Near-infrared Vegetation Indices Research Articles

Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests.

The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive "greenness" of these systems. We combine continuous needle-scale chlorophyll fluorescence measurements with tower-based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar-induced chlorophyll fluorescence (SIF) and solar intensity-normalized SIF (SIFrelative) under snow-covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near-infrared vegetation index [NIRv] under 50% snow cover). Needle-scale fluorescence and photochemistry strongly correlated (r2 = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower-based estimates of LUEP and SIFrelative which were well correlated across all sites (r2 = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data-driven and process-based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems.

Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest

Abstract. Photosynthesis by terrestrial plants represents the majority of CO2 uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400–900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400–900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400–900 nm associated with GPP seasonality in evergreen forests.

Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture

Unmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In this study, we investigated the difference in tillage treatments for cotton and sorghum using various RGB and NIR VIs. Minimized tillage has been known to increase farm sustainability and potentially optimize productivity over time; however, repeated tillage is the most commonly-adopted management practice in agriculture. To this day, quantitative comparisons of plant growth patterns between conventional tillage (CT) and no tillage (NT) fields are often inconsistent. In this study, high-resolution and multi-temporal UAV data were used for the analysis of tillage effects on plant health and the performance of various vegetation indices investigated. Time series data over ten dates were acquired on a weekly basis by RGB and multispectral (MS) UAV platforms: a DJI Phantom 4 Pro and a DJI Matrice 100 with the SlantRange 3p sensor. Ground reflectance panels and an ambient illumination sensor were used for the radiometric calibration of RGB and MS orthomosaic images, respectively. Various RGB and NIR-based vegetation indices were then calculated for the comparison between CT and NT treatments. In addition, a one-tailed Z-test was conducted to check the significance of VIs’ difference between CT and NT treatments. The results showed distinct differences in VIs between tillage treatments during the whole growing season. NIR-based VIs showed better discrimination performance than RGB-based VIs. Out of 13 VIs, the modified soil adjusted vegetation index (MSAVI) and optimized soil adjusted vegetation index (OSAVI) showed better performance in terms of quantitative difference measurements and the Z-test between tillage treatments. The modified green red vegetation index (MGRVI) and excess green (ExG) showed reliable separability and can be an alternative for economic RGB UAV application.

Surface albedo as a proxy for land-cover clearing in seasonally dry forests: Evidence from the Brazilian Caatinga

Ongoing increase in human and climate pressures, in addition to the lack of monitoring initiatives, makes the Caatinga one of the most vulnerable forests in the world. The Caatinga is located in the semi-arid region of Brazil and its vegetation phenology is highly dependent on precipitation, which has a high spatial and temporal variability. Under these circumstances, satellite image-based methods are valued due to their ability to uncover human-induced changes from climate effects on land cover. In this study, a time series stack of 670 Landsat images over a period of 31 years (1985–2015) was used to investigate spatial and temporal patterns of land-cover clearing (LCC) due to vegetation removal in an area of the Caatinga. We compared the LCC detection accuracy of three spectral indices, i.e., the surface albedo (SA), the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We applied a residual trend analysis (TSS-RESTREND) to attenuate seasonal climate effects on the vegetation time series signal and to detect only significant structural changes (breakpoints) from monthly Landsat time series. Our results show that SA was able to identify the general occurrence of LCC and the year that it occurred with a higher accuracy (89 and 62%, respectively) compared to EVI (44 and 22%) and NDVI (46 and 22%). The overall outcome of the study shows the benefits of using Landsat time series and a spectral index that incorporates the short-wave infrared range, such as the SA, compared to visible and near-infrared vegetation indices for monitoring LCC in seasonally dry forests such as the Caatinga.

