Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r²_NDVI = 0.90; r²_CHL3 = 0.85; p < 0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll content well. This study will help guide and inform future efforts to map forest health conditions at landscape and over increasingly coarse scales.     

Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations

The main objective of the current paper is to evaluate and explain differences between computed green-up dates of vegetated land surface derived from satellite observations and budburst dates from ground observational networks. Landscapes dominated by deciduous broad-leaved trees in Germany are analysed. While ground observations generally record the onset of bud break, remote sensing refers to a detectable change of surface reflectance, which accounts for the unfolding of the majority of the leaves. The satellite detects, even in a homogeneous stand, two signals: the green-up of the understorey and, shortly after, the green-up of the canopy (overstorey). Results of comparisons indicate an earlier, although not consistently, satellite-derived green-up than bud break derived from ground observations.We hypothesise that this is due to heterogeneous ground cover and a detection of the greening of non-tree vegetation by the satellite. This hypothesis is tested by analysing the difference between satellite-derived green-up dates (d_GU) and budburst observed on the ground (d_BB) in function of the proportion of non-deciduous-forest (ndf) land use types in satellite scenes. The satellite data (a daily 1-km resolution AVHRR product) are analysed with progressively more restricted selection criteria regarding the land surface elements. The two sets of observations are compared using Gaussian Mixture Models to evaluate the statistical properties of the probability density functions (pdf) as produced by the two sets rather than comparisons of geographically coincident data. It is shown that a heterogeneous vegetation cover is likely to be the main factor determining the difference between the computed green-up date and date of budburst of the dominating tree species.     

Development of an approach for generation of temporally complete daily nadir MODIS reflectance time series

Consistent, spatially and temporally complete reflectance time series are required for reliable terrestrial monitoring. The Moderate Resolution Imaging Spectroradiometer (MODIS), like other polar-orbiting wide field of view satellite sensors, can provide global observations on a nearly daily basis, but the sparseness of valid observations due to cloud, residual atmospheric effects, and sensor anomalies, may result in gaps in the derived reflectance time series. This paper presents an approach for the generation of temporally complete daily MODIS 500 m nadir view BRDF-adjusted reflectance (NBAR) time series. The research is illustrated and assessed quantitatively using two years of cloud and snow screened, daily MODIS Terra and Aqua reflectance data at four sites in Africa, and demonstrated for phenology monitoring using NBAR derived NDVI time series. The components of the approach include: 1) an outlier detection algorithm to remove residual anomalous daily observations undetected in the upstream processing, 2) the dynamic generation of NBAR time series on a daily basis when seven or more observations are available for a day under consideration over a 16-day period, and 3) the means to gap fill the NBAR time series where less than 7 observations are available. The MODIS Ross-Thick/Li-Sparse-Reciprocal BRDF model is used with a rolling approach whereby a 16-day BRDF inversion window is moved on a daily overlapping basis to provide more reliable outlier detection and daily NBAR. NBAR gap filling in periods of missing observations is investigated using static land cover specific archetype BRDF parameters and using BRDF parameters defined adaptively from the temporally closest 16-day periods with 7 or more observations. Scaling factor estimators using ordinary least squares (OLS) and median-based robust least squares regression are investigated, and the robust method is demonstrated to provide on average temporally more coherent gap filled NBAR values. For regions with persistent clouds, the utility of the adaptive NBAR gap filling method is demonstrated to be severely limited due to the decreased likelihood that the surface BRDF at each gap can be described reliably. The reliability of the NBAR gap filling methodology is evaluated statistically using a cross-validation approach. For the small number of study site considered, the adaptive method is shown to provide more accurate results than the archetype method when there are more than an average of ~ 4-5 observations per 16-day window, or when a gap day is on average less than about 30 days from a 16-day period with 7 or more observations. The resulting gap free daily NBAR time series and derived daily NBAR NDVI generated by the approach is shown to capture phenological variations in a coherent temporally consistent manner, suggesting that it is a fruitful avenue for future research and validation.     

Phenological change detection while accounting for abrupt and gradual trends in satellite image time series

A challenge in phenology studies is understanding what constitutes phenological change amidst background variation. The majority of phenological studies have focused on extracting critical points in the seasonal growth cycle, without exploiting the full temporal detail. The high degree of phenological variability between years demonstrates the necessity of distinguishing long-term phenological change from temporal variability. Here, we demonstrate the phenological change detection ability of a method for detecting change within time series. BFAST, Breaks For Additive Seasonal and Trend, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change. We tested BFAST by simulating 16-day NDVI time series with varying amounts of seasonal amplitude and noise, containing abrupt disturbances (e.g. fires) and long-term phenological changes. This revealed that the method is able to detect the timing of phenological changes within time series while accounting for abrupt disturbances and noise. Results showed that the phenological change detection is influenced by the signal-to-noise ratio of the time series. Between different land cover types the seasonal amplitude varies and determines the signal-to-noise ratio, and as such the capacity to differentiate phenological changes from noise. Application of the method on 16-day NDVI MODIS images from 2000 until 2009 for a forested study area in south eastern Australia confirmed these results. It was shown that a minimum seasonal amplitude of 0.1 NDVI is required to detect phenological change within cleaned MODIS NDVI time series using the quality flags. BFAST identifies phenological change independent of phenological metrics by exploiting the full time series. The method is globally applicable since it analyzes each pixel individually without the setting of thresholds to detect change within a time series. Long-term phenological changes can be detected within NDVI time series of a large range of land cover types (e.g. grassland, woodlands and deciduous forests) having a seasonal amplitude larger than the noise level. The method can be applied to any time series data and it is not necessarily limited to NDVI.     

