Radio Frequency Interference (RFI) causes severe contamination to passive and active microwave sensing observations and corresponding retrieval products. RFI signals should be detected and filtered before applying the microwave data to retrieval and data assimilation. It is difficult to detect RFI over land surfaces covered by snow because of the scattering effect of snow surface. The double principal component analysis (DPCA) method is adopted in this study, and its ability in identifying RFI signals in AMSR-E data over snow covered regions is investigated. Results show that the DPCA method can detect RFI signals effectively in spite of the impact of snow scattering, and the detected RFI signals persistent over time. Compared to other methods, such as PCA and normalized PCA, DPCA is more robust and suitable for operational application.
With the widespread adoption of location-aware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps: spatial clustering of taxi origin-destination (OD) based on urban traffic grids to discover potentially meaningful places, identifying threshold values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.
The Atmospheric Infrared Sounder (AIRS) provides infrared radiance observations twice daily, which can be used to retrieve total column ozone with high spatial resolution. However, it was found that almost all of the ozone data within typhoons and hurricanes were flagged to be of bad quality by the AIRS original quality control (QC) scheme. This determination was based on the ratio of total precipitable water (TPW) error divided by TPW value, where TPW was an AIRS retrieval product. It was found that the difficulty in finding total column ozone data that could pass AIRS QC was related to the low TPW employed in the AIRS QC algorithm. In this paper, a new two-step QC scheme for AIRS total column ozone is developed. A new ratio is defined which replaces the AIRS TPW with the zonal mean TPW retrieved from the Advanced Microwave Sounding Unit. The first QC step is to remove outliers when the new ratio exceeds 33%. Linear regression models between total column ozone and mean potential vorticity are subsequently developed with daily updates, which are required for future applications of the proposed total ozone QC algorithm to vortex initialization and assimilation of AIRS data. In the second QC step, observations that significantly deviate from the models are further removed using a biweighting algorithm. Numerical results for two typhoon cases and two hurricane cases show that a large amount of good quality AIRS total ozone data is kept within Tropical Cyclones after implementing the proposed QC algorithm.
We propose an adaptive regularized algorithm for remote sensing image fusion based on variational methods. In the algorithm, we integrate the inputs using a “grey world” assumption to achieve visual uniformity. We propose a fusion operator that can automatically select the total variation (TV)–L1 term for edges and L2-terms for non-edges. To implement our algorithm, we use the steepest descent method to solve the corresponding Euler–Lagrange equation. Experimental results show that the proposed algorithm achieves remarkable results.
Landscape metrics are measurements of land-use patterns and land-use change, but even so, have rarely been integrated into land-use change simulation models. This paper proposes a new artificial neural network-cellular automaton by integrating landscape metrics into the model. In this model, each cell acquires unique landscape metric values. The landscape metric values of each cell are actually the landscape metric values of land use type in its neighborhood, which takes the cell as center. The calculation of landscape metrics ensures that those of each cell can represent cellular spatial environmental characteristics. The model is used to simulate land use change in the Changping district of Beijing, China. Comparisons of the simulated land use map with the actual map show that the proposed model is effective for land use change simulation. The validation is further carried out by comparing the simulated land use map with that simulated by an artificial neural network-cellular automaton model, which has not been integrated with landscape metrics. Results indicate that the proposed model is more appropriate for simulating both quantity and spatial distribution of land use change in the study area.
