Time series of remote sensing imagery or derived vegetation indices and biophysical products have been shown particularly useful to characterize land ecosystem dynamics. Various methods have been ...developed based on temporal trajectory analysis to characterize, classify and detect changes in ecosystem dynamics. Although time series similarity measures play an important role in these methods, a quantitative comparison of the similarity measures is lacking. The objective of this study was to provide an overview and quantitative comparison of the similarity measures in function of varying time series and ecosystem characteristics, such as amplitude, timing and noise effects. For this purpose, the performance was evaluated for the commonly used similarity measures (D), ranging from Manhattan (
D
Man
), Euclidean (
D
E
) and Mahalanobis (
D
Mah
) distance measures, to correlation (
D
CC
), Principal Component Analysis (PCA;
D
PCA
) and Fourier based (
D
FFT
,
D
ξ
,
D
Fk
) similarities. The quantitative comparison consists of a series of Monte-Carlo simulations based on subsets of global MODIS Normalized Difference Vegetation index (NDVI) and Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) data. Results of the simulations reveal four main groups of time series similarity measures with different sensitivities: (i)
D
Man
,
D
E
,
D
PCA
,
D
Fk
quantify the difference in time series values, (ii)
D
Mah
accounts for temporal correlation and non-stationarity of variance, (iii)
D
CC
measures the temporal correlation, and (iv) the Fourier based
D
FFT
and
D
ξ
show their specific sensitivity based on the selected Fourier components. The difference measures show relatively the highest sensitivity to amplitude effects, whereas the correlation based measures are highly sensitive to variations in timing and noise. The Fourier based measures, finally, depend highly on the signal to noise ratio and the balance between amplitude and phase dominance. The heterogeneity in sensitivity of each
D stresses the importance of (i) understanding the time series characteristics before applying any classification of change detection approach and (ii) defining the variability one wants to identify/account for. This requires an understanding of the ecosystem dynamics and time series characteristics related to the baseline, amplitude, timing, noise and variability of the ecosystem time series. This is also illustrated in the quantitative comparison, where the different sensitivities of
D for the NDVI, EVI, and LAI data relate specifically to the temporal characteristics of each data set. Additionally, the effect of noise and intra- and interclass variability is demonstrated in a case study based on land cover classification.
► A quantitative comparison of time series similarity measures D is performed. ► Four groups of D with different sensitivities are obtained. ► Time series characteristics, noise and variability affect the performance of D. ► The sensitivities stress the importance of proper selection of similarity measures. ► Understanding time series properties is crucial for classification/change detection.
Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their ...causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods.
The theoretical background of modeling the gap fraction and the leaf inclination distribution is presented and the different techniques used to derive leaf area index (LAI) and leaf inclination angle ...from gap fraction measurements are reviewed. Their associated assumptions and limitations are discussed, i.e., the clumping effect and the distinction between green and non-green elements within the canopy. Based on LAI measurements in various canopies (various crops and forests), sampling strategy is also discussed.
Linear spectral mixture analysis (SMA) has been used extensively in remote sensing studies to estimate the sub-pixel composition of spectral mixtures. The lack of ability to account for sufficient ...temporal and spatial variability between and among ground component or endmember spectra has been acknowledged as a major shortcoming of conventional SMA approaches. In an attempt to overcome this problem, a novel and automated linear spectral mixture protocol, referred to as stable zone unmixing (SZU), is presented and evaluated. Stable spectral features (i.e. least sensitive to spectral variability) are automatically selected for use in the mixture analysis based on a minimum InStability Index (ISI) criterion. ISI is defined as the ratio of the spectral variability within and the spectral variability among the endmember classes that are present within the mixture. The algorithm was tested on a set of scenarios, generated from in situ measured hyperspectral data. The scenarios covered both urban and natural environments under differing conditions. SZU provided reliable endmember cover distribution maps in all scenarios. On average, an absolute gain in R
2
-the coefficient of determination of the modelled versus the observed sub-pixel cover fractions-of 0.14 over the traditional SMA approaches was observed while the absolute gain in fraction abundance error was 0.06. It was concluded that the SZU protocol has potential to be an effective and efficient SMA algorithm for generating optimal cover fraction estimates regardless of the scenario considered. Moreover, the subset selection protocol, as implemented in SZU, can be regarded as complementary to conventional SMA approaches resulting in a further reduction of spectral variability.
A hybrid segmentation procedure to integrate contextual information with per-pixel classification in a metropolitan area land cover classification project is described and evaluated. It is presented ...as a flexible tool within a commercially available image processing environment, allowing components to be adapted or replaced according to the user's needs, the image type, and the availability of state-of-the-art algorithms. In the case of the Twin Cities metropolitan area of Minnesota, the combination of the Shen and Castan edge detection operator with iterative centroid linkage region growing/merging based on Student's
t-tests proved optimal when compared to other more common contextual approaches, such as majority filtering and the Extraction and Classification of Homogeneous Objects classifier. Postclassification sorting further improved the results by reducing residual confusion between urban and bare soil categories. Overall accuracy of the optimal classification technique was 91.4% for a level II classification (10 classes) with a
K
e
of 90.5%. The incorporation of contextual information in the classification process improved accuracy by 5.8% and
K
e
by 6.5%. As expected, classification accuracy for a simplified level I classification (five classes) was higher with 95.4% and 94.3% for
K
e
. A second important advantage of the technique is the reduced occurrence of smaller mapping units, resulting in a more attractive classification map compared to traditional per-pixel maximum likelihood classification results.
