Zhihua Mao

Image‐object detectable in multiscale analysis on high‐resolution remotely sensed imagery

Landscapes are complex systems composed of a large number of heterogeneous components as well as explicit homogeneous regions that have similar spectral character on high‐resolution remote sensing imagery. The multiscale analysis method is considered an effective way to study the remotely sensed images of complex landscape systems. However, there remain some difficulties in identifying perfect image‐objects that tally with the actual ground‐object figures from their hierarchical presentation results. Therefore, to overcome the shortcomings in applications of multiresolution segmentation, some concepts and a four‐step approach are introduced for homogeneous image‐object detection. The spectral mean distance and standard deviation of neighbouring object candidates are used to distinguish between two adjacent candidates in one segmentation. The distinguishing value is used in composing the distinctive feature curve (DFC) with object candidate evolution in a multiresolution segmentation procedure. The scale order of pixels is built up by calculating a series of conditional relative extrema of each curve based on the class separability measure. This is helpful in determining the various optimal scales for diverse ground‐objects in image segmentation and the potential meaningful image‐objects fitting the intrinsic scale of the dominant landscape objects. Finally, the feasibility is analysed on the assumption that the homogeneous regions obey a Gaussian distribution. Satisfactory results were obtained in applications to high‐resolution remote sensing imageries of anthropo‐directed areas.

Using long time series of Landsat data to monitor impervious surface dynamics: a case study in the Zhoushan Islands

Abstract Islands are an important part of the marine ecosystem. Increasing impervious surfaces in the Zhoushan Islands due to new development and increased population have an ecological impact on the runoff and water quality. Based on time-series classification and the complement of vegetation fraction in urban regions, Landsat thematic mapper and other high-resolution satellite images were applied to monitor the dynamics of impervious surface area (ISA) in the Zhoushan Islands from 1986 to 2011. Landsat-derived ISA results were validated by the high-resolution Worldview-2 and aerial photographs. The validation shows that mean relative errors of these ISA maps are < 15   % . The results reveal that the ISA in the Zhoushan Islands increased from 19.2     km 2 in 1986 to 86.5     km 2 in 2011, and the period from 2006 to 2011 had the fastest expansion rate of 5.59     km 2 per year. The major land conversions to high densities of ISA were from the tidal zone and arable lands. The expansions of ISA were unevenly distributed and most of them were located along the periphery of these islands. Time-series maps revealed that ISA expansions happened continuously over the last 25 years. Our analysis indicated that the policy and the topography were the dominant factors controlling the spatial patterns of ISA and its expansions in the Zhoushan Islands. With continuous urbanization processes, the rapid ISA expansions may not be stopped in the near feature.

Automatic registration of SeaWiFS and AVHRR imagery

An automatic approach for integrating images from multitemporal and multisensor remote sensing is outlined based on coastlines derived from satellite images. One point on a coastline is taken as a candidate point of ground control points (GCPs). A correlation-relaxation (CR) technique is used to search for the corresponding point in the second image. A decision rule is used to guarantee the correctness of GCPs which are used to compute a polynomial equation for registering two images. The relationship between the accuracy of registration and the number of GCPs indicates that a large number of GCPs will lead to more accurate image registration. The correctness of GCPs can also improve the accuracy of geometric registration. The approach can be used particularly well to register images of coastal areas. Examples are given for registration of SeaWiFS and AVHRR imagery.

Detecting changes in high-resolution satellite coastal imagery using an image object detection approach

This article presents a spatial contrast-enhanced image object-based change detection approach (SICA) to identify changed areas using shape differences between bi-temporal high-resolution satellite images. Each image was segmented and intrinsic image objects were extracted from their hierarchic candidates by the proposed image object detection approach (IODA). Then, the dominant image object (DIO) presentation was labelled from the results of optimal segmentation. Comparing the form and the distribution of bi-temporal DIOs by using the raster overlay function, ground objects were recognized as being spatially changed where the corresponding image objects were detected as merged or split into geometric shapes. The result of typical spectrum-based change detection between two images was enhanced by using changed spatial information of image objects. The result showed that the change detection accuracies of the pixels with both attribute and shape changes were improved from 84% to 94% for the strong attribute pixel, and from 36% to 81% for the weak attribute pixel in study area. The proposed approach worked well on high-resolution satellite coastal images.

