LAI Maps Research Articles

Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands.

We aimed to map and analyze LAI by using Landsat 8 and Sentinel-2 time series and the corresponding ground measurements collected in pure Anatolian black pine [Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe] stands within seven-month (from June to December) period. A total of 30 sample plots were selected and seven-month changes of LAI values were determined through hemispherical photography for each sample plot. Remote sensing (reflectance values and vegetation indices obtained from Landsat-8 and Sentinel-2) and topographic (elevation, aspect, and slope) data were used to model the LAI for each month using multiple linear regression (MLR) method. Additionally, the data for all months were combined and modeled. In this case, autoregressive modeling techniques were used to solve the temporal autocorrelation problem. Our study indicated that the models developed from Sentinel-2 give more successful results than Landsat 8 on monthly LAI models. The most successful models were obtained in June by using the reflectance values ({R}_{mathrm{adj}}^{2} = 0.39, RMSE = 0.3138 m2 m−2), reflectance values–topographic data ({R}_{mathrm{adj}}^{2} = 0.59, RMSE = 0.3174 m2 m−2), vegetation indices–topographic data ({R}_{mathrm{adj}}^{2} = 0.82, RMSE = 0.2126 m2 m−2), and reflectance values–vegetation indices–topographic data ({R}_{mathrm{adj}}^{2} = 0.93, RMSE = 0.1060 m2 m−2). Among the autoregressive modeling techniques, the highest success was obtained with the Landsat 8 OLI using the moving average (2) procedure (R2 = 0.56). This study is significant that it is the first to analyze the monthly effect on LAI modeling and mapping in pure Anatolian black pine stands using both reflectance values, vegetation indices, and topographic data.

Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

Estimating the main hydrodynamic features of real vegetated water bodies is crucial to assure a balance between their hydraulic conveyance and environmental quality. Riparian vegetation stands have a high impact on vegetated channels. The present work has the aim to integrate riparian vegetation’s reflectance indices and hydrodynamics of real vegetated water flows to assess the impact of riparian vegetation morphometry on bulk drag coefficients distribution along an abandoned vegetated drainage channel fully covered by 9–10 m high Arundo donax (commonly known as giant reed) stands, starting from flow average velocities measurements at 30 cross-sections identified along the channel. A map of riparian vegetation cover was obtained through digital processing of Unnamed Aerial Vehicle (UAV)-acquired multispectral images, which represent a fast way to observe riparian plants’ traits in hardly accessible areas such as vegetated water bodies in natural conditions. In this study, the portion of riparian plants effectively interacting with flow was expressed in terms of ground-based Leaf Area Index measurements (LAI), which easily related to UAV-based Normalized Difference Vegetation Index (NDVI). The comparative analysis between Arundo donax stands NDVI and LAI map enabled the analysis of the impact of UAV-acquired multispectral imagery on bulk drag predictions along the vegetated drainage channel.

Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine.

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAIG) from Sentinel-2 imagery was imported. Although by running this GPR model into GEE any corner of the world can be mapped into LAIG at a resolution of 20 m, here we show some demonstration cases over western Europe with zoom-ins over Spain. Thanks to the computational power of GEE, the mapping takes place on-the-fly. Additionally, a GPR-based gap filling strategy based on pre-optimized kernel hyperparameters is also put forward for the generation of multi-orbit cloud-free LAIG maps with an unprecedented level of detail, and the extraction of regularly-sampled LAIG time series at a pixel level. The ability to plugin a locally-trained GPR model into the GEE framework and its instant processing opens up a new paradigm of remote sensing image processing.

