A novel unsupervised bee colony optimization (UBCO) method for remote-sensing image classification: a case study in a heterogeneous marsh area

Unsupervised image classification is an important means to obtain land-use/cover information in the field of remote sensing, since it does not require initial knowledge (training samples) for classification. Traditional methods such as k-means and Iterative self-organizing data analysis technique (ISODATA) have limitations in solving this NP-hard unsupervised classification problem, mainly due to their strict assumptions about the data distribution. The bee colony optimization (BCO) is a new type of swarm intelligence, based upon which a simple and novel unsupervised bee colony optimization (UBCO) method is proposed for remote-sensing image classification. UBCO possesses powerful exploitation and exploration capacities that are carried out by employed bees, onlookers, and scouts. This allows the promising regions to be globally searched quickly and thoroughly, without becoming trapped on local optima. In addition, it has no restrictions on data distribution, and thus is especially suitable for handling complex remote-sensing data. We tested the method on the Zhalong National Nature Reserve (ZNNR) – a typical inland wetland ecosystem in China, whose landscape is heterogeneous. The preliminary results showed that UBCO (overall accuracy = 80.81%) achieved statistically significant better classification result (McNemar test) in comparison with traditional k-means (63.11%) and other intelligent clustering methods built on genetic algorithm (unsupervised genetic algorithm (UGA), 71.49%), differential evolution (unsupervised differential evolution (UDE), 77.57%), and particle swarm optimization (unsupervised particle swarm optimization (UPSO), 69.86%). The robustness and superiority of UBCO were also demonstrated from the two other study sites next to the ZNNR with distinct landscapes (urban and natural landscapes). Enabling one to consistently find the optimal or nearly optimal global solution in image clustering, the UBCO is thus suggested as a robust method for unsupervised remote-sensing image classification, especially in the case of heterogeneous areas.     

Monitoring salt crusts on an AMD contaminated coastal wetland using hyperspectral Hyperion data (Estuary of the River Odiel,SW Spain)

The aim of this work is to establish preliminary spectral trends focused on the development of salt crusts in the marsh located at the mouth of the River Odiel (SW Spain) based on maps from archive Hyperion data. Temporal monitoring of salt efflorescence on the marshes at the mouth of the contaminated river is carried out using hyperspectral space imagery. Climate variability relationships are made based on well-known spectral features related to vegetation and shallow water, using both archive spectral libraries and field local spectra. The observations point to spectral and geomorphological indicators related to salt crust development that can be monitored through image processing supported by field and laboratory spectral data, on a repeatable basis. Future mapping of a larger sequence of images under different climate regimes and wider tidal ranges would improve the estimation of spectral features and thus ensure routine monitoring of salt crusts with hyperspectral data. The study of acid and salt environments, which limit biota development, could be improved by monitoring that employs hyperspectral remote sensing as a useful tool.     

Mapping impervious surface expansion using medium-resolution satellite image time series: a case study in the Yangtze River Delta,China

Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China–Brazil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived from this study agree well with an existing urban extent polygon data set that was previously developed independently. The overall mapping accuracy was estimated at about 92.5% with 3% commission error and 12% omission error for the impervious type from all images regardless of image quality and initial spatial resolution.     

Change analysis of land surface temperature based on robust statistics in the estuarine area of Pearl River (China) from 1990 to 2000 by Landsat TM/ETM+ data

The estuarine area of Pearl River that has taken great changes in land cover since 1990 is a typical area for studying the change of land surface temperature (LST). The LST of the years 1990 and 2000 in this area was estimated from the data of Landsat TM/ETM+ band 6, respectively, and three scales, corresponding to high, normal and low temperature ranges, were divided by a robust statistical method. The results show that the area of high temperature range in 2000 has an increase of 250 km² compared with the year 1990. The urban‐used land and the bare land are the main land cover types constituting the high temperature range area.     

Alteration mineral mapping using masking and Crosta technique for mineral exploration in mid-vegetated areas: a case study in Areletuobie,Xinjiang (China)

Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM)+ data have been successfully employed in the field of mineral exploration to identify key minerals over arid and semi-arid terrains. However, redundant vegetation and cloud may seriously interfere with the discrimination of the minerals with diagnostic features. Therefore, in this study, we use masking technique to eliminate the negative influence of vegetation and cloud and Crosta technique to identify the diagnostic features of hydroxyl-minerals, carbonate-minerals and iron oxides. Then the anomalies were endowed with special colours and overlapped with the remote-sensing and geochemical data, overlaying images as remote-sensing anomalies. The mineral exploration work was carried through by synthetic analysis of the remote-sensing images, geochemical data and structures. Finally, areas with high correlation between the occurrence of hydrothermal alteration and presence of main faults and geochemical anomalies were considered as mineral exploration targets worthy of further detailed exploration programmes.     

Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan,China

Cellular Automata (CA) are widely used to model the dynamics within complex land use and land cover (LULC) systems. Past CA model research has focused on improving the technical modeling procedures, and only a few studies have sought to improve our understanding of the nonlinear relationships that underlie LULC change. Many CA models lack the ability to simulate the detailed patch evolution of multiple land use types. This study introduces a patch-generating land use simulation (PLUS) model that integrates a land expansion analysis strategy and a CA model based on multi-type random patch seeds. These were used to understand the drivers of land expansion and to investigate the landscape dynamics in Wuhan, China. The proposed model achieved a higher simulation accuracy and more similar landscape pattern metrics to the true landscape than other CA models tested. The land expansion analysis strategy also uncovered some underlying transition rules, such as that grassland is most likely to be found where it is not strongly impacted by human activities, and that deciduous forest areas tend to grow adjacent to arterial roads. We also projected the structure of land use under different optimizing scenarios for 2035 by combining the proposed model with multi-objective programming. The results indicate that the proposed model can help policymakers to manage future land use dynamics and so to realize more sustainable land use patterns for future development. Software for PLUS has been made available at https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model     

On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: A case study of the Honghu Lake,PR China

The visible and near infrared bands of the Landsat thematic mapper (TM) were used in an empirical assessment of submerged vegetation biomass in Honghu Lake in the middle reaches of the Yangtse River, in the People's Republic of China. The method used here was based on eigenvector rotation of the four bands to enhance submerged vegetation biomass variations. Field measurement of spectral reflectance of submerged vegetation was taken for various biomass and vegetation types to determine the possibility of estimating submerged vegetation biomass using remote sensing. The locations of sample points were determined by global positioning system (GPS) and field biomass was obtained at the same time as the TM image. Regression analyses were performed between the principal components and biomass, and a marked linear relationship between submerged vegetation biomass and first two principal components (PC) was revealed. This was used to determine the total biomass of submerged vegetation.     

Morphostructural analysis,radar and SPOT image interpretation as tools for petroleum exploration: an example from the Gabian-Pezenas area (Herault,Southern France)

To determine the neotectonic framework of the Gabian-Pezenas area for oil exploration purposes, we have applied a multi-scale approach utilizing the anomalies of the drainage patterns, the radar and SPOT analyses, followed by field data confirmations and seismic profile interpretations. Existing structural data have been reinterpreted. They result in the extension of the Gabian Fault towards the South, and in the recognition of an important detachment fault previously interpreted as a major thrust. Such a multisouree and multiscale approach may provide data critical for the identification of potential oil prospects.     

Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed,China

In this study, a PCA-based cluster quantile regression (PCA-CQR) method was proposed through integrating principal component analysis and quantile regression approaches into a stepwise cluster analysis framework. In detail, the principal component analysis was adopted to overcome the multicollinearity among the explanatory variables, while the quantile regression approach was used to provide probabilistic information in prediction. The proposed PCA-CQR method can effectively capture discrete and nonlinear relationships between explanatory and response variables. The applicability of PCA-CQR was demonstrated by a case study of monthly streamflow prediction in the Xiangxi River, China. The nonlinearity between the hydro-meteorological variables and the streamflow measurements was characterized through the measure of maximal information coefficient (MIC), which demonstrated the need of the proposed PCA-CQR method. The results showed that the previous monthly streamflow and precipitation, as well as potential evapotranspiration in current month posed significant nonlinear impacts on the streamflow in current month. Three components could well reflect the total variance of the input variables. Comparison between traditional SCA and PCA-CQR showed that the proposed approach could provide more accurate predictions than traditional SCA methods. Moreover, probabilistic forecasts could be provided by PCA-CQR, and the 90% predictive intervals could well bracket the observations in both calibration and validation periods. Also, sensitivity analysis was performed to identify the impacts of the control parameters in PCA-CQR on the performance of the proposed model. The results showed the proposed PCA-CQR improved the robustness of traditional SCA. Finally, comparison among PCA-CQR, GRNN and MLR also showed the effectiveness of the proposed method.     

Evaluation of standard and regional satellite chlorophyll-a algorithms for moderate-resolution imaging spectroradiometer (MODIS) in the Bohai and Yellow Seas,China: a comparison of chlorophyll-a magnitude and seasonality

Retrieval of satellite remotely sensed chlorophyll-a (chl-a) concentrations in coastal regions such as the Bohai and Yellow Seas (BYS) is challenging due to their complex oceanic and atmospheric optical properties. The standard OC3M (ocean chl-a three-band algorithm for MODIS (moderate-resolution imaging spectroradiometer)) algorithm has been widely used in the BYS, despite well-known uncertainties about its accuracy in terms of absolute magnitude. This was based on the belief that OC3M chl-a is capable of representing reliable relative spatial and temporal patterns of sea surface chl-a concentrations. In this study, the ability of the standard OC3M chl-a algorithm to reproduce accurate seasonality patterns was evaluated, based on comparisons with in situ chl-a measurements in the BYS. The results quantified the overestimation by the standard OC3M algorithm with a median absolute percentage difference of 98.48% and a median relative difference of 1.13 mg m^?3.More importantly, the seasonality from OC3M chl-a was significantly biased relative to the seasonal patterns of in situ chl-a. In addition, a regional GAM (generalized additive model)-based satellite chl-a algorithm was evaluated and compared with OC3M chl-a. The results showed the GAM chl-a improved accuracy in both magnitude and seasonality when compared with in situ chl-a, relative to that from OC3M chl-a.