Climate financing is a key issue in current negotiations on climate protection. This study establishes a climate financing model based on a mechanism in which donor countries set up funds for climate financing and recipient countries use the funds exclusively for carbon emission reduction. The burden-sharing principles are based on GDP, historical emissions, and consumption-based emissions. Using this model, we develop and analyze a series of scenario simulations, including a financing program negotiated at the Cancun Climate Change Conference (2010) and several subsequent programs. Results show that sustained climate financing can help to combat global climate change. However, the Cancun Agreements are projected to result in a reduction of only 0.01°C in global warming by 2100 compared to the scenario without climate financing. Longer-term climate financing programs should be established to achieve more significant benefits. Our model and simulations also show that climate financing has economic benefits for developing countries. Developed countries will suffer a slight GDP loss in the early stages of climate financing, but the long-term economic growth and the eventual benefits of climate mitigation will compensate for this slight loss. Different burden-sharing principles have very similar effects on global temperature change and economic growth of recipient countries, but they do result in differences in GDP changes for Japan and the FSU. The GDP-based principle results in a larger share of financial burden for Japan, while the historical emissions-based principle results in a larger share of financial burden for the FSU. A larger burden share leads to a greater GDP loss.
The 12 May 2008 Mw 7.9 Wenchuan, China earthquake triggered about 200,000 landslides, which were controlled by a number of factors. This study examines five factors: slope angle, slope aspect, lithology, peak ground acceleration (PGA), and fault side (relative position on the seismogenic fault, i.e., hanging wall or footwall), to determine how these factors control the co-seismic landslide occurrence and whether one or more factors, acting alone or in concert, are involved in promoting or suppressing landslides. We performed a multi-factor statistical analysis using data from the 2008 Wenchuan earthquake. The results show that in the areas characterized by steep topography or where strong ground shaking occurred during the earthquake, there is a closer relationship between slope aspect and landslide number density (LND) than other areas. The relationship between lithology and LND values depends on PGA. In turn, the relationship between LND values and PGA is also influenced by lithology. In addition, the controlling effect of lithology on co-seismic landslides on the hanging wall of the seismogenic fault is greater than that on the footwall. Examining interactions among these factors can improve understanding of the mechanisms of co-seismic landslide occurrence.
A multidisciplinary approach using the integrated field of geosciences (e.g., geomorphology, geotectonics, geophysics, and hydrology) is established to conduct groundwater recharge potential mapping of the Kunur River Basin, India. The relative mean error (RME) calculation of the results of three applied techniques and water table data from twenty-four observation wells in the basin over the 2000-2010 period are presented. Nine sub-basins were identified and ranked for the RME calculation, where the observation wells-based ranking was taken as standard order for comparison. A linear model has been developed using six factors (drainage density, surface slope, ruggedness index, lineament density, Bouguer gravity anomaly, and potential maximum water retention capacity) and a grid-wise weighted index. In a separate comparative approach, the sub-basin and grid-wise analyses have been conducted to identify the suitable spatial unit for watershed level hydrological modeling.
Crop phenology retrieval in the double-cropping area of China is of great significance in crop yield estimation and water management under the influences of global change. In this study, rice phenology in Jiangsu Province, China was extracted from multi-temporal MODIS NDVI using frequency-based analysis. Pure MODIS pixels of rice were selected with the help of TM images. Discrete Fourier Transformation (DFT), Discrete Wavelet Transformation (DWT), and Empirical Mode Decomposition (EMD) were performed to decompose time series into components of different frequencies. Rice phenology in the double-cropping area is mainly located on the last 2 IMFs of EMD and the first 2‒3 frequencies of DFT and DWT. Compared with DFT and DWT, EMD is limited to fewer frequencies. Multi-temporal MODIS NDVI data combined with frequency-based analysis can retrieve rice phenology dates with on average 79% valid estimates. The sorting result for effective estimations from different methods is DWT (85%)>EMD (80%)>DFT (74%). Planting date (88%) is easier to estimate than harvesting date (70%). Rice planting date is easily affected by the former cropping mode within the same year in a double-cropping region. This study sheds light on understanding crop phenology dynamics in the frequency domain of multi-temporal MODIS data.
