High-resolution remote sensing mapping of global land water

Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China's HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment.

Distribution of Vascular Plant Species Richness Along an Elevational Gradient in the Dongling Mountains, Beijing, China

Quantifying spatial patterns of species richness and determining the processes that give rise to these patterns are core problems In blodlverslty theory. The aim of the present paper was to more accurately detect patterns of vascular species richness at different scales along altitudinal gradients In order to further our understanding of blodlverslty patterns and to facilitate studies on relationships between blodlverslty and environmental factors. Species richness patterns of total vascular plants species, Including trees, shrubs, and herbs, were measured along an altitudinal gradient on one transect on a shady slope In the Dongllng Mountains, near Beljlng, China. Direct gradient analysis, regression analysis, and geostatlstlcs were applied to describe the spatial patterns of species richness. We found that total vascular species richness did not exhibit a linear pattern of change with altitude, although species groups with different ecological features showed strong elevatlonal patterns different from total species richness. In addition to total vascular plants, analysis of trees, shrubs, and herbs demonstrated remarkable hierarchical structures of species richness with altitude （I.e. patchy structures at small scales and gradients at large scales）. Species richness for trees and shrubs had similar spatial character-Istics at different scales, but differed from herbs. These results Indicated that species groups with similar ecological features exhibit similar blodlverslty patterns with altitude, and studies of blodlverslty based on species groups with similar ecological properties or life forms would advance our understanding of variations In species diversity. Furthermore, the gradients or trends appeared to be due mainly to local variations In species richness means with altitude. We also found that the range of spatial scale dependencies of species rlchnese for total vascular plants, trees, shrubs, and herbs was relatively large. Thus, to detect the relationships between species richness with environmental factors along altitudinal gradients, It was necessary to quantify the scale dependencies of environmental factors In the sampling design or when establishing non-linear models.

Comparison of variability and change rate in tropospheric NO_(2) column obtained from satellite products across China during 1997–2015

Tropospheric NO_(2) column(TNC)products retrieved from five satellites including GOME/ERS-2(H,1997–2002),SCIAMACHY(S,2003–2011),OMI(O,2005–2015),GOME-2/METOP_A(A,2007–2013)and GOME-2/METOP_B(B,2013–2015)were compared in terms of their spatiotemporal variability and changes over China.The temporal series of H suggested an increasing trend of TNC from 1997 to 2002,those of S,O and A revealed further increasing trends until the highest level of TNC was reached in 2011,but decreasing trends were detected by those of O and B from 2011 to 2015.Seasonally,TNC was the highest in winter and the lowest in summer.Variability and changes from satellite TNC products are also analyzed in different regions of China.Spatially,it was the highest in North China and the lowest in Tibetan Plateau based on five datasets.Overall,TNCs from A,B and S were higher than that from O;and TNC from S was larger than that from A at the country level.The higher TNC the region has,the larger difference satellite products would show.However,different datasets reached a good agreement in the spatial pattern of trends in TNC with highly significant increasing trends detected in North China.

A simple error estimation method for linear-regression-based thermal sharpening techniques with the consideration of scale difference

Thermal remote sensing imagery is helpful for land cover classification and related analysis.Unfortunately,the spatial resolution of thermal infrared(TIR)band is generally coarser than that of visual near-infrared band,which limits its more precise applications.Various thermal sharpening(TSP)techniques have been developed for improving the spatial resolution of the imagery of TIR band or land surface temperature(LST).However,there is no research on the theoretical estimation of TSP error till now,which implies that the error in sharpened LST imagery is unknown and the further analysis might be not reliable.In this paper,an error estimation method based on classical linear regression theory for the linear-regression-based TSP techniques was firstly introduced.However,the scale difference between the coarse resolution and fine resolution is not considered in this method.Therefore,we further developed an improved error estimation method with the consideration of the scale difference,which employs a novel term named equivalent random sample size to reflect the scale difference.A simulation study of modified TsHARP(a typical TSP technique)shows that the improved method estimated the TSP error more accurately than classical regression theory.Especially,the phenomena that TSP error increases with the increasing resolution gap between the initial and target resolutions can be successfully predicted by the proposed method.

Applying a dual optimization method to quantify carbon fluxes:recent progress in carbon flux inversion

The widely performed Bayesian synthesis inversion method(BSIM)utilizes prior carbon flux and atmospheric carbon dioxide observations to optimize the unknown flux.The prior flux is usually computed from ecological models with large biases.The BSIM is useful in solving the problem of insufficient data,but it will increase the inaccuracies in the estimates caused by the biased prior flux.In this study,we propose a dual optimization method(DOM)to introduce a set of scaling factors as new state variables to correct for the prior flux according to information on plant functional types.The DOM estimates the scaling factors and carbon flux simultaneously by minimizing the cost function.The statistical properties of the DOM,which compare favorably with the BSIM,are provided in this article.We tested the DOM through simulation experiments which represent a true ecosystem.The results,according to the root mean squared error,show that the DOM has a higher accuracy than the BSIM in flux estimates.

Modeling Long-term Forest Carbon Spatiotemporal Dynamics With Historical Climate and Recent Remote Sensing Data

森林有长寿周期直到几百年并且更长。他们也在他们的生命周期的不同阶段有很不同的生长率。因此，在森林生态系统的碳周期有长时间规模，使考虑在估计森林里的碳水池的时间空间的动力学的森林年龄必要。这篇文章的焦点是为为估计森林碳来源和水池分发把最近的遥感数据与历史的气候数据相结合考察方法。卫星遥感在最近的十年为陆地表面提供有用数据。从遥感数据导出的信息能被用于短期的森林生长评价并且为长期的模拟为印射森林看台变老。为短期的森林生长评价，遥感能提供森林是的结构的参数输入到基于过程的模型，包括大叶，二叶，并且 multi-layered 模型。这些模型使用不同策略到从叶高级到为遥感的华盖，和他们的可靠性和适用性，应用将这里被检验。为长期的森林碳周期评价，森林生长率的空间分发(捕捞主要生产率， NPP ) 在最近的年里使用遥感数据建模是批评输入。这输入能与一张森林年龄地图被相结合在气候和大气的变化的影响下面模仿 NPP 的历史的变化。森林碳周期的另一个重要部件是在土壤的不是自养的呼吸，它取决于土壤碳水池以及气候条件的尺寸。进水池的空间分发和它的分离被描述的为估计土壤碳的方法。强调被放在怎么与与看台年龄变化和气候和大气的变化联系的森林碳动力学的考虑在当前的年里从 NPP 评价导出土壤碳水池上。在森林碳周期和在骚乱以后的森林生长的效果的骚乱的角色也在这评论被考虑。在加拿大的国家森林碳预算评价的一个例子在结束被给。它在在森林碳周期上估计国家森林碳预算和气候和大气的变化的效果说明森林看台年龄结构的重要性。