Several remote sensing studies have discussed the potential of satellite imagery as an alternative for extensive field sampling to quantify fire–vegetation impact over large areas. Most studies ...depend on Landsat image availability with infrequent image acquisition dates and consequently are limited for assessing intra-annual fire–vegetation dynamics or comparing different fire plots and dates. The control pixel based regeneration index (pRI) derived from SPOT-VEGETATION (VGT) normalized difference vegetation index (NDVI) is used in this study as an alternative to the traditional bi-temporal Landsat approach based on the normalized burn ratio (NBR). The major advantage of the pRI is the use of unburnt control plots which allow the expression of the intra-annual variation due to regeneration processes without external influences. In the comparison of Landsat and VGT data, (i) the inter-annual differences between the bi-temporal and control plot approach were contrasted and (ii) metrics of pRI were derived and compared with the inter-annual dynamics of both VGT and Landsat data. Results of these comparisons, demonstrate the overall similarity between NBR and NDVI data, stress the importance of the elimination of external influences (e.g., phenological variations), and emphasize the failure of including post-fire vegetation responses in bi-temporal Landsat assessments, especially in quickly recovering ecotypes with a strong annual phenological cycle such as savanna. This highlights the importance of using high frequency multi-temporal approaches to estimate fire–vegetation impact in temporally dynamic vegetation types.
The distribution of 203 forest plant species over 234 isolated forest patches in the western part of Belgium and the most northern part of France was studied. An analysis that considered species ...richness in the context of the SLOSS (single large or several small reserves) debate gave no evidence of habitat subdivision reducing total plant species richness in the forests. The presence of some functional ecological plant species groups was correlated with habitat features and patch area. Habitat diversity was found to be important in explaining the presence of species groups of high conservation value, but patch age (as an indicator for habitat quality) also played a major role. Habitat diversity was not a surrogate for patch age. For most of the species groups, patch area
sensu stricto is a redundant variable in explaining species richness relative to habitat diversity and patch age; area-dependent stochastic extinctions of forest plant species are of minor importance, at least at the present level of forest fragmentation. In contrast we suggest that extinction of forest plants occurred and still occurs mainly in a deterministic way. Finally, we conclude that even small forest fragments can be very important for maintaining plant species diversity, at least if they are of high habitat quality and if the forest management is appropriate.
The least squares error (LSE) technique is frequently used to estimate abundance fractions in linear spectral mixture analysis (LSMA). The LSE is typically equally weighted for all wavebands, ...assuming equally important effects. This is, however, not always the case and therefore traditional LSMA often results in suboptimal fraction estimates. This study presents a weighted LSMA approach that prioritises wavebands with minor or no negative effects on fraction estimates. Synthetic mixed pixel spectra compiled from in situ measured spectra of bare soil, citrus tree and weed canopies were used for validation. The results show markedly improved fraction estimates obtained for the weighted approach, with a mean absolute gain of 0.24 in R
2
and a mean absolute reduction in fraction abundance error of 0.06.
An innovative index for LAI estimation over orchard canopies based on the combination of two near-infrared narrow wavebands (1050 nm and 1250 nm) is presented. The index is insensitive to ...physiological changes on the leaf level even under changing pheneological developmental stage. In the meantime, the index is highly sensitive for variations at the canopy level. Values of LAI estimated by means of this newly developed index, called sLAIDI (standardized LAI Determining Index), resulted in excellent agreements with in-situ measured LAI values of citrus trees, with determination coefficients (
R
2) of 0.83. Based on in-situ measured data and simulated data (PROSPECT and ACRM), the newly developed sLAIDI was also found to perform outstanding at high LAI values in contrary to well-known LAI related indices such as the Normalized Difference Vegetation Index (NDVI) and the Modified Chlorophyll Ratio Index 2 (MCARI2) that saturate at LAI values of 5 and 8 respectively. Moreover, the sLAIDI index was independent of changing pigment contents. Consequently, this index was regarded a valuable tool to more accurately extract biochemical parameters after normalizing the considered spectral data for LAI. By implementing sLAIDI in existing chlorophyll-related indices (e.g., GM index), the performance to estimate the leaf chlorophyll content from spectral citrus and peach canopy level data is increased (
R
2 increases from 0.68 to 0.77). Moreover, a correction factor was proposed to enable the application of sLAIDI on fields with strong variations of water content.
Radiative transfer (RT) models for canopies entail multiple implicit and explicit assumptions. In this case study six different commonly used assumptions were evaluated on a detailed virtual orchard ...model. This model was constructed in a physically based ray-tracing environment using detailed sub-models for the description of tree geometry, leaf and soil bidirectional reflectance and diffuse illumination. After calibration and validation with both the RAMI Online Model Checker (ROMC) and field data obtained in a Citrus orchard in Wellington, South Africa, the model was used as a reference to analyze the different assumptions. A first set of three assumptions focused on leaf optical properties, evaluating the effect of the spectral mixing of randomly distributed leaves and trees, leaf asymmetry and leaf bidirectional reflectance and transmittance. The fourth assumption investigated the structure effects of variations in leaf shape and leaf curl. Finally, the last two assumptions were tested at the orchard level and dealt with the row orientation of trees and the angular distribution of diffuse irradiance. Errors induced by each assumption were evaluated in side-by-side comparisons with the reference. Only for the first assumption no measurable error could be detected. All other assumptions caused variable relative errors of up to 70%. The size of the errors was found to be spectrally variable and depends on canopy structure, leaf optical properties and illumination conditions. Corrections with minimal additional complexity in implementation in 3D RT models were suggested.
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