Mapping rice cropping systems with optical imagery in multiple cropping regions is challenging due to cloud contamination and data availability; development of a phenology-based algorithm with a reduced data demand is essential. In this study, the Landsat-derived Renormalized Index of Normalized Difference Vegetation Index (RNDVI) was proposed based on two temporal windows in which the NDVI values of single and early (or late) rice display inverse changes, and then applied to discriminate rice cropping systems. The Poyang Lake Region (PLR), characterized by a typical cropping system of single cropping rice (SCR, or single rice) and double cropping rice (DCR, including early rice and late rice), was selected as a testing area. The results showed that NDVI data derived from Landsat time-series at eight to sixteen days captures the temporal development of paddy rice. There are two key phenological stages during the overlapping growth period in which the NDVI values of SCR and DCR change inversely, namely the ripening phase of early rice and the growing phase of single rice as well as the ripening stage of single rice and the growing stage of late rice. NDVI derived from scenes in two temporal windows, specifically early August and early October, was used to construct the RNDVI for discriminating rice cropping systems in the polder area of the PLR, China. Comparison with ground truth data indicates high classification accuracy. The RNDVI approach highlights the inverse variations of NDVI values due to the difference of rice growth between two temporal windows. This makes the discrimination of rice cropping systems straightforward as it only needs to distinguish whether the candidate rice type is in the period of growth (RNDVI<0) or senescence (RNDVI>0).
The long-term field experiment data at four representative agro-meteorological stations, together with a crop simulation model, were used to disentangle the contributions of climate change, variety renewal, and fertilization management to rice yield change in the past three decades. We found that during 1981–2009 varieties renewal increased rice yield by 16%–52%, management improvement increased yield by 0–16%, and the contributions of climate change to rice yield varied from −16% to 10%. Varieties renewal and management improvement offset the negative impacts of climate change on rice production. Among the major climate variables, decreases in solar radiation reduced rice yield on average by 0.1% per year. The impact of temperature change had an explicit spatial pattern. It increased yield by 0.04%–0.4% per year for single rice at Xinbin and Ganyu station and for late rice at Tongcheng station, by contrast reduced yield by 0.2%–0.4% per year for single rice at Mianyang station and early rice at Tongcheng station. During 1981–2009, rice varieties renewal was characterized by increases in thermal requirements, grain number per spike and harvest index. The new varieties were less sensitive to climate change than old ones. The development of high thermal requirements, high yield potential and heat tolerant rice varieties, together with improvement of agronomic management, should be encouraged to meet the challenges of climate change and increasing food demand in future.
A simulated loess watershed, where the loess material and relief properly represent the true loess surface, is adopted to investigate the variation in slope spectrum with loess watershed evolution. The evolution of the simulated loess watershed was driven by the exogenetic force of artificial rainfall. For a period of three months, twenty artificial rainfall events with different intensities and durations were carried out. In the process, nine DEM data sets, each with 10 mm grid resolution, were established by the method of close-range photogrammetry. The slope spectra were then extracted from these DEMs. Subsequent series of carefully designed quantitative analyses indicated a strong relationship between the slope spectrum and the evolution of the simulated loess watershed.
Quantitative indices of the slope spectrum varied regularly following the evolution of the simulated loess watershed. Mean slope, slope spectrum information entropy (H), terrain driving force (Td), Mean patch area (AREA_MN), Contagion Index (CONTAG), and Patch Cohesion Index (COHESION) kept increasing following the evolution of the simulated watershed, while skewness (S), Perimeter-Area Fractal Dimension (PAFRAC), and Interspersion and Juxtaposition Index (IJI) represented an opposite trend. All the indices changed actively in the early and active development periods, but slowly in the stable development periods. These experimental results indicate that the time series of slope spectra was able to effectively depict the slope distribution of the simulated loess watershed, thus presenting a potential method for modeling loess landforms.
