利用差分吸收激光雷达探测二氧化碳浓度廓线

针对大气中二氧化碳(CO2)浓度在对流层分布变化明显的特点,采用激光雷达主动观测方式获取低空CO2廓线浓度.利用自主研制的基于染料差分激光技术的CO2探测差分吸收激光雷达,在中国安徽省淮南市进行了长期观测,受制于白天的低信噪比,只进行了夜间观测,并通过开发先进的信号处理算法获取了高空间、高时间分辨率的CO2廓线浓度以及...     

四川省传统村落空间分布特征及其影响因素

四川传统村落是中国传统村落的重要组成部分,承载了优秀的中国传统文化。本研究以住房和城乡建设部公布的3批共225个传统村落为研究对象,通过统计学方法与GIS技术,分析了四川传统村落的空间分布特征。研究发现:四川传统村落在空间上呈现明显的聚集型分布,分别在川西高原同纬度地带、高原与成都平原过渡带以及川南长江上游一带聚集。不同坡度、不同海拔高度及与河流的不同距离条件下,四川传统村落在空间分布上存在差异,成为影响四川传统村落的分布的重要因素。     

基于CLUMondo模型的多情景土地利用变化动态模拟——以广西沿海城市为例

使用能够深入刻画土地利用强度的CLUMondo模型,利用2010年和2015年2期土地利用数据,模拟了广西沿海城市2025年在"自然增长"、"经济发展"以及"土地利用优化"3种不同情景下的土地利用空间分布格局。结果表明:CLUMondo模型可以有效模拟沿海大尺度范围内土地系统的发展状况及轨迹;"自然增长"情景下,沿海城市土地资源的集约及有效利用进展较为缓慢;"经济发展"情景下,城市及农村建设用地增长迅速且在空间上的联系密切,区域森林及耕地保护与工业建设之间有着较为剧烈的矛盾;"土地利用优化"情景下,研究区域经济建设的步伐逐渐放缓,区域城市建设形成了资源向城镇集中、农田向规模集中的趋势。该模拟结果对于广西乃至全国沿海城市未来土地利用规划以及相关制度的制定具有一定的借鉴意义。     

平原地区航空重力测量的精度分析

为检验我国首套航空重力测量系统(CHAGS)在平原地区的测量精度以及验证其在严寒气候条件下的作业性能,2003年11月以国产某新型航测机为载体在我国东北某地区进行试验。概述试验情况,介绍航空重力测量数据处理方法。试验结果表明,CHAGS在严寒气候条件下工作稳定、性能可靠,半波长分辨率为9km时,交叉点重力异常不符值的标准差为2.0mGal,5′×5′格网平均重力异常的外部精度为1.7mGal。     

2002年—2018年太湖水体溶解二氧化碳浓度卫星遥感数据集

湖泊在全球碳循环中发挥着重要作用,而湖泊中溶解CO2浓度(CCO2)控制着其CO2通量的方向及大小,是湖泊CO2排放估算的关键参数.中国第三大淡水湖——太湖虽然具有长期的野外监测数据,但其样点分布在空间和时间上并不均匀,很可能给其CO2排放的估算带来不确定性和偏差.有必要利用更高频率和覆盖范围地遥感手段来弥补野外监测在...     

中国太阳总辐射的多元逐步回归模拟

研究采用年日照时数、年均总云量、年均水汽压、维度等常规气象要素及地理要素为解析变量,建立起估算全国范围内太阳年辐射总量的多元逐步回归模拟模型;利用全国730多个地面站1951—2002年间的常规气象观测资料,通过所构建的模型对中国太阳年辐射总量进行模拟分析,模型模拟分辨率为1km×1km,规模为4173×4847格网。模型数值计算结果显示:中国太阳辐射资源最丰富的地区为西藏自治区、青海东北部及甘肃西部边境等,高达6700MJ·m-2·a-1以上(一类地区),总面积约130万km2;最低的为四川盆地、青藏高原南部的雅鲁藏布峡谷地区,低于4200MJ·m-2·a-1(五类地区),总面积约75万km2;中国陆地表面每年接受的总太阳辐射能约为52.4×1018kJ;单位面积上太阳年辐射总量约介于2780—7560MJ·m-2·a-1;分布具有明显的地域性,呈现西北、北部高,东南、东部及东北的部分地区低的特点,模拟值较好拟合实际的太阳年辐射总量。研究所构建模型实现了太阳年辐射总量空间分布的连续模拟,模型在保证足够的模拟精度条件下,提高了运算效率,简化了模拟过程,适于全国尺度下太阳年辐射总量的数值模拟计算。     

