数字环境下面状要素分级设色的适宜方案分析

为探究明度与色相同步变化时不同分级设色方案在数字环境下的适宜性,基于Munsell色彩调和秩序与CIEDE2000色差公式设计了7种分级设色方案,并开展了视觉认知试验。试验结果表明：①增加各级间色差会导致颜色辨识的准确性增强;②与各级间色差相等的色阶相比,色差先增大后减小的色阶有利于读图判别;③图斑面积分布均匀程度对颜色辨识的影响并不显著;④色阶的总色差增大,会导致颜色过渡的协调性降低。色差先增大后减小的色阶能产生较好的辨识效果,同时保持较好的色彩协调性,且相关规律可以推广到图斑面积分布均匀程度不同的地图中,在分级设色中运用具有优势。     

Toward green cartography & visualization: a semantically-enriched method of generating energy-aware color schemes for digital maps

ABSTRACT

We introduce a semantically-enriched method of generating color schemes for various types of digital maps that reduces the energy consumption of the display device while preserving the quality of the original design. Energy-aware design intersects two important trends in cartography. First, as more maps are viewed today on mobile, battery life has become a central constraint influencing design. Second, there is increasing need for green computing, which encourages the efficient use of energy to limit environmental impacts. This paper focuses on one important aspect of energy-aware cartography: color design. Existing research on energy-aware color adjustment methods apply broadly to images or websites. However, the colors used in maps have more structured semantic relationships than most documents viewed on mobile devices, and efforts to account for these relationships while reducing energy consumption are limited. To fill this gap, we mathematically formalize energy-aware map-color adjustment as a constrained optimization problem: we define energy consumption as the objective function and model the preservation of semantic relationships as the search constraints. We evaluate our proposed method against a common color dimming method using four maps with different semantic relationships. The evaluation suggests that our proposed method better preserves the original color semantics.     

基于核密度估计方法的地图色域分析

针对地图制图中色彩选择较为困难的问题,该文收集国内外优秀地图、地图集、著作等上成功的设色方案,提取大量地图色彩样本。利用核密度估计方法,分析各类地图的常用色域区间,作为地图配色时的基色调。根据大量的地图色彩样本,利用定量的数学方法以及数据挖掘方法,探索地图色域特征分布规律。最后以收集的人口地图和环保地图颜色样本为例进行实验分析,对比证明本文方法能够很好地缩小地图色域选择区间,方便制图者根据地图种类选择颜色。     

政区地图设色方案的自动实现

分析了政区地图的设色要求 ,阐述了色彩设计中的对比调和理论 ,以此为依据设计出相应色环 ,规定一种间色 ,在色环中确定这种间色的对比色和类似色的CMY配色值 ,依据一定的顺序 ,自动把颜色配置在全国政区图上 ,并对自动设色和人工设色结果进行了比较分析 ,表明在可视化环境中 ,自动设色的地图比没有或仅有较少制图知识的人设计出的地图工效高     

Estimating spatial variations of total evaporation using multispectral sensors within the uMngeni catchment,South Africa

Total evaporation is of importance in assessing and managing long-term water use, especially in water-limited environments. Therefore, there is need to account for water utilisation by different land uses for well-informed water resources management and future planning. This study investigated the feasibility of using multispectral Landsat 8 and moderate resolution imaging spectroradiometer (MODIS) remote sensing data to estimate total evaporation within the uMngeni catchment in South Africa, using surface energy balance system. The results indicated that Landsat 8 at 30 m resolution has a better spatial representation of total evaporation, when compared to the 1000 m MODIS. Specifically, Landsat 8 yielded significantly different mean total evaporation estimates for all land cover types (one-way ANOVA; F_4.964?=?87.011, p < 0.05), whereas MODIS failed to differentiate (one-way ANOVA; F_2.853?=?0.125, p = 0.998) mean total evaporation estimates for the different land cover types across the catchment. The findings of this study underscore the utility of the Landsat 8 spatial resolution and land cover characteristics in deriving accurate and reliable spatial variations of total evaporation at a catchment scale.     

