Qingting Li

Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

Cropland mapping via remote sensing can provide crucial information for agri-ecological studies. Time series of remote sensing imagery is particularly useful for agricultural land classification. This study investigated the synergistic use of feature selection, Object-Based Image Analysis (OBIA) segmentation and decision tree classification for cropland mapping using a finer temporal-resolution Landsat-MODIS Enhanced time series in 2007. The enhanced time series extracted 26 layers of Normalized Difference Vegetation Index (NDVI) and five NDVI Time Series Indices (TSI) in a subset of agricultural land of Southwest Missouri. A feature selection procedure using the Stepwise Discriminant Analysis (SDA) was performed, and 10 optimal features were selected as input data for OBIA segmentation, with an optimal scale parameter obtained by quantification assessment of topological and geometric object differences. Using the segmented metrics in a decision tree classifier, an overall classification accuracy of 90.87% was achieved. Our study highlights the advantage of OBIA segmentation and classification in reducing noise from in-field heterogeneity and spectral variation. The crop classification map produced at 30 m resolution provides spatial distributions of annual and perennial crops, which are valuable for agricultural monitoring and environmental assessment studies.

Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain

Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat–maize and winter wheat–cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement with in situ observations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region.

Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery

The Defense Meteorological Satellite Program (DMSP)’s Operational Line-scan System (OLS) stable nighttime light (NTL) imagery offers a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales. However, their ability to characterize intra-urban variation is limited due to saturation and blooming of the data values. In this study, we adopted the methods of Mann-Kendall and linear regression to analyze urban dynamics from time series Vegetation Adjusted NTL Urban Index (VANUI) data from 1992 to 2013 in the Southeast United States of America (U.S.A.), which is one of the fastest growing regions in the nation. The newly built urban areas were effectively detected based on the trend analysis. In addition, the VANUI-derived urban areas with an optimal threshold method were found highly consistent with the Landsat-derived National Land Cover Database. The total urbanized areas in large metropolitan areas in southeastern U.S.A. increased from 8524 km2 in 1992 to 14,684 km2 in 2010, accounting for 5% and 9% of the total area, respectively. The results further showed that urban expansion in the region cannot be purely explained by population growth. Our results suggested that the VANUI time series provided an effective method for characterizing the spatiotemporal dynamics of urban extent at the regional scale.

Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series’ suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001–2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation.

A Novel Land Cover Classification Map Based on a MODIS Time-Series in Xinjiang, China

Accurate mapping of land cover on a regional scale is useful for climate and environmental modeling. In this study, we present a novel land cover classification product based on spectral and phenological information for the Xinjiang Uygur Autonomous Region (XUAR) in China. The product is derived at a 500 m spatial resolution using an innovative approach employing moderate resolution imaging spectroradiometer (MODIS) surface reflectance and the enhanced vegetation index (EVI) time series. The classification results capture regional scale land cover patterns and small-scale phenomena. By applying a regionally specified classification scheme, an extensive collection of training data, and regionally tuned data processing, the quality and consistency of the phenological maps are significantly improved. With the ability to provide an updated land cover product considering the heterogenic environmental and climatic conditions, the novel land cover map is valuable for research related to environmental change in this region.

Locality-preserving sparse representation-based classification in hyperspectral imagery

Abstract. This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping

Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs). The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far beyond that offered by multispectral images. However, HSIs are inevitably corrupted by noise during acquisition and transmission processes. The presence of noise may significantly degrade the quality of the extracted mineral information. In order to improve the accuracy of mineral mapping, denoising is a crucial pre-processing task. By leveraging on low-rank and self-similarity properties of HSIs, this paper proposes a state-of-the-art HSI denoising algorithm that implements two main steps: (1) signal subspace learning via fine-tuned Robust Principle Component Analysis (RPCA); and (2) denoising the images associated with the representation coefficients, with respect to an orthogonal subspace basis, using BM3D, a self-similarity based state-of-the-art denoising algorithm. Accordingly, the proposed algorithm is named Hyperspectral Denoising via Robust principle component analysis and Self-similarity (HyDRoS), which can be considered as a supervised version of FastHyDe. The effectiveness of HyDRoS is evaluated in a series of mineral mapping experiments using noise-reduced AVIRIS and Hyperion HSIs. In these experiments, the proposed denoiser yielded systematically state-of-the-art performance.