Detecting trend and seasonal changes in satellite image time series

A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01-0.07 σ) and seasonal amplitudes (0.1-0.5 NDVI). Additionally, BFAST was applied to 16-day NDVI Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur.     

Combining high spatial resolution multi-temporal satellite data with leaf-on LiDAR to enhance tree species discrimination at the crown level

ABSTRACT

The long-standing goal of discriminating tree species at the crown-level from high spatial resolution imagery remains challenging. The aim of this study is to evaluate whether combining (a) high spatial resolution multi-temporal images from different phenological periods (spring, summer and autumn), and (b) leaf-on LiDAR height and intensity data can enhance the ability to discriminate the species of individual tree crowns of red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina) in the Fernow Experimental Forest, West Virginia, USA. We used RandomForest models to measure a loss of classification accuracy caused by iteratively removing from the classification one or more groups from six groups of variables: spectral reflectance from all multispectral bands in the (1) spring, (2) summer, and (3) autumn images, (4) vegetation indices derived from the three multispectral datasets, (5) canopy height and intensity from the LiDAR imagery, and (6) texture related variables from the panchromatic and LiDAR datasets. We also used ANOVA and decision tree analyses to elucidate how the multispectral and LiDAR datasets combine to help discriminate tree species based on their unique phenological, spectral, textural, and crown architectural traits. From these results, we conclude that combing high spatial resolution multi-temporal satellite data with LiDAR datasets can enhance the ability to discriminate tree species at the crown level.     

AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005

The Sahel region of Africa has experienced a decrease in rainfall from the early 1960s to mid 1990s. Recent studies have detected an increased in NDVI amplitude and growing season integrated NDVI for the region since 1982. However, these studies have not examined how plant phenology has changed. Phenology examines life cycle events such as bud burst and leaf senescence. Using the software TIMESAT to estimate phenological parameters from the GIMMS AVHRR NDVI dataset, we have found significant positive trends for the length of the growing and end of the growing season for the Soudan and Guinean regions, but significant trends in the Sahel could not be detected. The geographical extent of these trends contrasts with the more northern extent of positive trends of NDVI amplitude and growing season integrated NDVI. Results suggest two types of “greening” trends associated with rainfall change since the drought in the early 1980s.     

Is the future blue-green? A review of the current model predictions of how climate change could affect pelagic freshwater cyanobacteria

There is increasing evidence that recent changes in climate have had an effect on lake phytoplankton communities and it has been suggested that it is likely that Cyanobacteria will increase in relative abundance under the predicted future climate. However, testing such a qualitative prediction is challenging and usually requires some form of numerical computer model. Therefore, the lake modelling literature was reviewed for studies that examined the impact of climate change upon Cyanobacteria. These studies, taken collectively, generally show an increase in relative Cyanobacteria abundance with increasing water temperature, decreased flushing rate and increased nutrient loads. Furthermore, they suggest that whilst the direct effects of climate change on the lakes can change the timing of bloom events and Cyanobacteria abundance, the amount of phytoplankton biomass produced over a year is not enhanced directly by these changes. Also, warmer waters in the spring increased nutrient consumption by the phytoplankton community which in some lakes caused nitrogen limitation later in the year to the advantage of some nitrogen-fixing Cyanobacteria. Finally, it is also possible that an increase in Cyanobacteria dominance of the phytoplankton biomass will lead to poorer energy flow to higher trophic levels due to their relatively poor edibility for zooplankton.     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT/VEGETATION time-series

In mountain forest ecosystems where elevation gradients are prominent, temperature gradient-based phenological variability can be high. However, there are few studies that assess the capability of remote sensing observations to monitor ecosystem phenology along elevation gradients, despite their relevance under climate change. We investigated the potential of medium resolution remotely sensed data to monitor the elevation variations in the seasonal dynamics of a temperate deciduous broadleaf forested ecosystem. Further, we explored the impact of elevation on the onset of spring leafing. This study was based on the analysis of multi-annual time-series of VEGETATION data acquired over the French Pyrenees Mountain Region (FPMR), in conjunction with simultaneous ground-based observations of leaf phenology made for two dominant tree species in the region (oak and beech). The seasonal variations in the perpendicular vegetation index (PVI) were analyzed during a five-year period (2002 to 2006). The five years of data were averaged into a one sole year in order to fill the numerous large spatio-temporal gaps due to cloud and snow presence - frequent in mountains - without altering the temporal resolution. Since a VEGETATION pixel (1 km²) includes several types of land cover, the broadleaf forest-specific seasonal dynamics of PVI was reconstructed pixel-by-pixel using a temporal unmixing method based on a non-parametric statistical approach. The spatial pattern of the seasonal response of PVI was clearly consistent with the relief. Nevertheless the elevational or geographic range of tree species, which differ in their phenology sensitivity to temperature, also has a significant impact on this pattern. The reduction in the growing season length with elevation was clearly observable from the delay in the increase of PVI in spring and from the advance of its decrease in the fall. The elevation variations in leaf flushing timing were estimated from the temporal change in PVI in spring over the study area. They were found to be consistent with those measured in situ (R2 > 0.95). It was deduced that, over FPMR, the mean delay of leaf flushing timing for every 100 m increase in elevation was estimated be approximately 2.3 days. The expected estimation error of satellite-based leaf unfolding date for a given elevation was approximately 2 days. This accuracy can be considered as satisfactory since it would allow us to detect changes in leafing timing of deciduous broadleaf forests with a magnitude equivalent to that due to an elevation variation of 100 m (2.3 days on average), or in other words, to that caused by a variation in the mean annual air temperature of 0.5 °C. Although averaging the VEGETATION data over five years led to a loss of interannual information, it was found to be a robust approach to characterise the elevation variations in spring leafing and its long-term trends.