Early detection of GPP-related regime shifts after plant invasion by integrating imaging spectroscopy with airborne LiDAR

Invasive plant species can have high, self-reinforcing impacts on ecosystem structure and functioning that induce permanent changes of ecosystem properties. Therefore, early detection and timely management is required to alleviate ecosystem consequences of invasion. Integrating airborne hyperspectral imagery with LiDAR data can deliver spatially explicit information on invader occurrence and ecosystem transformations even at early stages of invasion. However, relevant “model invaders” and well-characterized ecosystems need to be identified to both increase predictive power of invasion theory and prioritize management. In addition, there is still a knowledge gap regarding sensor-based approaches that are valid in space and time to assess the impact of invasive engineers on ecosystem functioning as well as the potential to induce regime shifts. In this study, occurrence and spatio-temporal impact of the invasive N2-fixing shrub, Acacia longifolia, was assessed in a heterogeneous, Mediterranean dune ecosystem. The invader was mapped using vegetation indices derived from airborne hyperspectral images as well as airborne LiDAR data using Random Forest classification with a Sensitivity of 0.79, a Positive Predicted Value (PPV) of 0.81, and Cohen's Kappa of 0.77. Invaded sites varied between early stages with low cover, where isolated patches were detected, to heavily infested A. longifolia thickets. Analysis of historical images showed that the invader could establish under the harsh conditions of open dune plains, possibly triggered by human interference. The recently developed Near-Infrared Vegetation Index (NIRV), which is related to Gross Primary Production (GPP), increased linearly and significantly with increasing invader cover. This indicated a GPP-related regime shift induced by the invader, changing ecosystem productivity representative of open shrublands to that of forests. Such a shift could even be identified at early stages of invasion. Thus, the NIRV index may provide an appropriate sensor-based “model metric” to assess impacts of invasive engineers. This offers the opportunity to predict and anticipate regime shifts as a basis for timely management.

UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices

ABSTRACTIn the context of plant breeding, high-throughput phenotyping is an assessment of plant phenotypes on a scale and with a level of speed and precision not achievable with traditional methods, through the application of emerging technologies such as automation and robotics, new sensors, and imaging technologies (hardware and software). In the present work, high-resolution digital images have been acquired with an unmanned aerial vehicle (UAV) prototype platform on an experimental phenotyping barley field. Six vegetation indices generated from the red–green–blue and near-infrared-based images were calculated for 912 experimental barley plots and provided high correlation with the indices determined from hyperspectral data taken at the ground (gt); the indices performance in discriminating the vigour of genotypes was finally assessed.

Comparison of broad-band and narrow-band red and near-infrared vegetation indices

An experiment has been conducted in which narrow-band field reflectance spectra were acquired of a rooted pinyon pine canopy with five different gravel backgrounds. Leaf area was successively removed as the measurements were repeated. From these reflectance spectra, narrow-band and broad-band (AVHRR, TM, MSS) red and near-infrared (NIR) vegetation index values were calculated. The performance of the vegetation indices was evaluated based on their capability to accurately estimate leaf area index (LAI) and percent green cover. Background effects were found for each of the tested vegetation indices. However, the background effects are most pronounced in the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). Background effects can be reduced using either the perpendicular vegetation index (PVI) or soil adjusted vegetation index (SAVI) formulations. The narrow-band versions of these vegetation indices had only slightly better accuracy than their broad-band counterparts. The background effects were minimized using derivative based vegetation indices, which measure the amplitude of the chlorophyll red-edge using continuous narrow-band spectra from 626 nm to 795 nm.

Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery

The effects of soil optical properties on vegetation index imagery are analyzed with ground-based spectral measurements and both simulated and actual AVHRR data from the NOAA satellites. Soil effects on vegetation indices were divided into primary variations associated with the brightness of bare soils, secondary variations attributed to 'color' differences among bare soils, and soil-vegetation spectral mixing. Primary variations were attributed to shifts in the soil line owing to atmosphere or soil composition. Secondary soil variance was responsible for the Saharan desert 'artefact' areas of increased vegetation index response in AVHRR imagery.