利用地理属性量表的色盲人群地图色彩设计研究

根据地图表达地理属性的量表性质,将地图设色方案分为定性设色、顺序设色和双向设色等3种方式。基于这3种地图设色方式的特点,根据红-绿色盲的光谱特性,设计了一系列实验地图,通过颜色辨识的心理学实验,分析了红-绿色盲的视觉特征,初步揭示出一些针对红-绿色盲人群使用的地图设色原则,为制作红-绿色盲人群使用的地图提供了有效的参考。     

Application of hyperspectral remote sensing reflectance data to photochemical rate calculations in the Duplin River,a tidal river on the coast of Georgia,USA

Estuaries are photochemically dynamic environments with high carbon loads and relatively small areas. The small area poses problems for large-scale satellite-based remote sensing calculations, where the resolution is too coarse to distinguish land from water. Airborne remote sensing instruments have the potential to reveal the dynamics of these areas with fine-scale resolution. In June 2006, hyperspectral remote sensing imagery, using an AISA Eagle instrument, was collected over the tidal Duplin River, Georgia, USA. A dark-water updated version of the SeaUV algorithm was applied to the AISA remote sensing image to determine diffuse attenuation constants in the ultraviolet and calculate surface photochemical production rates of two inorganic products – carbon monoxide (CO) and carbon dioxide (CO₂). For an average day in June at the study site, the modeled photoproduction rates for CO₂ and CO averaged ~7 × 10^?1 nmol C/day/cm³ and ~3.5 × 10^?2 nmol C/day/cm³, respectively.     

Positional accuracy of the Google Earth terrain model derived from stratigraphic unconformities in the Big Bend region,Texas, USA

The Google Earth terrain model could prove beneficial for extraction of positional data in the future. At present, only an aging independent benchmark study (Potere, D., 2008. Horizontal position accuracy of Google Earth's high-resolution imagery archive. Sensors, 8, 7973–7981) provides constraints on positional accuracy for Google Earth imagery. In this investigation, we compared virtually traced positions against high-precision (<1 m) field measurements along three stratigraphic unconformity sub-sections in the Big Bend region to determine current positional accuracy for the Google Earth terrain model. A horizontal position accuracy of 2.64 m RMSE_r was determined for the Google Earth terrain model with mean offset distance being 6.95 m. A vertical position accuracy of 1.63 m RMSE_z with mean offset distance of 2.66 m was also calculated for the terrain model. Results suggest data extracted from the Google Earth terrain model could plausibly be used in future studies. However, we urge caution in using Google Earth data due to limited information disclosures by developers.     

Quantification of LST and CO2 levels using Landsat-8 thermal bands on urban environment

Land surface temperature (LST) is an important aspect in global to regional change studies, for control of climate change and balancing of high temperature. Urbanization is one of the influencing factors increasing land surface and atmospheric temperature, by the emission of greenhouse gases (e.g. CO₂, NO and methane). In the present study, LST was derived from Landsat-8 of multitemporal data sets to analyse the spatial structure of the urban thermal environment in relation to the urban surface characteristics and land use–land cover (LULC). LST is influenced by the greenhouse gases i.e. CO₂ plays an important role in increasing the earth’s surface temperature. In order to provide the evidence of influence of CO₂ on LST, the relationship between LST, air temperature and CO₂ was analysed. Landsat-8 satellite has two thermal bands, 10 and 11. These bands were used to accurately to calculate the temperature over the study area. Results showed that the strength of correlation between ground monitoring data and satellite data was high. Based on correlation values of each month April (R² = 0.994), May (R² = 0.297) and June (R² = 0.934), observed results show that band 10 was significantly correlating with air temperature. Relationship between LST and CO₂ levels were obtained from linear regression analysis. band 11 was correlating significantly with CO₂ values in each of the months April (R² = 0.217), May (R² = 0.914) and June, (R² = 0.934), because band 11 is closer to the 15-micron band of CO₂. From the results, it was observed that band 10 can be used for calculating air temperature and band 11 can be used for estimation of greenhouse gases.     

A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia,Canada)

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r² = 0.56–0.77 AIC = 0.16–0.30 for SR models; r² = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVI_MODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVI_MSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVI_MSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVI_MODIS data (r² = 0.68 for SC for non-grazed systems, modeled with SR based on NDVI_MODIS data; r² = 0.77 for SC for non-grazed systems, modeled with SR based on NDVI_MSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.     

Analysis on the global morphology of stratospheric gravity wave activity deduced from the COSMIC GPS occultation profiles

Monthly mean global morphologies of potential energy density E _p from stratospheric gravity waves are revealed by observations of COSMIC GPS radio occultation. The E _p is obtained from vertical wavelengths ranging from 2 to 10 km over cells of 1° × 2° in latitude and longitude. The computed values confirm previous results and obtain new ones. The large gravity wave E _p values found in the tropics between 25°N and 25°S could be mainly due to the strong tropical cumulus convection; July values are larger than those for January (2007). In mid and high latitudes, the most prominent features of the northern winter hemisphere are the enhanced densities above the Eurasian continent and the North Atlantic and the depressed E _p values above the North Pacific and North America for which topography, wind sources and wind filtering may be responsible. In southern winter hemisphere, large E _p values are found around 180° and 300° longitudes that are likely due to the topography of the Antarctic plateau, the Antarctic Peninsula and South America. Enhanced E _p values are found over Scandinavia. However, there is no clear evidence to show that gravity waves are localized over the Rocky Mountains, the Himalayas and the Andes. Topography and planetary wave modulations are proposed to interpret the large-scale longitudinal variations and inter-hemisphere asymmetry of the GW activity.     

ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps

Abstract

Choosing effective colour schemes for thematic maps is surprisingly difficult. ColorBrewer is an online tool designed to take some of the guesswork out of this process by helping users select appropriate colour schemes for their specific mapping needs by considering: the number of data classes; the nature of their data (matched with sequential, diverging and qualitative schemes); and the end-use environment for the map (e.g., CRT, LCD, printed, projected, photocopied). ColorBrewer contains 'learn more' tutorials to help guide users, prompts them to test-drive colour schemes as both map and legend, and provides output in five colour specification systems.     

Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley,Zimbabwe using Geographic Information Systems and remote sensing

The objective of this study was to understand the factors that explain the spatial distribution of elephant poaching activities in the areas of the mid-Zambezi Valley, Zimbabwe using geographic information system (GIS) and remotely sensed data integrated with spatial logistic regression. The results showed that significant (α = 0.05) elephant poaching hot spots are located closer to wildlife protected areas. Results further demonstrated that resource availability (water and forage) are the main factors explaining elephant poaching activities in the mid-Zambezi Valley. For example, the majority of poaching activities were found to occur in areas with high vegetation fractional cover (high forage) and close to waterholes. The results also showed that poaching incidences were more prevalent during the dry season. The findings of this study highlight the significance of integrating GIS, remotely sensed data and spatial logistic regression tools for understanding and monitoring elephant poaching activities. This information is critical if poaching activities are to be minimized and it is also important for planning, monitoring and mitigation of poaching activities in similar protected areas across the sub-Saharan Africa.     

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.     

Use of cartographic images by expert and novice field geologists in planning fieldwork routes

Topographic maps and aerial photographs are particularly useful when geoscientists are faced with fieldwork tasks such as selecting paths for observation, establishing sampling schemes, or defining field regions. These types of images are crucial in bedrock geologic mapping, a cognitively complex field-based problem-solving task. Geologic mapping requires the geologist to correctly identify rock types and three-dimensional bedrock structures from often partial or poor-quality outcrop data while navigating through unfamiliar terrain. This paper compares the walked routes of novice to expert geologists working in the field (n = 66) with the results of a route planning and navigation survey of a similar population of geologists (n = 77). Results show clearly that those geologists with previous mapping experience make quick and decisive determinations about field areas from available imagery and maps, regardless of whether they are or not physically present in the field area. Recognition of geologic features enabled experts to form and verbalize a specific plan for travel through a landscape based on those features. Novices were less likely to develop specific travel route plans and were less likely to identify critical landscape cues from aerial photographs.     

A spatial interaction model with spatially structured origin and destination effects

We introduce a Bayesian hierarchical regression model that extends the traditional least-squares regression model used to estimate gravity or spatial interaction relations involving origin-destination flows. Spatial interaction models attempt to explain variation in flows from n origin regions to n destination regions resulting in a sample of N = n ² observations that reflect an n by n flow matrix converted to a vector. Explanatory variables typically include origin and destination characteristics as well as distance between each region and all other regions. Our extension introduces latent spatial effects parameters structured to follow a spatial autoregressive process. Individual effects parameters are included in the model to reflect latent or unobservable influences at work that are unique to each region treated as an origin and destination. That is, we estimate 2n individual effects parameters using the sample of N = n ² observations. We illustrate the method using a sample of commodity flows between 18 Spanish regions during the 2002 period.     

Implications of ocean color in the upper water thermal structure at NINO3.4 region: a sensitivity study for optical algorithms and ocean color variabilities

