Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series

Yan, Enping; Wang, Guangxing; Lin, Hui; Xia, Chang; Sun, Hua

doi:10.1080/01431161.2014.999167

Cited by 80 publications

(61 citation statements)

References 44 publications

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“…For vegetation, a traditional group of methods relies on quantifications of the differences in statistical metrics of the vegetation-index time series like, for example, the beginning and end of the growing season, the maximum and minimum values, the annual mean, or the variance [9]. In regions with strongly seasonal climates, production is typically assessed by searching for anomalies in the current NDVI against the average of the whole time series, or against reference values from the same period of the year, which informs about the current status of vegetation as compared to other seasons, or to an average condition [20].…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing constitutes an increasingly used alternative [8], based on the relationship between satellite-derived metrics and primary production [7]. Remote sensing may allow for the non-destructive, high-resolution coverage of large, remote, and/or inaccessible areas, such as mountains [9], deserts [10], or wetlands [4,[11][12][13]. Remote sensing allows for the reconstruction of historical trends as well, using satellite image time series: for example, the reconstruction of the hydroperiod in Doñana marsh from 1974-2014 [14], or the assessment of rangeland conditions in semiarid regions [15].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

et al. 2017

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Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources-particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park's marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001-2015 used to calibrate the models (R 2 = 0.65). Estimators based on a single MODIS NDVI image performed worse (R 2 ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

et al. 2017

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“…In this study, the ability of each spectral variable to distinguish six land cover types, including water bodies, bare lands, carex, phragmites or reed, poplar and willow, was quantified using three widely used measures of class separability, including JM distance [13,14,[16][17][18][19][20], transformed divergence [1,17,33,34] and B-distance [35]. The class separability measures were used to rank the spectral variables that were input one by one into the set of the spectral variables used for classifying the six land cover types.…”

Section: Improving Selection Of Spectral Variablesmentioning

confidence: 99%

“…Complicated methods include fuzzy-logic expert system, exhaustive search by recursion, isolated independent search and sequential dependent search for optimizing the selection of remote sensing variables [11,12]. In addition, Bhattacharyya distance and Jeffreys-Matusita (JM) distance have been widely used to measure the ability of remote sensing variables for separating LULC types and selecting significant remote sensing variables [1,[13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image

et al. 2017

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Abstract:There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys-Matusita (JM) distance and random forests (RF) have been developed for this purpose, the existing methods ignore correlation and information duplication among remote sensing variables. In this study, a novel approach was proposed to improve the measures of potential class separability for the selection of remote sensing variables by taking into account correlations among the variables. The proposed method was examined with a total of thirteen spectral variables from a Gaofen-1 image, three class separability measures including JM distance, transformed divergence and B-distance and three classifiers including Bayesian discriminant (BD), Mahalanobis distance (MD) and RF for classification of six LULC types at the East Dongting Lake of Hunan, China. The results showed that (1) The proposed approach selected the first three spectral variables and resulted in statistically stable classification accuracies for three improved class separability measures. That is, the classification accuracies using three or more spectral variables statistically did not significantly differ from each other at a significant level of 0.05; (2) The statistically stable classification accuracies obtained by integrating MD and BD classifiers with the improved class separability measures were also statistically not significantly different from those by RF; (3) The numbers of the selected spectral variables using the improved class separability measures to create the statistically stable classification accuracies by MD and BD classifiers were much smaller than those from the original class separability measures and RF; and (4) Three original class separability measures and RF led to similar ranks of importance of the spectral variables, while the ranks achieved by the improved class separability measures were different due to the consideration of correlations among the variables. This indicated that the proposed method more effectively and quickly selected the spectral variables to produce the statistically stable classification accuracies compared with the original class separability measures and RF and thus improved the selection of the spectral variables for the classification.

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“…Therefore, the long-term sequence of NDVI information derived from long-term remote-sensing images has led to NDVI time series of remote-sensing data becoming a crucial source of information on land cover classification [20][21][22]. Compared with the traditional classification approach with different intervals from NDVI time series, the use of daily NDVI time series remote-sensing data classification often improves the classification accuracy because it derives more phenological features that have a statistically significant effect on improving land cover classification accuracy [23,24]. However, daily NDVI time series remote sensing is prone to data loss, and unequal intervals cannot fully reveal the change of land-cycle vegetation index [25].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Zhao

Zhang

Pan

2017

Mathematical Problems in Engineering

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A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of -means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

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Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series

Cited by 80 publications

References 44 publications

Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

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