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.     

遥感影像时—空融合的“点”—“线”—“面”质量评价

遥感影像时—空融合可集成多源数据高空间分辨率和高时间分辨率互补优势,生成时间连续的高空间分辨率影像,在遥感影像的动态监测与时序分析等方面具有重要应用价值.然而,现有多数研究往往基于单一数据产品对时—空融合算法进行评价,而在实际生产应用中,需要验证算法在多种遥感产品数据的融合表现;此外,目前研究大多基于“单点时刻”进行评...     

新形势下土地利用规划编制信息系统设计思路

针对上一轮规划存在的问题,提出了新形势下开发土地利用规划编制信息系统的必要性和设计思路,尤其把环境影响评价引入规划编制的过程中,为土地优化配置、合理利用土地提供了决策依据。     

粒度特征揭示的中全新世以来毛乌素沙地演化过程

通过对毛乌素沙地现代固定沙丘、半固定沙丘、半流动沙丘和流动沙丘的粒度特征分析,发现研究区沙地变化敏感粒度是>245 μm的沉积物粒度组分,尝试性地将其用于地质时期沙地演化研究。结合区域典型风成砂-古土壤剖面的沉积相特征,探讨了中晚全新世以来毛乌素沙地的时间演化过程及其特征。结果表明：中全新世以来毛乌素沙地存在多次活化扩张和固定萎缩事件,除7.5 ka BP前为流动沙地占优势以外,其余阶段多在半流动沙地、半固定沙地和固定沙地之间频繁转换,即使在全新世适宜期古土壤大规模发育期间仍存在局地短时间的沙地活化事件。     

放牧对浑善达克沙地丘间低地植被群落及土壤的影响

为探究放牧对丘间低地植被群落及土壤的影响,分析了浑善达克沙地丘间低地在重度和轻度放牧下植被的地上生物量、物种重要值、物种丰富度、Shannon-Wiener多样性指数、Pielou均匀度指数、Simpson生态优势度指数及0~10 cm土壤中黏粒、粉粒、砂粒、有机碳和全氮含量。结果表明：（1）植被地上生物量、土壤黏粒、粉粒、砂粒、土壤有机碳和全氮含量在不同放牧程度下存在极显著差异（P<0.01）,物种丰富度、Shannon-Wiener多样性指数和Pielou均匀度指数在不同放牧程度下差异不显著（P>0.05）,Simpson生态优势度指数在不同放牧程度下差异显著（P<0.05）。（2）放牧区植被以禾本科为主,禾本科植被地上生物量占整个植被群落的54.88%~57.76%;重度放牧区植被地上生物量为63.59 g·m^-2,比轻度放牧区低26.61%。（3）羊草（Leymus chinensis）和狗尾草（Setaria viridis）是放牧区的优势种,其重要值依次为14.16%（重度放牧区）和19.10%（轻度放牧区）、13.40%（重度放牧区）和15.42%（轻度放牧区）,重度放牧下雾滨藜（Bassia dasyphylla）、克氏针茅（Stipa krylovii）和猪毛蒿（Artemisia scoparia）的重要值较显著高于轻度放牧区,而羊草则低于轻度放牧区。（4）重度放牧加剧了土壤沙漠化,使砂粒含量增加,草地生产力下降,土壤有机碳和全氮含量降低。     

毛乌素沙区1961-2016年降水特征

基于毛乌素沙区10个气象站1961-2016年观测资料,应用Mann-Kendall方法和t检验法对各气象站年降水量进行了突变检验,借助小波分析讨论了各气象站年降水量的周期特征,根据降水量等值线划分结果对整个研究区分区分析了年、季、月和日尺度上的降水变化特征,并在两个时段上分析了季节性降水的差异。结果表明：毛乌素沙区年降水量空间特征差异明显,东部亚区呈上升趋势,中西部亚区呈下降趋势,但变化趋势不显著且无突变发生;降水年内分配不均,干湿季分明,降水集中在5-9月,夏秋季降水占全年降水比重大,季、月和日尺度降水量存在梯度递减变化;年降水量的年际变化过程存在多重时间尺度的自相似结构;近26年的冬春季降水增加显著,但降水波动幅度小于前30年。     

轨道交通沿线土地溢价归公的政策与实践——以东莞市为例

轨道交通周边土地开发和溢价归公有助于缓解城市财政压力,推动公交都市建设。结合国外实施溢价归公的政策和实践模式,以东莞市为例,在资料收集、政策分析、文本和案例解读的基础上,探讨了中国城市政府在规划和建设轨道交通中,以土地溢价归公反哺轨道交通融资的政策安排和实施效果。结果表明:1)溢价归公的本质是以特定方式实现正外部效应的内部化,由于土地制度和税收体系的差异,欧美地区溢价归公主要依赖多样化的土地税收体系,而中国城市的实践主要借鉴了“轨道+物业”的联合开发模式;2)东莞市溢价归公策略的形成是综合开发规划编制和审批、沿线土地控制和储备、土地发展权转移、土地增值分配等核心环节有效协调的结果;3)东莞轨道交通沿线土地开发能形成可观的财政收益,潜在土地收益占轨道资金总需求的比例超过20%,但在实施过程中仍面临较大困难。东莞市案例能为其他城市轨道交通融资、土地开发和溢价归公的政策体系编制和有效实施提供一定的参考。