Anthropogenic activities in the Pearl River Delta (PRD) have caused a deterioration of groundwater quality over the past twenty years as a result of rapid urbanization and industrial development. In this study, the hydrochemical characteristics, quality, and sources of heavy metals in the groundwater of the PRD were investigated. Twenty-five groundwater samples were collected and analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), δ18O, δ2H, major ions, and heavy metals. The groundwater was slightly acidic and presented TDS values that ranged from 35.5 to 8,779.3 mg·L?1. The concentrations of the major ions followed the order Cl->
Industrial symbiosis is the quintessential characteristic of an eco-industrial park. To divide parks into different types, previous studies mostly focused on qualitative judgments, and failed to use metrics to conduct quantitative research on the internal structural or functional characteristics of a park. To analyze a park’s structural attributes, a range of metrics from network analysis have been applied, but few researchers have compared two or more symbioses using multiple metrics. In this study, we used two metrics (density and network degree centralization) to compare the degrees of completeness and dependence of eight diverse but representative industrial symbiosis networks. Through the combination of the two metrics, we divided the networks into three types: weak completeness, and two forms of strong completeness, namely “anchor tenant” mutualism and “equality-oriented” mutualism. The results showed that the networks with a weak degree of completeness were sparse and had few connections among nodes; for “anchor tenant” mutualism, the degree of completeness was relatively high, but the affiliated members were too dependent on core members; and the members in “equality-oriented” mutualism had equal roles, with diverse and flexible symbiotic paths. These results revealed some of the systems’ internal structure and how different structures influenced the exchanges of materials, energy, and knowledge among members of a system, thereby providing insights into threats that may destabilize the network. Based on this analysis, we provide examples of the advantages and effectiveness of recent improvement projects in a typical Chinese eco-industrial park (Shandong Lubei).
Urban underlying surface has been greatly changed with rapid urbanization, considered to be one of the major causes for the destruction of urban natural hydrological processes. This has imposed a huge challenge for stormwater management in cities. There has been a shift from gray water management to green stormwater management thinking. The green stormwater infrastructure (GSI) is regarded as an effective and cost-efficient stormwater management eco-landscape approach. China’s GSI practice and the development of its theoretical framework are still in the initial stage. This paper presents an innovative framework for stormwater management, integrating green stormwater infrastructure and landscape security patterns on a regional scale based on an urban master plan. The core concept of green stormwater infrastructure eco-planning is to form an interconnected GSI network (i.e., stormwater management landscape security pattern) which consists of the location, portion, size, layout, and structure of GSI so as to efficiently safeguard natural hydrological processes. Shanghai Lingang New City, a satellite new town of Shanghai, China was selected as a case study for GSI studies. Simulation analyses of hydrological processes were carried out to identify the critical significant landscape nodes in the high-priority watersheds for stormwater management. GSI should be planned and implemented in these identified landscape nodes. The comprehensive stormwater management landscape security pattern of Shanghai Lingang New City is designed with consideration of flood control, stormwater control, runoff reduction, water quality protection, and rainwater utilization objectives which could provide guidelines for smart growth and sustainable development of this city.
The massive Ulva (U.) prolifera bloom in the Yellow Sea was first observed and reported in summer of 2008. After that, the green tide event occurred every year and influenced coastal areas of Jiangsu and Shandong provinces of China. Satellite remote sensing plays an important role in monitoring the floating macroalgae. In this paper, U. prolifera patches are detected from quasi-synchronous satellite images with different spatial resolution, i.e., Aqua MODIS (Moderate Resolution Imaging Spectroradiometer), HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting), CCD (Charge-Coupled Device), Landsat 8 OLI (Operational Land Imager), and ENVISAT (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) images. Two comparative experiments are performed to explore the U. prolifera monitoring abilities by different data using detection methods such as NDVI (Normalized Difference Vegetation Index) with different thresholds. Results demonstrate that spatial resolution is an important factor affecting the extracted area of the floating macroalgae. Due to the complexity of Case II sea water characteristics in the Yellow Sea, a fixed threshold NDVI method is not suitable for U. prolifera monitoring. A method with adaptive ability in time and space, e.g., the threshold selection method proposed by Otsu (1979), is needed here to obtain accurate information on the floating macroalgae.