大气CO2浓度卫星遥感进展

大气CO2是重要的温室气体,CO2浓度及其空间分布是全球气候变化评估中的主要不确定性因素之一。从1998年以来,卫星遥感大气CO2成为获取全球CO2的重要手段。本文阐述了现阶段大气CO2浓度卫星遥感反演进展情况,包括CO2探测载荷、反演算法和算法验证等。同时,论文详细介绍了近红外波段和热红外波段的反演算法特点和不确定因素,并针对CO2反演应用需求提出了展望。     

规则约束的南京市用地布局多情景模拟

本文提出一种基于规则约束的多情景土地分配模型,将城市规划领域内的法律法规提取并概括为一系列规则,加入到现有的用地布局情景模拟过程中。该模型由用地评价子模型、规则约束子模型、用地分配子模型和GIS空间数据库组成。文章以南京市为研究区进行实例验证,通过土地评定和适宜性评价、情景归纳、规则约束和情景模拟等步骤,借助GIS的空间分析功能,生成3种情景下的用地布局模拟方案,并对3种用地布局方案进行了综合分析。     

土地利用变化反事实情景模拟与评价——以湖北省嘉鱼县为例

引入了反事实情景模拟方法来模拟土地利用变化的历史,通过重构土地利用变化历史情景来辅助评价土地利用政策实施效果。以我国的耕地保护政策对粮食安全和城市扩张影响为例,构建耕地保护政策干预下的事实情景和假设无耕地保护政策影响的反事实情景,并借助CLUE-S模型进行土地利用变化情景模拟。通过比较两种模拟结果在耕地数量、产能、空间分布以及城市建设用地数量、效益和空间扩张的差异,实现对耕地保护政策实施效果的评价。实例研究表明,现行的耕地保护政策在遏制耕地流失、提高粮食产量、调整耕地及建设用地空间布局等方面取得了积极效果。     

走进白马寨

朋友．你知道白马藏人吗？他们生活在中国四川省平武县的白马藏族乡。这里地处四川盆地西北部,是青藏高原东部边缘的过渡地带、嘉陵江的分支涪江上游地区。大自然造就的气候环境,孕育出高山峡谷壮丽景观。     

Quantifying local fire regimes using the Landsat data-archive: a conceptual framework to derive detailed fire pattern metrics from pixel-level information

Free and open access to the Landsat archive has enabled the detection and delineation of an unprecedented number of fire events across the globe. Despite the availability and potential of these data, few studies have analysed residual vegetation patterns and/or partial mortality of fire across the Canadian boreal forest, and those available, are either incomplete or inaccurate. Further, they all differ in the methods and spatial language, which makes it difficult for managers to interpret fire patterns over large areas. There is an urgent need for methods to help unify fire pattern observations across the Canadian boreal forest. This study explores the capacity of the Landsat data archive when coupled with a recently developed fire mapping approach and a robust spatial language to characterize and compare tree mortality patterns across the boreal plains ecozone, Canada. With 507 fires 2.5?Mha mapped, this study represents the most comprehensive analysis of mortality patterns for study area. Summaries from this demonstration generated an accurate characterization of the fire patterns the various ecoregions based on seven key fire metrics. The comparison between ecoregions revealed differences in the amount of residual vegetation, which in turn suggested various climate, topography and/or vegetation ecosystem drivers.     

What governs the presence of residual vegetation in boreal wildfires?

