Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015     

《International Journal of Applied Earth Observation and Geoinformation》2020

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.  相似文献   

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

《International Journal of Applied Earth Observation and Geoinformation》2020

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.  相似文献   

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity     

《International Journal of Applied Earth Observation and Geoinformation》2020

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.  相似文献   

融合多源时空数据的城市火灾危险性评估     

赵晓旭 《测绘通报》2020,(5):101-106

城市层面的火灾风险评估主要包括火灾危险性、危害性及救援能力等方面。本文选取火灾危险性评估进行针对性研究,在大数据思维的指导下,以相关关系代替因果关系,采用多源数据对评估指标权重、分值进行率定,得出福州市城区火灾危险性时空分布图。首先利用高德地图API对消防历史出警记录进行地址解析,将近万条火灾出警地址空间落点,获得福州市历史火灾空间分布;然后综合城市用地性质现状、用地开发性质、人口分布热力图等多源异构数据,探索其与历史火灾空间分布的相关性;最后以福州城区为例,初步实现具有充分数理支撑的火灾危险性评估方法,形成火灾危险性动态评估成果,为城市消防规划等提供支撑和依据。  相似文献   

多重加密格网内插法坐标转换   总被引：1，自引：0，他引：1  

李旺民  李秀龙  王东阁  马德富  陈文相  刘亚楠 《测绘通报》2020,(10):118-122

我国CGCS2000坐标系启用后,各地均面临着大批量、多类型、多坐标系成果数据坐标转换的需求,而国家大地坐标系、地心坐标系及独立坐标系之间的转换参数属于绝密级别,保密要求高,因此给实际应用带来了困难。顾及与参数法坐标转换成果的一致性及数据的安全要求,本文提出了多重加密格网内插坐标转换方法,研制了适用于多种数据格式的二/三维格网坐标转换软件,并将该算法应用于CORS服务,通过实例验证了该方法的可靠性与实用性。  相似文献   

利用多元回归分析反演西南印度洋区域海底地形     

范雕  李姗姗  杨军军  孟书宇  邢志斌  张驰  冯进凯 《测绘学报》2020,49(2):147-161

针对海底地形与重力异常和重力异常垂直梯度在相应频段呈现强线性相关的特点,引入多元回归分析技术,提出并详细推导了联合多元重力数据的海底地形建模方法。然后,在西南印度洋SWIR(Southwest India Ridge)所在部分海域开展了海底地形反演试验及地形地貌分析研究。试验结果表明:6种海深模型中,基于多元回归分析技术构建的海深模型(BDVG模型)检核精度最高,相较于S&S V18.1模型和ETOPO1模型精度分别提高了11.51%和57.81%左右;2000 m以上水深海域,各个海深模型的检核精度较高,相对误差波动较小,反映了深海海域具有良好的反演效果;地形起伏剧烈海域或者浅海海域,BDVG海深模型,相较于以重力异常和重力异常垂直梯度作为单一输入源建立的BDG模型和BVGG模型相对误差及相对误差波动变化较小,反映了BDVG模型拥有更好的稳定性,从而体现了联合反演的必要性和优势。Indomed FZ—Gallieni FZ上唯一轴部缺失裂谷洋脊段(27洋脊段)目前属于岩浆供应充足阶段,构造作用的海底扩张对其影响较小;同时由于对称裂离方式影响,27洋脊段沿轴南北对称分布有地形隆起。  相似文献   

日光诱导叶绿素荧光辐射传输模型研究进展     

詹春晖  章钊颖  张永光 《遥感学报》2020,24(8):945-959

日光诱导叶绿素荧光(SIF)是指示植被光合作用过程的无损探针,在不同时空尺度上对植被进行SIF的观测可以反映植被的实际光合作用及生理状态。然而在观测、分析和利用SIF的过程中,仍存在很多不确定因素。SIF的发生具有较为复杂的机理,从机理出发理解SIF与植被结构的相互作用,并分析影响SIF激发的主要因素将有助于更好地理解SIF与光合作用以及生物量的内在联系。因此,植被SIF辐射传输模型在解释和利用SIF遥感信号方面具有重要的作用。植被SIF信号相对较弱,且受环境、植被和生理等多种因子的影响,需要定量化描述,这为SIF辐射传输模型的构建带来挑战。近年来,大量学者已经发展一系列SIF辐射传输模型,为SIF遥感的发展提供了坚实的理论基础。本文回顾了叶片、冠层和生态系统尺度的SIF模型,从建模机理出发,对比模型优劣势,并对未来SIF模型的发展前景进行了展望。  相似文献   

基于广义回归神经网络的全球表层海水1°×1°二氧化碳分压数据推演     

钟国荣  李学刚  曲宝晓  王彦俊  袁华茂  宋金明 《海洋学报》2020,42(10):70-79

表层海水二氧化碳分压是评估海洋碳源汇强度的关键参数,但其实测数据较少、时空分布极不均匀,导致二氧化碳交换通量的估算有很大的不确定性,海洋源汇特征就不能确切获取。为了解决这个难题,在收集的表层大洋二氧化碳地图(Surface Ocean CO₂ Atlas,SOCAT)实测数据集基础上,运用广义回归神经网络建立二氧化碳分压与经纬度、时间、温度、盐度和叶绿素浓度间的非线性关系,构建了1998?2018年间全球1°×1°经纬度的表层海水二氧化碳分压格点数据,其标准误差为16.93 μatm,平均相对误差为2.97%,优于现有研究中的前反馈神经网络、自组织映射神经网络和机器学习算法等方法。根据构建的数据所绘制的全球表层海水二氧化碳分压的分布与现有研究有较好的一致性。  相似文献   

一种利用浮标站资料改进海浪模式有效波高预报的方法     

龚 《海洋预报》2020,37(1):50-54

基于浮标站海浪历史数据,利用回归分析方法建立了海浪数值模式有效波高预报产品的一元二次回归方程订正统计模型。通过2017年7月1日-2018年10月10日期间业务试运行结果发现:订正方程能有效改善有效波高数值预报产品的预报精度,且预报时效越短订正效果越显著。其中,第6~11 h预报时效内的订正前后平均绝对误差值减小0.17~0. 241 m,第6~18 h预报时效内订正前后均方根误差减小幅度为0.103~0. 28 m。这说明应用订正统计模型对海浪模式输出产品进行订正,也是改进海浪模式预报准确率的一种有效途径。  相似文献   

东川式铜矿的成矿作用及后期叠加改造:来自硫化物原位硫同位素的制约   总被引：2，自引：1，他引：1  

殷学清  林海涛  苏治坤  赵新福 《矿床地质》2021,40(1):34-52

沉积岩型层状铜矿床(SSC型)的成因争论聚焦在成矿作用主要集中在沉积成岩期并可能叠加有后期成矿作用,还是形成于成岩后盆地闭合过程和造山作用有关.产于扬子板块西缘的东川式铜矿是中国SSC型矿床的典型代表,这些矿床赋存在晚古元古界东川群岩石中,主要呈层状矿体产出,但也存在少量脉状矿体.文章选择东川铜矿田内因民、汤丹和滥泥坪...  相似文献