Chlorophyll a (Chl-a) has been the most commonly used biomass metric in biological oceanographic processes. Although limited to two-dimensional surfaces, remote-sensing tools have been successfully providing the most recent state of marine phytoplankton biomass to better understand bottom-up processes initiating daily marine material cycles. In this exercise, ocean color products with various time-scales, derived from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), were used to investigate how their bio-optical properties affect the upper-ocean thermal structure in a global ocean modeling framework. This study used a ¼-degree Hybrid Coordinate Ocean Model forced by hourly atmospheric fluxes from the Climate Forecast System Reanalysis at National Oceanic Atmospheric Administration. Three numerical experiments were prepared by combining two ocean color products – downwelling diffuse attenuation coefficients (K_dPAR) and chlorophyll a (Chl-a) – and two shortwave radiant flux algorithms. These three runs are: (1) KparCLM, based on a 13-year long-term climatological K_dPAR derived from SeaWiFS; (2) ChlaCLM, based on a 13-year long-term Chl-a derived from SeaWiFS; and (3) ChlaID, which uses the inter-annual time-series of monthly-mean SeaWiFS Chl-a product. The KparCLM experiment uses a Jerlov-like two-band scheme; whereas, both ChlaCLM and ChlaID use a two-band scheme that considers inherent (absorption (a) and backscattering (b_b) coefficients) and apparent optical properties (downwelling attenuation coefficient (K_d) and solar zenith angle (θ, varying 0–60°)). It is found that algorithmic differences in optical parameterizations have a bigger impact on the simulated temperatures in the upper-100 m of the eastern equatorial Pacific, NINO3.4 region, than other parts of the ocean. Overall, the K_dPAR-based approach estimated relatively low surface temperatures compared to those estimated from the chlorophyll-based method. In specific, this cold bias, pronounced in the upper 20–30 m, is speculated to be due to optical characteristics of the algorithm and K_dPAR products, or due to nonlinear hydrodynamical processes involving displacement of mixed-layer depth. Comparisons between each experiment against Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004) analyses find that KparCLM-based simulations have lower mean differences and variabilities with higher cross-correlation coefficients compared to ChlaCLM- and ChlaID-based experiments.     

Developing water quality retrieval models with in situ hyperspectral data in Poyang Lake,China

Applying remote sensing techniques to develop the retrieval models and further to obtain the spatiotemporal information of water quality parameters is necessary for understanding, managing, and protecting lake ecosystems. This study aimed to calibrate and validate the retrieval models for estimating the concentrations of chlorophyll a (C_CHL), suspended particulate matter (C_SPM), and dissolved organic carbon (C_DOC) with the in situ hyperspectral measurements in Poyang Lake, China in 2010 and 2011. The model calibration and validation results indicated that: (1) for C_CHL retrieval, significantly strong and moderate correlations existed between the measured and estimated values (with the correlation coefficient r = 0.92 and r = 0.76) using the exponential model and the three-band model, respectively, with biased estimation observed for the exponential model; (2) for retrieving C_SPM, there was a strong correlation between the measured and estimated values (r = 0.95) using the exponential model; and (3) no significant correlation between measured and estimated C_DOC values was found with our developed models. More work is needed to allow the water quality of Poyang Lake to be accurately and steadily estimated, especially for C_CHL and C_DOC.     

The influence of wind speed on infrared temperature in impervious surface areas based on in situ measurement data

Wind perturbations can cause a relatively rapid decay in infrared temperature, thus resulting in abnormal spatial patterns of infrared temperature in urban areas and the subsequent reduction in the reliability of infrared temperature measurements. To increase the reliability of such measurements, the effects of wind speed must be evaluated and removed. However, studies on the quantitative estimation of wind speed effects on infrared temperature are limited. In this study, in situ infrared temperature measurements and synchronous meteorological data were used to evaluate the influence of wind speed on in situ infrared temperature measurements of impervious surfaces. Five different impervious surfaces were selected in this study. The technical schemes are proposed for quantitative estimation of wind speed effects: (1) the residual-based method from the diurnal temperature cycle model was proposed to estimate the infrared temperature decay (ITD) due to wind fluctuations; (2) quantile regression method was introduced to define the relationship between wind speed fluctuations and the ITD; and (3) An improved probabilistic prediction interval as well as a ratio method were developed to estimate the magnitude and duration of the ITD. The results indicated that relative extreme wind speed (EWS) was significantly correlated with the range of ITD over 5-min intervals; the hourly decay rate and impact duration of ITD varied with changes in relative wind speed and impervious surface type; and the impact duration of ITD increased with an increase in the relative EWS and lasted more than 1.3 h for the studied impervious surfaces. The above findlings provide us a guidance for in situ measurement of infrared temperature and could be utilized for correcting thermal infrared images.     

Noise-signal index threshold: a new noise-reduction technique for generation of reference spectra and efficient hyperspectral image classification

Reference spectra of terrestrial targets are usually collected using field spectro-radiometers for mineral abundance mapping and target detection. These spectra often have noise that masks characteristic absorption and reflection features and affects the efficiency of material mapping. This work aims at obtaining an empirical technique for reduction of high-frequency noise from field spectra. The proposed noise correction technique uses a ‘normalized’ measure R_n , where R_n  = (L_n  ? F_n )/L_n for each band (n) calculated from field and laboratory spectra of test material, with F_n and L_n being the depth of the absorption feature in field and laboratory spectra, respectively. On the basis of the assumption of the constancy of this ratio in neighbouring bands, an empirical algorithm that approximates the ratio R_n of a noisy band to the corrected ratio of an adjacent band is used to obtain the noise-corrected field spectra. The classification accuracy increases significantly when noise reduced field spectra are used as reference spectra.