Wildfires are frequent boreal forest disturbances in Canada, and emulating their patterns with forest harvesting has emerged as a common forest management goal. Wildfires contain many patches of residual vegetation of various size, shape, and composition; understanding their characteristics provides insights for improved emulation criteria. We studied the occurrence of residual vegetation within eleven boreal wildfire events in a natural setting; fires ignited by lightning, no suppression efforts, and no prior anthropogenic disturbances. Relative importance of the measurable geo-environmental factors and their marginal effects on residual presence are studied using Random Forests. These factors included distance from natural firebreaks (wetland, bedrock and non-vegetated areas, and water), land cover, and topographic variables (elevation, slope, and ruggedness index). We present results at spatial resolutions ranging from four to 64 m while emphasizing four and 32 m since they mimic IKONOS- and Landsat-type images. Natural firebreak features, especially the proximity to wetlands, are among the most important variables that explain the likelihood residual occurrence. The majority of residual vegetation areas are concentrated within 100 m of wetlands. Topographic variables, typically important in rugged terrain, are less important in explaining the presence of residuals within our study fires.     

A forestry GIS-based study on evaluating the potential of imaging spectroscopy in mapping forest land fertility

Fertility, or the availability of nutrients and water, controls forest productivity. It affects its carbon sequestration, and thus the forest's effect on climate, as well as its commercial value. Although the availability of nutrients cannot be measured directly using remote sensing methods, fertility alters several vegetation traits detectable from the reflectance spectra of the forest stand, including its pigment content and water stress. However, forest reflectance is also influenced by other factors, such as species composition and stand age. Here, we present a case study demonstrating how data obtained using imaging spectroscopy is correlated with site fertility. The study was carried out in Hyytiälä, Finland, in the southern boreal forest zone. We used a database of state-owned forest stands including basic forestry variables and a site fertility index. To test the suitability of imaging spectroscopy with different spatial and spectral resolutions for site fertility mapping, we performed two airborne acquisitions using different sensor configurations. First, the sensor was flown at a high altitude with high spectral resolution resulting in a pixel size in the order of a tree crown. Next, the same area was flown to provide reflectance data with sub-meter spatial resolution. However, to maintain usable signal-to-noise ratios, several spectral channels inside the sensor were combined, thus reducing spectral resolution. We correlated a number of narrowband vegetation indices (describing canopy biochemical composition, structure, and photosynthetic activity) on site fertility. Overall, site fertility had a significant influence on the vegetation indices but the strength of the correlation depended on dominant species. We found that high spatial resolution data calculated from the spectra of sunlit parts of tree crowns had the strongest correlation with site fertility.     

Comparison of SAR-Based Snow-Covered Area Estimation Methods for the Boreal Forest Zone

Spaceborne synthetic aperture radar data have been utilized for regional-scale snow-covered area (SCA) monitoring for several years. Different methods have been developed and demonstrated for different geographical regions. A method utilizing a single reference image for SCA estimation has been shown to function well on mountainous and nonforested regions. For the boreal forest zone, a method using two reference images and a forest compensation procedure has been previously utilized. The single-reference-image method is evaluated here for the boreal forest zone, and its performance is compared with the Helsinki University of Technology (TKK) SCA method that is specifically developed for boreal forest regions. The SCA evaluations are carried out using Radarsat-1 data for the snow-melt seasons of 2004–2007. The SCA estimation accuracies for the radar-based methods are determined using optical satellite-based SCA data as reference. The results show that SCA estimation using a single reference image is usable for the boreal forest zone, although the accuracy is significantly weaker than that of the TKK-developed boreal forest-specific SCA method. The best accuracy obtained shows a root-mean-square error (rmse) of 0.176 for the single-reference-image method and an rmse of 0.123 for the TKK SCA method.      

Sensitivity of narrowband vegetation indices to boreal forest LAI,reflectance seasonality and species composition

There is growing evidence that imaging spectroscopy could improve the accuracy of satellite-based retrievals of vegetation attributes, such as leaf area index (LAI) and biomass. In this study, we evaluated narrowband vegetation indices (VIs) for estimating overstory effective LAI (LAI_eff) in a southern boreal forest area for the period between the end of snowmelt and maximum LAI using three Hyperion images and concurrent field measurements. We compared the performance of narrowband VIs with two SPOT HRVIR images, which closely corresponded to the imaging dates of the Hyperion data, and with synthetic broadband VIs computed from Hyperion images. According to the results, narrowband VIs based on near infrared (NIR) bands, and NIR and shortwave infrared (SWIR) bands showed the strongest linear relationships with LAI_eff over its typical range of variation and for the studied period of the snow-free season. The relationships were not dependent on dominant tree species (coniferous vs. broadleaved), which is an advantage in heterogeneous boreal forest landscapes. The best VIs, particularly those based on NIR spectral bands close to the 1200 nm liquid water absorption feature, provided a clear improvement over the best broadband VIs.     

Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions

Spectral invariants provide a novel approach for characterizing canopy structure in forest reflectance models and for mapping biophysical variables using satellite images. We applied a photon recollision probability (p) based forest reflectance model (PARAS) to retrieve leaf area index (LAI) from fine resolution SPOT HRVIR and Landsat ETM+ satellite data. First, PARAS was parameterized using an extensive database of LAI-2000 measurements from five conifer-dominated boreal forest sites in Finland, and mixtures of field-measured forest understory spectra. The selected vegetation indices (e.g. reduced simple ratio, RSR), neural networks and kNN method were used to retrieve effective LAI (L_e) based on reflectance model simulations. For comparison, we established empirical vegetation index-LAI regression models for our study sites. The empirical RSR–L_e regression performed best when applied to an independent test site in southern Finland [RMSE 0.57 (24.2%)]. However, the difference to the best reflectance model based retrievals produced by neural networks was only marginal [RMSE 0.59 (25.1%)]. According to this study, the PARAS model provides a simple and flexible modelling tool for calibrating algorithms for LAI retrieval in conifer-dominated boreal forests. The advantage of PARAS is that it directly uses field measurements to parameterize canopy structure (LAI-2000, hemispherical photographs) and optical properties of foliage and understory.     

利用AVHRR植被指数数据集分析中国东部季风区的物候季节特征

利用ＮＯＡＡ／ＮＡＳＡＰａｔｈｆｉｎｄｅｒＡＶＨＲＲ陆地数据集,建立了中国东部季风区（１０８°－１２３°Ｅ,２１°－４５°Ｎ）的１９８６年归一化植被指数（ＮＤＶＩ）距平图像序列。对此数据集进行主成分分析（ＰＣＡ）,前２个主成分的时间序列和空间场展示了中国东部季风区植被物候季节性特征和地域差异。南岭一五夷山以南的华南地区,植被生长季的物候季节性变化不明显。在南岭－五夷山以北地区,植被生长季的物候季节性特征明显,可以比较清晰地确定生长季的变化过程。以淮河流域为界,植被生长季的物候季节性特征又存在明显差异。华北平原表现出强烈的双峰植被物候过程。淮河以南地区,虽然也存在这种双峰物候过程,但比较华北平原的植被,还具有持续性的植被生长特征。淮河流域构成一条区分南北物候季节特征差异的过渡带。     

南水北调中线水源区植被覆盖度遥感监测分析

南水北调中线工程是我国大规模跨流域调水工程的一部分,开展该区域植被覆盖度变化的研究与分析,对于保护该区域的生态环境及水质具有重要意义。该文以2000年和2009年两期遥感图像为本底数据,利用基于NDVI的像元二分模型对南水北调中线水源区的植被覆盖度进行了估算,并分析了该区植被覆盖度的时空变化特征。结果表明:2000年该水源区植被覆盖度的平均值为67.5%,2009年的平均值达到72%,植被覆盖度总体呈增长趋势;植被覆盖度增幅的空间特征表现为水源区中部地区高,东西部地区相对较低;在不同植被类型中,落叶针叶林的覆盖度平均值增幅最大,草地覆盖度增幅最小;位于水源区的大多数县(市)的植被覆盖度在近十年来都有不同程度的增加,其中柞水县的植被覆盖度平均值增长幅度最大,这与国家实施退耕还林、封山育林、基本农田建设等政策有关。     

西双版纳橡胶种植特点及其对热带森林景观影响的遥感研究

利用遥感技术研究西双版纳橡胶分布格局、时空动态及其对热带森林景观的影响。研究表明： 西双版纳橡胶种植既有明显的地域分布和地带分布差异特征，也有很高的时空动态度； 橡胶种植对土壤侵蚀作用不明显，但是使周边环境破碎度加大，热带森林异质性自然景观向单一人工斑块化景观发展； 西双版纳橡胶种植获得了巨大的社会效益和经济效益，也对热带森林的稳定性和多样性造成威胁。     

Spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May–September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.