SASYA: An integrated framework for crop biophysical parameter retrieval and within-season crop yield prediction with SAR remote sensing data

Food security can be assured with a reasonable crop yield forecast at a national and regional scale. The agencies require advance estimates of production of major crops at a regional scale for taking various policy decisions. Hence, it is necessary to develop operational systems for crop monitoring and yield forecasting. Unlike the traditional annual crop yield estimates based on the statistics-based framework, remote sensing techniques are recognized as an effective approach for yield forecasting. In particular, the microwave remote sensing data have gained importance due to its all-weather imaging capability and sensitivity to crop phenological changes. In this present work, an integrated framework ‘SASYA-Space technology for Agricultural Systems and Yield forecasting Algorithm’ is introduced for crop yield forecasting using Synthetic Aperture Radar (SAR) remote sensing images. SASYA incorporates a model inversion scheme to estimate crop biophysical parameters (Leaf Area Index–LAI and wet biomass) simultaneously using a multi-output support vector regression (MSVR). The framework cross-link the MSVR based inversion module with a yield prediction module, which utilizes the WOrld FOod STudies (WOFOST) crop simulation model. The robustness of the framework is assessed for estimation of winter barley yield using L-band NISAR data simulated using full-pol L-band E-SAR airborne SAR data acquired during AgriSAR 2006 campaign over DEMMIN test site in Germany. The simulated NISAR data in HH-HV-VV, HH-HV, and VV-HV modes are first compared by investigating accuracies to estimate crop biophysical parameters with the inversion of Water Cloud Model. These LAI and wet biomass estimates are then used in SASYA framework to assimilate with WOFOST crop yield model to estimate grain yield. Different polarization modes HH-HV-VV, HH-HV, and VV-HV are also assessed to investigate best performance in grain yield estimation. Also, different test cases are considered to find the suitable acquisition dates based on crop phenological stages, which returns the best potential yield estimation. The SAR images during heading and early fruit development stage are found to be most important for yield prediction with lowest RMSE error in the range of 0.57–1.612 ton/ha. The LAI, biomass and potential yield map indicate promising results, which can be used further for crop risk assessment for that region.

Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model

Crop insurance is a viable solution to reduce the vulnerability of smallholder farmers to risks from pest and disease outbreaks, extreme weather events, and market shocks that threaten their household food and income security. In developing and emerging countries, the implementation of area yield-based insurance, the form of crop insurance preferred by clients and industry, is constrained by the limited availability of detailed historical yield records. Remote-sensing technology can help to fill this gap by providing an unbiased and replicable source of the needed data. This study is dedicated to demonstrating and validating the methodology of remote sensing and crop growth model-based rice yield estimation with the intention of historical yield data generation for application in crop insurance. The developed system combines MODIS and SAR-based remote-sensing data to generate spatially explicit inputs for rice using a crop growth model. MODIS reflectance data were used to generate multitemporal LAI maps using the inverted Radiative Transfer Model (RTM). SAR data were used to generate rice area maps using MAPScape-RICE to mask LAI map products for further processing, including smoothing with logistic function and running yield simulation using the ORYZA crop growth model facilitated by the Rice Yield Estimation System (Rice-YES). Results from this study indicate that the approach of assimilating MODIS and SAR data into a crop growth model can generate well-adjusted yield estimates that adequately describe spatial yield distribution in the study area while reliably replicating official yield data with root mean square error, RMSE, of 0.30 and 0.46 t ha−1 (normalized root mean square error, NRMSE of 5% and 8%) for the 2016 spring and summer seasons, respectively, in the Red River Delta of Vietnam, as evaluated at district level aggregation. The information from remote-sensing technology was also useful for identifying geographic locations with peculiarity in the timing of rice establishment, leaf growth, and yield level, and thus contributing to the spatial targeting of further investigation and interventions needed to reduce yield gaps.

