Yonghua Qu ◽  
Wenchao Han ◽  
Mingguo Ma
Keyword(s):  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Modis Lai ◽  
Reflectance Data

Time series 30m resolution leaf area index estimation and vegetation change monitoring in Saihanba, China

2020 ◽  
Author(s):  
Hongmin Zhou ◽  
Guodong Zhang ◽  
Changjing Wang ◽  
Jindi Wang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dynamic Model ◽  
Support Vector ◽  
Growth Monitoring ◽  
Area Index ◽  
Modis Lai ◽  
Reflectance Data

<p>Leaf area index (LAI) is one of the most important biophysical variables for regulating the physiological processes of vegetation canopy. Time series high-resolution LAI data is critical for vegetation growth monitoring, surface process simulation and global change research. However, there are no high-resolution LAI data products that are continuous in time and space. In this paper, we use MODIS LAI products and Landsat surface reflectance data to generate time series high-resolution LAI datasets from 2000 to 2018 in Saihanba based on the ensemble kalman filter, and uses time-series LAI data to monitor surface vegetation changes according to the Prophet model. Firstly, the multi-step Savitzky–Golay filtering algorithm is used to smooth the MODIS LAI data, and the upper envelope of time series LAI is generated. A dynamic model is constructed according to the trend of LAI upper envelope to provide the short-range forecast of LAI. Then the ground measured LAI data and the corresponding Landsat reflectance data are used to train a Back Propagation neural network. High-resolution LAI data from BP model is used to update the dynamic model in real time to generate high-resolution time series LAI data based on EnKF. Finally, the time series LAI data is used as the input of Prophet deep learning model to obtain the LAI time series prediction values of a certain year. Comparing the prediction results with the LAI of current year, the correlation coefficient and the root mean square error distribution maps can be obtained, a support vector machine method is used to classify the disturbed pixels and the normal pixels. The LAI time series estimation has a high accuracy of R²larger than 0.90, and RMSE less than 0.54. The disturbance monitoring results indicate that vegetation in 2009, 2010, 2013, 2014, 2015, 2017 is seriously disturbed, Variation of meteorological conditions and deforest contributes heavily to the disturbance.</p>

scholarly journals Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

2019 ◽  
Vol 11 (21) ◽  
pp. 2517 ◽  
Author(s):  
Huaan Jin ◽  
Weixing Xu ◽  
Ainong Li ◽  
Xinyao Xie ◽  
Zhengjian Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Area Index ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Modis Lai ◽  
Temporal Profiles ◽  
Reflectance Data

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

scholarly journals An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

2008 ◽  
Vol 35 (10) ◽  
pp. 1070 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Anthony R. Palmer ◽  
Daniel Taylor ◽  
Melanie Zeppel ◽  
Rhys Whitley ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Gap Analysis ◽  
Digital Camera ◽  
Field Measurements ◽  
Accurate Estimation ◽  
Image Capture ◽  
Area Index ◽  
Modis Lai ◽  
Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

scholarly journals Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

2020 ◽  
Vol 57 (7) ◽  
pp. 943-964
Author(s):  
Aleksi Räsänen ◽  
Sari Juutinen ◽  
Margaret Kalacska ◽  
Mika Aurela ◽  
Pauli Heikkinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Biomass Estimation ◽  
Area Index

scholarly journals Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

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