Monitoring the occurrence of seasonal low‐oxygen events off the Changjiang Estuary through integration of remote sensing, buoy observations, and modeling

Bottom water hypoxia occurs frequently during the summer off the Changjiang Estuary. To estimate its spatial extent and investigate how climatic variations and extreme events may affect its occurrence, we developed a regional statistical model that combines satellite and buoy observations via empirical and statistical relationships. The estimated results were validated using cruise data off the Changjiang Estuary and its adjacent areas. First, we quantified the linkage between the observed dissolved oxygen (DO) concentrations in the bottom water and the chlorophyll-a (Chl-a) concentrations at the surface. With the help of the model, the bottom layer DO concentrations in the region lacking in situ measurements were estimated using the observations near the buoys and the satellite-derived Chl-a concentrations. Comparisons between the modeled results and surveys conducted between 2006 and 2011 indicate that the error of the estimated extent of low-oxygen bottom water was less than 26% and that the bias in the estimated minimum DO concentration was less than 0.5 mg L−1. Both buoy observations and modeled results indicate that the strength of water stratification and the amount of labile organic matter added to the bottom water are the two main factors that control the occurrence and the magnitude of seasonal low-oxygen events off the Changjiang Estuary.

Influence of bio-optical parameter variability on the reflectance peak position in the red band of algal bloom waters

article i nfo On the basis of field measurements, the quantitatively different relationships of peak position in the red band of the remote sensing reflectance vs. Chl concentration are found in the bloom waters of the diatom Skeletonema costatum and the dinoflagellate Prorocentrum donghaiense in coastal areas of the East China Sea. Model simulations of remote sensing reflectance, Rrs, accounting for the influence of variations in the bio-optical parameters such as chlorophyll fluorescence quantum efficiency, Φ, and specific absorption coef- ficient, aph , are carried out to analyze the characteristics of this spectral peak. The strong effect of fluorescence on the magnitude of Rrs results in the inhibition of the shift of the peak to longer wavelengths, increasing Φ enhances this effect. Increasing aph , specifically in the red-wavelength band, causes a sharper shift in the red peak position by decreasing the effect of the fluorescence. The dominant parameter governing the slope of the shift is aph . The analysis indicates that the higher aph of S. costatum in the red region is primarily respon- sible for the much higher slope of the peak shift than for that of P. donghaiense. We show that the relationship between the peak position and Chl concentration may be useful for discriminating S. costatum blooms from those due to P. donghaiense, although information about chlorophyll fluorescence quantum efficiency should be included. Finally, we show that using the band ratio Rrs(708 nm)/Rrs(665 nm) instead of Chl in the relation- ship with peak position can be useful for the practical identification of S. costatum blooms from hyperspectral measurements of remote sensing reflectance.

Optical detection of Prorocentrum donghaiense blooms based on multispectral reflectance

Prorocentrum donghaiense is one of the most common red tide causative dinoflagellates in the Changjiang (Yangtze) River Estuary and the adjacent area of the East China Sea. It causes large-scale blooms in late spring and early summer that lead to widespread ecologic and economic damage. A means for distinguishing dinoflagellate blooms from diatom(Skeletonema costatum) blooms is desired. On the basis of measurements of remote sensing reflectance [Rrs(λ)] and inherent optical parameters, the potential of using a multispectral approach is assessed for discriminating the algal blooms due to P. donghaiense from those due to S. costatum. The behavior of two reflectance ratios [R1 = Rrs(560)/Rrs (532) and R2 = Rrs(708)/Rrs(665)], suggests that differentiation of P. donghaiense blooms from diatom bloom types is possible from the current band setup of ocean color sensors. It is found that there are two reflectance ratio regimes that indicate a bloom is dominated by P. donghaiense: (1) R1 > 1.55 and R2 < 1.0 or (2) R1 > 1.75 and R2 ⩾ 1.0. Various sensitivity analyses are conducted to investigate the effects of the variation in varying levels of chlorophyll concentration and colored dissolved organicmatter (CDOM) as well as changes in the backscattering ratio (bbp/bp) on the efficacy of this multispectral approach. Results indicate that the intensity and inherent optical properties of the algal species explain much of the behavior of the two ratios. Although backscattering influences the amplitude of Rrs(λ), especially in the 530 and 560 nm bands, the discrimination between P. donghaiense and diatoms is not significantly affected by the variation of bbp/bp. Since aCDOM(440) in coastal areas of the ECS is typically lower than 1.0 m−1 in most situations, the presence of CDOM does not interfere with this discrimination, even as SCDOM varies from 0.01 to 0.026 nm−1. Despite all of these effects, the discrimination of P. donghaiense blooms from diatom blooms based on multispectral measurements of Rrs(λ) is feasible.