Red Edge Index as an Indicator of Vegetation Growth and Vigor Using Hyperspectral Remote Sensing Data

Nitrogen (N) is a major constituent element of chlorophyll and thylakoid proteins in the plant canopy. The amount of foliar nitrogen reflects the growth of vegetation as it affects the photosynthetic process and the interaction of electromagnetic radiation with canopy. Non-destructive approach using space-borne hyperspectral remote sensing has shown potential in estimating the nitrogen content in leaves. This study used hyperspectral image from Hyperion sensor to indicate the strand growth and vigor of Shorea robusta (Sal) forest in Doon Valley by estimating foliar nitrogen. Identification of sampling sites was done in a Sal forest patch of area (270 ha) using crown closure information based on LAI measurement. High spatial resolution satellite data, RapidEye was used for LAI mapping using ground truth data. Leaf spectra using field spectroradiometer, as well as leaf samples were collected from each sample site. The different red edge indices derived from Hyperion data were correlated against laboratory analyzed leaf nitrogen mass. Amongst these indices, Modified Normalized Difference Red Edge (MNDRE) index showed highest correlation coefficient (r = 0.89) with nitrogen mass. For generating spatial nitrogen mass map, regression equation was developed based on MNDRE index. Predicting potential of satellite based MNDRE was validated from field based spectra, as showed r = 0.91. Spatially depicted nitrogen information using hyperspectral satellite image, is one of the first demonstration of the work carried out in the tropical species of Himalaya. Spatial N distribution map has potential to be used in predicting carbon sequestration potential and disturbances caused due to pest-insect infestation, anthropogenic pressure and site degradation.

Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

ABSTRACTLeaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρred), near-infrared (ρNIR), shortwave infrared (ρSWIR1, ρSWIR2) reflectance bands (R2 > 0.6), and all SVIs (R2 > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R2, low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R2 = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.

LAI, FAPAR and FCOVER ground-truth map creation from FASat-C satellite imagery and in-situ measurements in Chimbarongo, Chile, for satellite products validation

&lt;p align="justify"&gt;In remote sensing, validation exercises are essential to ensure the quality of the products originated from satellite Earth observations. To assess the measurement uncertainty derived from satellite products, several ground field data from different ecosystems must be available for use. In the same order of importance, it is necessary to define data sampling and up-scaling methodologies to allow a suitable comparison between the ground data and the pixel size of the product. This paper shows the applied methodology used in the FP7 ImagineS project (Implementing Multi-scale Agricultural Indicators Exploiting Sentinels) to validate 10-days global LAI, FAPAR and vegetation cover products at 1km spatial resolution using in-situ data. These global products are derived from PROBA-V observations in the Copernicus Global Land Service. In particular, this case study shows the results of the field-campaign carried out in January of 2015 in the agricultural area of Chimbarongo, Chile. The methodology to scale the ground data and to create ground-based maps using FASat-C Chilean satellite imagery with a 5,8 m spatial resolution using multivariate least squares regression is shown. Finally, the same methodology was used with a 30 m spatial resolution Landsat-8 image to analyze the effect of the field-data input on the ground-truth maps used to validate the results. Our results show the reliability on the presented methodology and the consistency of the method with regard to the input data. Better results and lower RMSE errors were obtained using FASat-C data. The comparison with satellite products at 1 km shows a good agreement with Copernicus Global Land products derived from PROBA-V observations, and systematic negative bias for the MODIS products.&lt;/p&gt;

Leaf area index mapping with optical methods and allometric models in SMEAR flux tower footprint at Järvselja, Estonia

Abstract Leaf area index (LAI) characterizes the amount of photosynthetically active tissue in plant canopies. LAI is one of the key factors determining ecosystem net primary production and gas and energy exchange between the canopy and the atmosphere. The aim of the present study was to test different methods for LAI and effective plant area index (PAIe) estimation in mixed hemiboreal forests in Järvselja SMEAR Estonia (Station for Measuring Ecosystem-Atmosphere Relations) flux tower footprint. We used digital hemispherical images from sample plots, forest management inventory data, allometric foliage mass models, airborne discrete-return recording laser scanner (ALS) data and multispectral satellite images. The free ware program HemiSpherical Project Manager (HSP) was used to calculate canopy gap fraction from digital hemispherical photographs taken in 25 sample plots. PAIe was calculated from the gap fraction for up-scaling based on ALS point cloud metrics. The all ALS pulse returns-based canopy transmission was found to be the most suitable lidar metric to estimate PAIe in Järvselja forests. The 95-percentile (H95 ) of lidar point cloud height distribution correlates very well with allometric regression models based LAI and in birch stands the relationship was fitted with 0.7 m2 m−2 residual error. However, the relationship was specific to each allometric foliage mass model and systematic discrepancies were detected at large LAI values between the models. Relationships between the spectral reflectance and allometric LAI were not good enough to be used for LAI mapping. Therefore, airborne laser scanning data-based PAIe map was created for areas near SMEAR tower. We recommend to establish a network of permanent sample plots for forest growth and gap fraction measurements into the flux footprint of SMEAR Estonia flux tower in Järvselja to provide consistent up to date data for interpretation of the flux measurements.

Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products

The development of an efficient ground sampling strategy is critical to assess uncertainties associated with moderate- or coarse-resolution remote-sensing products. This work presents a comparison of estimating spatial means from fine spatial resolution images using spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Nonhomogeneity (MSN) at 1 km2 spatial scale. Towards this goal, we focus on the sampling strategies for ground data measurements and provide an assessment of the MODIS LAI product validated by the spatial means estimated by the above-mentioned three methods. The results of this study indicate that: (1) for its effective stratification strategies and its criteria of minimum mean square estimation error, MSN demonstrates the lowest mean squared estimation error for estimating the means of stratified nonhomogeneous surface; (2) BK is efficient in estimating the means of homogeneous surfaces without bias and with minimum mean squared estimation errors. The MODIS LAI product is assessed using the means estimated by SRS, BK, and MSN based on Landsat 8 OLI and SPOT HRV fine-resolution LAI maps. For heterogeneous surfaces, MSN results in low RMSE and high accuracy of MODIS LAI product compared with BK and SRS, whereas for homogeneous surfaces, the statistical parameters outputted by these three methods are similar. These results reveal that MSN is an effective method for estimating the spatial means for heterogeneous surfaces. There are differences in the accuracies of MODIS LAI product assessed by these three methods.

Assimilation of COSMO-SkyMed-derived LAI maps into the AQUATER crop growth simulation model. Capitanata (Southern Italy) case study

AQUATER is a Decision Support System (DSS) developed to drive crop management decisions at district level in a Mediterranean area; it integrates information from soil and climatic databases with a crop growth simulation model and provides estimates of crop yield at regional scale. AQUATER can assimilate LAI maps derived from Earth observation data in order to mitigate the risk of erroneous model predictions over large areas. In this study, time-series of LAI maps derived from COSMO-SkyMed SAR images, acquired over the Capitanata plain (Puglia region) in 2010 and 2011, have been assimilated by a forcing procedure in AQUATER and the improvements of its predictions have been assessed. Results indicate that the LAI assimilation leads to significant improvements in the yield forecast of sugar beet and tomato crops, whereas in the case of wheat the improvements are marginal.

Mapping Seagrass from Space: Addressing the Complexity of Seagrass LAI Mapping

Information of seagrass LAI is still lacking in most parts of the world due to the high cost of comprehensive mapping. In this paper, we described the use of remote sensing as the cost and time effective solution to perform continuous seagrass LAI mapping, and discussed the issues and difficulties encountered during the mapping. ASTER VNIR and ALOS AVNIR-2 were used to perform the mapping. We proposed at life-form seagrass classification scheme to accommodate the low accuracy of at species level mapping. We also developed sampling mapping unit consist of several factors affecting the distribution of seagrass LAI. The results showed that sensor, method, and environmental limitation contribute to the low accuracy of seagrass LAI mapping.

