QUANTIFICATION OF ERROR IN AVHRR NDVI DATA

Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. However temporal aggregation improved the precision of the data to a limited extent only. The research presented in this dissertation indicated that the NDVI change of ~0.03 to ~0.08 NDVI units in 10 to 20 years, frequently reported in recent literature, can be significant in some cases. However, unless spatially explicit uncertainty metrics are quantified for the specific spatiotemporal aggregation schemes used by these studies, the significance of observed differences between sites and temporal trends in NDVI will remain unknown

A Top-Down Approach to Estimating Spatially Heterogeneous Impacts of Development Aid on Vegetative Carbon Sequestration

Since 1945, over $4.9 trillion dollars of international aid has been allocated to developing countries. To date, there have been no estimates of the regional impact of this aid on the carbon cycle. We apply a geographically explicit matching method to estimate the relative impact of large-scale World Bank projects implemented between 2000 and 2010 on sequestered carbon, using a novel and publicly available data set of 61,243 World Bank project locations. Considering only carbon sequestered due to fluctuations in vegetative biomass caused by World Bank projects, we illustrate the relative impact of World Bank projects on carbon sequestration. We use this information to illustrate the geographic variation in the apparent effectiveness of environmental safeguards implemented by the World Bank. We argue that sub-national data can help to identify geographically heterogeneous impact effects, and highlight many remaining methodological challenges

Impact of Sensor Degradation on the MODIS NDVI Time Series

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor degradation on trend detection using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004/yr decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in NDVI trends over vegetation

Amazon Forests Maintain Consistent Canopy Structure and Greenness During the Dry Season

The seasonality of sunlight and rainfall regulates net primary production in tropical forests. Previous studies have suggested that light is more limiting than water for tropical forest productivity, consistent with greening of Amazon forests during the dry season in satellite data.We evaluated four potential mechanisms for the seasonal green-up phenomenon, including increases in leaf area or leaf reflectance, using a sophisticated radiative transfer model and independent satellite observations from lidar and optical sensors. Here we show that the apparent green up of Amazon forests in optical remote sensing data resulted from seasonal changes in near-infrared reflectance, an artefact of variations in sun-sensor geometry. Correcting this bidirectional reflectance effect eliminated seasonal changes in surface reflectance, consistent with independent lidar observations and model simulations with unchanging canopy properties. The stability of Amazon forest structure and reflectance over seasonal timescales challenges the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions. Correcting optical remote sensing data for artefacts of sun-sensor geometry is essential to isolate the response of global vegetation to seasonal and interannual climate variability

Quantification of Impact of Orbital Drift on Inter-Annual Trends in AVHRR NDVI Data

The Normalized Difference Vegetation Index (NDVI) time-series data derived from Advanced Very High Resolution Radiometer (AVHRR) have been extensively used for studying inter-annual dynamics of global and regional vegetation. However, there can be significant uncertainties in the data due to incomplete atmospheric correction and orbital drift of the satellites through their active life. Access to location specific quantification of uncertainty is crucial for appropriate evaluation of the trends and anomalies. This paper provides per pixel quantification of orbital drift related spurious trends in Long Term Data Record (LTDR) AVHRR NDVI data product. The magnitude and direction of the spurious trends was estimated by direct comparison with data from MODerate resolution Imaging Spectrometer (MODIS) Aqua instrument, which has stable inter-annual sun-sensor geometry. The maps show presence of both positive as well as negative spurious trends in the data. After application of the BRDF correction, an overall decrease in positive trends and an increase in number of pixels with negative spurious trends were observed. The mean global spurious inter-annual NDVI trend before and after BRDF correction was 0.0016 and −0.0017 respectively. The research presented in this paper gives valuable insight into the magnitude of orbital drift related trends in the AVHRR NDVI data as well as the degree to which it is being rectified by the MODIS BRDF correction algorithm used by the LTDR processing stream

Measurement of Within-Season Tree Height Growth in a Mixed Forest Stand Using UAV Imagery

Tree height growth measurements at monthly and annual time scales are important for calibrating and validating forest growth models, forest management and studies of forest ecology and biophysical processes. Previous studies measured the terminal growth of individual trees or forest stands at annual or decadal time scales. Short-term, within-season measurements, however, are largely unavailable due to technical and practical limitations. Here, we describe a novel approach for measuring within-season tree height growth using a time series of co-registered digital surface models obtained with a low-cost unmanned aerial vehicle in combination with ground control plates and Structure from Motion data processing. The test site was a 2-hectare temperate mixed forest stand of varying age and successional stage in central Europe. Our results show median growth rates between 27 May and 19 August of 68 cm for Norway spruce, 93 cm for Scots pine, 106 cm for Silver birch and 26 cm for European beech. The results agree well with published field observations for these species. This study demonstrates the capability of inexpensive, increasingly user-friendly and versatile UAV systems for measuring tree height growth at short time scales, which was not previously possible, opening up new avenues for investigation and practical applications in forestry and research.https://doi.org/10.3390/f807023

Characterization of FIREFLY, an Imaging Spectrometer Designed for Remote Sensing of Solar Induced Fluorescence

Solar induced fluorescence (SIF) is an ecological variable of interest to remote sensing retrievals, as it is directly related to vegetation composition and condition. FIREFLY (fluorescence imaging of red and far-red light yield) is a high performance spectrometer for estimating SIF. FIREFLY was flown in conjunction with NASA Goddard’s lidar, hyperspectral, and thermal (G-LiHT) instrument package in 2017, as a technology demonstration for airborne retrievals of SIF. Attributes of FIREFLY relevant to SIF retrieval, including detector response and linearity; full-width at half maximum (FWHM); stray light; dark current; and shot noise were characterized with a combination of observations from Goddard’s laser for absolute measurement of radiance calibration facility; an integrating sphere; controlled acquisitions of known targets; in-flight acquisitions; and forward modelling. FWHM, stray light, and dark current were found to be of acceptable magnitude, and characterized to within acceptable limits for SIF retrieval. FIREFLY observations were found to represent oxygen absorption features, along with a large number of solar absorption features. Shot noise was acceptable for direct SIF retrievals at native resolution, but indirect SIF retrievals from absorption features would require spatial aggregation, or repeated observations of targets

Quantification of Impact of Orbital Drift on Inter-Annual Trends in AVHRR NDVI Data

The Normalized Difference Vegetation Index (NDVI) time-series data derived from Advanced Very High Resolution Radiometer (AVHRR) have been extensively used for studying inter-annual dynamics of global and regional vegetation. However, there can be significant uncertainties in the data due to incomplete atmospheric correction and orbital drift of the satellites through their active life. Access to location specific quantification of uncertainty is crucial for appropriate evaluation of the trends and anomalies. This paper provides per pixel quantification of orbital drift related spurious trends in Long Term Data Record (LTDR) AVHRR NDVI data product. The magnitude and direction of the spurious trends was estimated by direct comparison with data from MODerate resolution Imaging Spectrometer (MODIS) Aqua instrument, which has stable inter-annual sun-sensor geometry. The maps show presence of both positive as well as negative spurious trends in the data. After application of the BRDF correction, an overall decrease in positive trends and an increase in number of pixels with negative spurious trends were observed. The mean global spurious inter-annual NDVI trend before and after BRDF correction was 0.0016 and −0.0017 respectively. The research presented in this paper gives valuable insight into the magnitude of orbital drift related trends in the AVHRR NDVI data as well as the degree to which it is being rectified by the MODIS BRDF correction algorithm used by the LTDR processing stream