Reconstruction of incomplete satellite oceanographic data sets based on EOF and Kriging methods

A complete data set is crucial for many applications of satellite images. Therefore, this paper tries to reconstruct the missing data sets by combining Empirical Orthogonal Functions(EOF) decomposition with Kriging methods. The EOF-based method is an effective way of reconstructing missing data for large gappiness and can maintain the macro-scale and middle-scale information of oceanographic variables. As for sparse data area (area without data or with little data all the time), EOF-based method breaks down, while Kriging interpolation turns effective. Here are the main procedures of EOF-Kriging(EOF-K) method: firstly, the data sets are processed by the EOF decomposition and the spatial EOFs and temporal EOFs are obtained; then the temporal EOFs are analyzed with Singular Spectrum Analysis(SSA); thirdly, the sparse data area is interpolated in the spatial EOFs by using Kriging interpolation; lastly, the missing data is reconstructed by using the modified spatial-temporal EOFs. Furthermore, the EOF-K method has been applied to a large data set, i.e. 151 daily Sea Surface Temperature satellite images of the East China Sea and its adjacent areas. After reconstruction with EOF-K, comparing with original data sets, the root mean square error (RMSE) of cross-validation is 0.58 °C, and comparing with in-situ Argo data, the RMSE is 0.68 °C. Thus, it has been proved that EOF-K reconstruction method is robust for reconstructing satellite missing data.

Detection of water quality parameters in Hangzhou Bay using a portable laser fluorometer

A field, light-weight laser fluorometer based on the method of laser induced fluorescence was developed for water quality monitoring. The basic instrument configuration uses a high pulse repetition frequency microchip laser, a confocal reflective fluorescent probe and a broadband hyperspectral micro spectrometer; it weights only about 1.7 kg. Simultaneous estimates of three important water quality parameters, namely, chlorophyll a (chl-a), colored dissolved organic matter (CDOM), and total suspended matter (TSM) measured by the laser fluorometer were observed to agree well with those measured by traditional methods (0.27-0.84 μg L(-3) chl-a, R(2)=0.88; 0.104-0.295 m(-)(1) CDOM absorption, R(2)=0.90; and 59.8-994.9 mg L(-)(3) TSM, R(2)=0.86) in Hangzhou Bay water. Subsequently, distribution and characteristics of CDOM and chl-a laser fluorescence in Hangzhou Bay were analyzed, which will enhance our understanding of biogeochemical processes in this complex estuarine system at high-resolution, high-frequency and long-term scale.

An approach for estimating absorption and backscattering coefficients from MERIS in the Bohai Sea

Distribution of absorption and backscattering coefficients (a(560) and bb(550)) is important for characterizing the marine optical environment. Satellite remote sensing is a useful tool for investigating the absorption and backscattering coefficients in coastal waters. A simple semi-analytical algorithm (SAABS) was developed for estimating a(560) and bb(550) in the Bohai Sea from Medium Resolution Imaging Spectrometer (MERIS) images. Using field measurements, the SAABS model attained root-mean-square (RMS) values of 13.25% and 12.75% for a(560) and bb(550), respectively. The SAABS model was also used to retrieve a(560) and bb(550) from the MERIS image. The match-up analysis results indicate that the RMS values of a(560) and bb(550) retrievals are 18.75% and 17%, respectively. These findings suggested that if the atmospheric correction scheme is available, the SAABS model may be used for the quantitative monitoring of the absorption and backscattering coefficients in the Bohai Sea from the MERIS images.