The sensitivity based estimation of leaf area index from spectral vegetation indices

The performances of seven spectral vegetation indices (SVIs) were investigated for their sensitivity to a varying range of LAI. The evaluation was carried out for a dataset collected using SPOT 5 HRG 10m imagery and simulated spectra using PROSPECT+SAIL reflectance models with varying soil reflectance backgrounds. The aim was to evaluate the applicability of multiple SVIs for LAI mapping based on the sensitivity analysis. The main sensitivity function was the first derivative of the regression function divided by the standard errors of the SVIs. In addition, the sensitivity of individual band and SVI with LAI was carried out using the ordinary least squares regressions. A new SVI, reduced infrared simple ratio (RISR) was developed based on an empirical red modification to infrared simple ratio (ISR) SVI. The new SVI was demonstrated which has significantly reduced the effect of soil background reflectance while maintaining high sensitivity to a wide range of LAI.

Reflectance measures of grassland biophysical structure

The goal of this study is to develop an efficient method to retrieve vegetation biophysical properties based on ground LAI measurements and satellite data, and thus avoid the labour‐intensive and time‐consuming process for collecting biomass and canopy height in the future. The field data was conducted in Grasslands National Park (GNP), Saskatchewan, Canada. The two vegetation indices, ATSAVI and RDVI, were derived from SPOT 4 HRV images to estimate LAI and to prepare LAI and biophysical maps for the GNP. The results demonstrated strong relationships between LAI and selected vegetation indices. However, a detailed accuracy assessment indicated that ATSAVI was likely to be better in estimating and mapping LAI than the RDVI. The accuracy of the LAI map was calculated to be 66.7%. The significant relationship between measured LAI and the biophysical data solves the difficulty for mapping biophysical information due to insufficient sampling coverage for GNP.

Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations

The potential of radiative transfer modelling and inversion techniques for operational uses is investigated in order to retrieve leaf area index in a poplar plantation. The 1-D bidirectional canopy reflectance model SAIL, coupled with the leaf optical properties model PROSPECT, was inverted with hyperspectral airborne DAIS data by means of an iterative method. The root mean square error in LAI estimation was determined against in situ measurements in order to evaluate the impact of different inversion strategies on the LAI retrieval accuracy. These included the selection of an optimal spectral sampling set, the exploitation of prior knowledge in the inversion process and the use of multiview angle data. We claim that the best configuration is achieved by exploiting multiview DAIS data and prior knowledge information about the model variables (RMSE of 0.39 m 2 m −2). It is also shown that the use of prior knowledge and the selection of a limited number of bands forming the optimal spectral sampling are instrumental in increasing the accuracy of the inversion process. Our analysis confirms the operational potential of model inversion for biophysical parameter retrieval.

Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping

Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping

Simple calibration of AVIRIS data and LAI mapping of forest plantation in southern Argentina

During the 2001 EO-1 campaign in Argentina, two high spectral resolution image scenes of AVIRIS were acquired at two study sites in the Patagonia region of southern Argentina on 15 February 2001. A total of 70 LAI measurements were taken from different forest types in the same areas one month later, and some spectroradiometric measurements were also collected from the nearby highway and different forest stands in the areas. In this study, we compared the effectiveness of the three types of AVIRIS data used for estimating and mapping LAI. The three types of data correspond to AVIRIS original radiance (OR), corrected radiance (CR) and retrieved surface reflectance (SR). We first simulated the total at-sensor radiances using MODTRAN4, then used ground spectroradiometric measurements taken from different targets to improve the reflectances for each pixel on the image. The CR images were obtained by subtracting path radiance from the OR images. A 10-term LAI prediction model for each type of data was constructed to predict pixel-based LAI values. Finally, the pixel-based LAI value was sliced and mapped for all the three types of images. The results of mapping LAI using the three types of AVIRIS data (OR, CR and SR) indicate that mapping LAI by SR is the most realistic, followed by CR. The poorest result occurs when mapping LAI with OR data due to atmospheric effect. The SR data can lead to higher correlation with LAI in some bands and produce higher accuracy indices for the 10-term predictive model, although some indices from the test set for SR data have a somewhat lower correlation with LAI than those produced with OR data. Therefore, in general, it can be concluded that the retrieved surface reflectance data is more effective for mapping forest LAI compared to the other two types of data.