Classification of CBERS-02B high resolution image using morphological features for urban areas

Urban landscapes represent one of the most challenging areas for remote sensing analysis due to high spatial and spectral diversities of surface materials involved. High Resolution images (HR, better than 5-m spatial resolution) have a potential for detailed and accurate mapping of urban environment. The objective of this study is to analyze the effectiveness of multi-scale morphological features in the purpose of classifying urban landscapes with panchromatic HR images. The experiment is performed using two CBERS HR scenes with urban landscapes characterized by different architectural styles, namely an apartment block and a peri-urban village surrounding Beijing City. Seven types of morphological features including opening (O), closing (C), opening by reconstruction(OR), closing by reconstruction(CR), opening by top-hat(OTH), closing by top hat(CTH) and derivative morphological profile(DMP) are assessed. A support vector machine classifier was also employed to handle the considerable amount of morphological features. The classification results show that with multi-scale morphological features it is possible to discriminate surfaces with mixed spectral characters such as roads, parking lots, and tents due to their different textures for each scene. According to the validation results, the overall accuracy can be improved from 50% with single band HR data to 80.2% and 76.3% respectively for each urban scene using HR-OC-DMP morphological sets. The classification of residential buildings with similar textual character but different gray scales is improved a lot with the supplement of OC sets. The integration of DMP and OC sets benefits the differentiation of bare soil and roads. The mixed sets combining simple, reconstruction and DMP filters provide the best performance.

Oil Slope Index: An algorithm for crude oil spill detection with imaging spectroscopy

Marine oil spill is a major threat to marine and coastal ecosystems and is seen relatively often, such as the Deepwater Horizon oil spill disaster in the Gulf of Mexico in 2010 and Bohai Sea oil spills in China in 2011. Fast and accurate discrimination of oil spill is the largest challenge in detection of oil spills using remote sensing technology. In this research imaging spectroscopic analysis and Oil Slope Index(OSI) were developed to map the locations of surface crude oil in Gulf of Mexico using the SpecTIR data which was collected at 2.2m GSD and 360 spectral channels, covering 390–2450nm. The spectral features and differences of the main objects of oil, sea water and clouds can be found in the DN value of pixel spectra. The slope difference in the range from 550nm to 750nm between crude oil and other objects can be taken as a key feature for detection of crude oil on the sea surface. The Oil Slope Index(OSI) avoids the absorption bands of O2 and H2O in the air and transforms the imaging spectroscopy data into a single band image that shows the distribution of crude oil spill. OSI values can be easily calculated from radiance or DN data and no additional pre-processing of the imagery was necessary before crude oil detection. The result shows that the algorithms work well for oil spill detection which integrated the spectral feature of oil, sea water and clouds by establishing a decision tree. The automatic determination of thresholds by applying Otsus image segmentation can realize the fast and automatic extraction of surface crude oil. This study demonstrated that the Oil Slope Index (OSI) has the potential to become a useful image processing algorithm and operational tool for imaging spectroscopy detection of crude oil spill.

Urban expansion detection with SPOT5 panchromatic images using textural features and PCA

Remote sensing is an effective tool in urban extent mapping and monitoring. In this study, a procedure to detect urban expansion is presented using SPOT5 panchromatic image with 2.5m resolution. First of all, the urban extents of different images are extracted with a PANTEX methodology, which use the anisotropic rotation-invariant textural grey-level co-occurrence measures as the presence of built-up areas. The change detection is performed on the two built-up index images using principal component analysis technique. Finally, the accuracy of this procedure is assessed with visual analysis results. The method shows good performance according to the overall accuracy of 91.18% (Kappa coefficient=0.8719).