海南岛不同土地覆盖资料对WRF模拟的影响*

提取GLC2009土地覆盖数据,将其运用到WRF数值模式并与模式自带的MODIS、USGS土地覆盖进行对比,以海南岛做为研究区域,选择一次海风锋天气过程进行敏感性分析,结果表明：①GLC2009对海南岛农耕地、森林的划分最接近实际情况,在海南岛平原以及丘陵地带,MODIS和USGS划分的农耕地比例过高,而在山区森林比例偏低;②GLC2009、MODIS、USGS对海南岛北部的降水模拟出现了空报,但是GLC2009对东海岸的强降水模拟的较好,并避免了MODIS和USGS在海南西南部的降水空报的情况;③GLC2009模拟的10米风场清晰刻画了东海岸海风锋辐合,其相对合理的森林覆盖提高了地表粗糙度并增强了风场辐合、低反照率对地面气温的增加和海陆热力差异的增强有一定的促进作用。④GLC2009对这次海风锋过程中模拟的感热通量和潜热通量都要高于MODIS和USGS,这种陆气间的热通量交换利于强对流天气的触发,发展。     

FINITE ELEMENT MODELLING FOR LAND DISPLACEMENTS DUE TO PUMPING

Equations of equilibrium (force balance) and flow in multidimensions were coupled in this paper to describe land displacements due to pressure decline in aquifers. A Galerkin finite element model based on these equations was developed. The saturated/unsaturated behaviour and the isotropic/anisotropic properties of permeability and elasticity were considered when the model was formulated. This model was verified by comparing its simulation results with those of known analytical solutions for simplified cases. The simulation of displacements due to pressure decline in unsaturated media was also performed. Those results demonstrated that the choice of boundary ranges for an aquifer with infinite domain may significantly affect the estimated horizontal and vertical displacements. To obtain a good estimation of land displacements, the boundary ranges should be carefully chosen. The displacements occurring in unconfined aquifers are caused by the drop of the water table and the change in body force in the dewatering zone. Simulation results also indicated that the change in body force should be considered once an unconfined aquifer has been pumped. Otherwise, the horizontal and vertical displacements in unconfined aquifers would be overestimated and underestimated, respectively. The behaviour of land displacements due to pumping was shown to be affected by changes in the total stresses in aquifers.     

北京耕地流失的时空动态与调控途径研究

在国际学术前沿土地变化科学的概念框架下, 耕地非农化及其调控机制研究成为核心问 题之一。研究中国快速城市化地区耕地非农化的驱动力及其调控机制, 不仅是现实的迫切需求, 也可在国际相关学术领域做出独特贡献。以北京为案例进行研究发现: 1996~2004 年北京土地利 用变化中, 大多数地类转换都与耕地直接有关, 耕地流失是北京土地利用变化的核心问题; 城镇 扩展主要占用耕地, 且耕地生产力高的平原地区因建设占用而流失的耕地量大; 远郊山区耕地被 建设占用的压力相对较小, 但被生态用地占用的压力相对较大; 人口增长、经济发展、政府行为与 决策是北京等经济快速发展地区耕地流失的重要驱动因素, 解决途径关键在于寻求耕地保护与 耕地非农利用之间的平衡点、积极发挥土地管理政策的调控作用。     

