The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

[1]  Digital Image Analysis of Old World Bluestem Cover to Estimate Canopy Development , 2019, Agronomy Journal.

[2]  Yong Pang,et al.  Estimation of Forest Aboveground Biomass and Leaf Area Index Based on Digital Aerial Photograph Data in Northeast China , 2018 .

[3]  Xingwang Fan,et al.  A global study of NDVI difference among moderate-resolution satellite sensors , 2016 .

[4]  Daniel Garbellini Duft,et al.  The potential for RGB images obtained using unmanned aerial vehicle to assess and predict yield in sugarcane fields , 2018 .

[5]  Ranga B. Myneni,et al.  The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective , 2013, Remote. Sens..

[6]  Andrew E. Suyker,et al.  Application of day and night digital photographs for estimating maize biophysical characteristics , 2012, Precision Agriculture.

[7]  Zhaoqin Li,et al.  A suitable vegetation index for quantifying temporal variation of leaf area index (LAI) in semiarid mixed grassland , 2010 .

[8]  Dandan Xu,et al.  A Study of Soil Line Simulation from Landsat Images in Mixed Grassland , 2013, Remote. Sens..

[9]  Le Wang,et al.  Feasibility of using consumer-grade unmanned aerial vehicles to estimate leaf area index in Mangrove forest , 2018, Remote Sensing Letters.

[10]  Zhaoqin Li,et al.  Measuring the dead component of mixed grassland with Landsat imagery , 2014 .

[11]  Martha C. Anderson,et al.  A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆ , 2004 .

[12]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[13]  Zheng Wang,et al.  Saturation Correction for Nighttime Lights Data Based on the Relative NDVI , 2017, Remote. Sens..

[14]  María-Paz Diago,et al.  Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions , 2012, Sensors.

[15]  Christopher Conrad,et al.  Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data , 2013 .

[16]  Lifu Zhang,et al.  Monitoring vegetation dynamics using the universal normalized vegetation index (UNVI): An optimized vegetation index-VIUPD , 2019, Remote Sensing Letters.

[17]  Hongliang Fang,et al.  Retrieving leaf area index with a neural network method: simulation and validation , 2003, IEEE Trans. Geosci. Remote. Sens..

[18]  Hideki Kobayashi,et al.  Utility of information in photographs taken upwards from the floor of closed-canopy deciduous broadleaved and closed-canopy evergreen coniferous forests for continuous observation of canopy phenology , 2013, Ecol. Informatics.

[19]  B. Wylie,et al.  NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin, USA , 2013 .

[20]  Yuki Saito,et al.  Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields , 2019, Remote. Sens..

[21]  Chenghai Yang,et al.  Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras , 2017, Remote. Sens..

[22]  Qiming Qin,et al.  A novel dynamic stretching solution to eliminate saturation effect in NDVI and its application in drought monitoring , 2012, Chinese Geographical Science.

[23]  Huaguo Huang,et al.  Detection of Shoot Beetle Stress on Yunnan Pine Forest Using a Coupled LIBERTY2-INFORM Simulation , 2018, Remote. Sens..

[24]  J. Townshend,et al.  Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America , 2008 .

[25]  N. Koper,et al.  Quantifying the influences of grazing, climate and their interactions on grasslands using Landsat TM images , 2018 .

[26]  S. Liang,et al.  A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies , 2005 .

[27]  Hongliang Fang,et al.  Inconsistencies of interannual variability and trends in long‐term satellite leaf area index products , 2017, Global change biology.

[28]  Matthew F. McCabe,et al.  A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning , 2018 .

[29]  Hideki Kobayashi,et al.  Continuous observation of tree leaf area index at ecosystem scale using upward-pointing digital cameras , 2012 .

[30]  Hou Jiang,et al.  Intercomparison of AVHRR GIMMS3g, Terra MODIS, and SPOT-VGT NDVI Products over the Mongolian Plateau , 2019, Remote. Sens..

[31]  Xulin Guo,et al.  A suitable NDVI product for monitoring spatiotemporal variations of LAI in semiarid mixed grassland , 2013 .

[32]  Yuhong He,et al.  Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices , 2006 .

[33]  Venkat Lakshmi,et al.  Comparison of Normalized Difference Vegetation Index Derived from Landsat, MODIS, and AVHRR for the Mesopotamian Marshes Between 2002 and 2018 , 2019, Remote. Sens..

[34]  Huazhong Ren,et al.  Crop Leaf Area Index Retrieval Based on Inverted Difference Vegetation Index and NDVI , 2018, IEEE Geoscience and Remote Sensing Letters.

[35]  S. Brantley,et al.  Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies , 2011 .

[36]  Xin Li,et al.  A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat , 2017 .

[37]  S. Liang,et al.  Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model , 2003 .

[38]  J. Tenhunen,et al.  On the relationship of NDVI with leaf area index in a deciduous forest site , 2005 .

[39]  Changwei Tan,et al.  Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law , 2020, Scientific Reports.