海啸灾害后印度尼西亚班达阿齐市的土地覆盖变化与海岸带区域规划

2004年12月26日,由于欧亚板块的碰撞,40年以来最大的地震灾害发生在印度洋。地震诱发的海啸影响到Nangroe Aceh Darussalam省的许多城市,包括省会城市班达阿齐。在这地区共有超过12万人死亡,100万人无家可归。基于遥感数据的分析表明,有12万亩的土地受到了灾害。在班达阿齐市,鱼塘、住宅用地和保护区的变化是这一地区最显著的土地利用/覆盖变化,受灾前后这些用地类型的面积相应的变化了61.5%、57.8% 和77.6%。目前,印度尼西亚中央政府正在计划一个新的海岸带土地利用规划,在原来密集的海岸带建立一个缓冲区（约距海岸带2 km）。政府已经要求许多海岸带的社区代表与非政府组织参与到决策的过程中。 为了选择并采取最佳的土地利用方式,海啸灾害后的海岸带规划应该包括一些重要的基本要素。本研究主要关注作为该省社会经济活动中心的班达阿齐市。检测了由于海啸灾害造成的土地利用/覆盖变化（包括物理破坏）,特别是农业用地和居住区用地的变化,并且分析了受灾村落的不同类型及灾害对社会经济活动造成的影响。此外,还为政府以及当地居民在灾后的规划中选择更为可持续的空间布局方案提出了建议。     

中国土地利用/覆盖变化的生态环境安全响应与调控

中国从20世纪70年代末实施了前所未有的改革开放政策,巨大的境外投资、有偿转让使用与土地联产承包政策在广大城乡的推行,引致了快速的城市化与经济增长过程,城市化水平增长15%,GDP平均每年增长超过9%,城市化土地面积增长2%。与此相应,严重的生态环境灾害相继发生,1994年淮河特大污染事件;1997年黄河断流227天;1998年发生长江、嫩江流域特大水灾;2000年发生严重影响中国首都北京及韩国、日本的12次沙尘暴;2004年,淮河流域再次发生严重污染事件。综合分析表明,土地利用/覆盖格局的巨大变化、城市化过程的加速、乡镇企业的迅猛发展成为其主因。一些案例研究表明,重建生态环境安全条件下的中国土地利用/覆盖格局,制定与生态环境安全水平友好的国家土地利用政策,不仅有利于改善中国生态环境安全水平,还将促进其可持续发展,并有益于亚洲及世界生态环境的改良。     

1961—2016年中国西北地区陆地水分收支的年代际变化特征

利用观测气象数据集（CN05.1）、地表水文数据集（VIC-CN05.1）以及大气再分析数据（JRA55）分析了我国西北地区1961-2016年暖季（5-9月）陆地水分收支的长期和年代际变化特征。通过对陆地水分收支（Land Water Availability,LWA）时间序列作Mann-Kendall突变检验,将1961-2016年划分为3个时段进行进一步分析（P1：1961-1978年;P2：1979-2008年;P3：2009-2016年）。主要结论如下：在1961-2016年间西北地区LWA呈上升趋势,区域平均的LWA时间序列具有明显的年代际特征。三个时段的LWA距平百分率分别为-5.45%、-0.46%和13.99%,总体表现为"减少-不变-增加"的特征,尤其是近些年地表水资源增加明显。三个时段的LWA距平百分率空间分布差异显著,尤其在新疆中部、甘肃东部和陕西。西北地区总体上水汽通量输送和垂直速度与降水年代际变化特征基本一致,且区域特征明显。西北地区蒸发受降水、向下长波辐射通量和风速变化影响显著。     

The Austrian node of the natural resources satellite remote sensing cloud service platform:examples of Sino-Austrian cooperation

The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center(LASAC),Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna,Austria.Under this agreement panchromatic and multi-spectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research.So far,nearly 500 GB of data have been uploaded to the server.This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies.Some of them are already completed,others are still ongoing.They include a geometric accuracy validation of ZY-3 data,an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos,and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria.An accuracy study of DTMs from ZY-3 stereo data,as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.     

Accuracy assessment of NLCD 2011 percent impervious cover for selected USA metropolitan areas

The emergence of high-resolution land cover data has created the opportunity to assess the accuracy of impervious cover (IC) provided by the National Land Cover Database (NLCD). We assessed the accuracy of the 900 m² NLCD2011 %IC for 18 metropolitan areas throughout the conterminous United States using reference data from 1 m² land cover data developed as part of the United States Environmental Protection Agency’s EnviroAtlas project. Agreement was assessed from two perspectives: 1) sensitivity to the size of the assessment unit used for the comparison, and 2) utility of NLCD %IC to serve as a proxy for high-resolution IC. The former perspective was considered because statistical relationships can be sensitive to assessment unit size and shape, and the latter perspective was considered because high resolution (reference) %IC data are not available nationwide. The utility of NLCD %IC as a proxy for the high resolution data was assessed for seven lattice (square) cell sizes ranging from 1 ha to 200 ha using four EnviroAtlas IC indicators: 1) %IC per 100 ha (1 km²); 2) %IC by Census block group; 3) %IC within a 15 m (radius) of the riparian zone, and; 4) %IC within a 50 m (radius) of the riparian zone. Agreement was quantified as per assessment unit deviation (NLCD %IC – reference %IC) and summarized as Mean Absolute Deviation (MAD) and Mean Deviation (MD) both within and across the 18 metropolitan areas. Ordinary least squares (OLS) regression (y = reference %IC and x = NLCD %IC) was also used to evaluate the quality of the NLCD %IC data. MAD was ≤ 5% for six of the seven lattice cell sizes. MAD was also ≤ 5% for Census block groups > 100 ha and for both riparian units. These results suggest that uncertainty attributable to the measurement of %IC was no greater than the uncertainty related to the effect of IC on aquatic resources that have been derived from studies of aquatic condition (e.g., benthic fauna) over a range of %IC. Overall, agreement was variable from one metropolitan area to the next. Agreement improved as assessment unit size increased and declined as the level of urbanization (NLCD %IC) increased. NLCD %IC tended to underestimate reference %IC overall, but NLCD %IC was sometimes greater than reference %IC in urbanized settings.     

Update and spatial extension of strategic forest inventories using time series remote sensing and modeling

Up-to-date forest inventory information relating the characteristics of managed and natural forests is fundamental to sustainable forest management and required to inform conservation of biodiversity and assess climate change impacts and mitigation opportunities. Strategic forest inventories are difficult to compile over large areas and are often quickly outdated or spatially incomplete as a function of their long production cycle. As a consequence, automated approaches supported by remotely sensed data are increasingly sought to provide exhaustive spatial coverage for a set of core attributes in a timely fashion. The objective of this study was to demonstrate the integration of current remotely-sensed data products and pre-existing jurisdictional inventory data to map four forest attributes of interest (stand age, dominant species, site index, and stem density) for a 55 Mha study region in British Columbia, Canada. First, via image segmentation, spectrally homogenous objects were derived from Landsat surface-reflectance pixel composites. Second, a suite of Landsat-based predictors (e.g., spectral indices, disturbance history, and forest structure) and ancillary variables (e.g., geographic, topographic, and climatic) were derived for these units and used to develop predictive models of target attributes. For the often difficult classification of dominant species, two modelling approaches were compared: (a) a global Random Forests model calibrated with training samples collected over the entire study area, and (b) an ensemble of local models, each calibrated with spatially constrained local samples. Accuracy assessment based upon independent validation samples revealed that the ensemble of local models was more accurate and efficient for species classification, achieving an overall accuracy of 72% for the species which dominate 80% of the forested areas in the province. Results indicated that site index had the highest agreement between predicted and reference (R² = 0.74, %RMSE = 23.1%), followed by stand age (R² = 0.62, %RMSE = 35.6%), and stem density (R² = 0.33, %RMSE = 65.2%). Inventory attributes mapped at the image-derived unit level captured much finer details than traditional polygon-based inventory, yet can be readily reassembled into these larger units for strategic forest planning purposes. Based upon this work, we conclude that in a multi-source forest monitoring program, spatially localized and detailed characterizations enabled by time series of Landsat observations in conjunction with ancillary data can be used to support strategic inventory activities over large areas.     

Integration of remote sensing,county-level census,and machine learning for century-long regional cropland distribution data reconstruction

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 10⁴ km² in 1850 to 6.64 × 10⁴ km² in 1980 and then decreased to 6.16 × 10⁴ km² in 2018. Cropland expansion rate was the largest in the 1960s (749 km² yr⁻¹) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km² yr⁻¹ in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.