Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County,North China

Soil respiration (R_s) data from 45 plots were used to estimate the spatial patterns of R_s during the peak growing seasons of winter wheat and summer maize in Julu County, North China, by combining satellite remote sensing data, field-measured data, and a support vector regression (SVR) model. The observed R_s values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 μmol CO₂ m⁻² s⁻¹ and a coefficient of determination (R²) of 0.73. No significant difference was detected between the prediction accuracy of the SVR model for winter wheat and summer maize. With forcing from satellite remote sensing data and gridded soil property data, we used the SVR model to predict the spatial distributions of R_s during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of R_s at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict R_s. Removal of this variable caused an RMSE increase from 0.31 μmol CO₂ m⁻² s⁻¹ to 0.42 μmol CO₂ m⁻² s⁻¹. Soil properties such as soil organic carbon (SOC) content and soil bulk density (SBD) were the second most important factors. Their removal led to an RMSE increase from 0.31 μmol CO₂ m⁻² s⁻¹ to 0.37 μmol CO₂ m⁻² s⁻¹. The SVR model performed better than multiple regression in predicting spatial variations of R_s in winter wheat and summer maize rotation croplands, as shown by the comparison of the R² and RMSE values of the two algorithms. The spatial patterns of R_s are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low R_s values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of R_s values on the basis of remotely sensed data and gridded soil property data at the county scale.     

基于Landsat TM和OLI数据的30年间土地利用变化及生态敏感性研究——以西安市秦岭段鄠邑区为例

对位于关中平原南缘的西安市秦岭段鄠邑区,采用Landsat TM和OLI数据,分析1990~2021年土地利用及景观格局变化特征,并选取高程、坡度、坡向、土地利用类型、植被覆盖度和水系缓冲距离6个敏感性因子,结合层次权重决策分析法（AHP法）评价生态敏感性,识别了不同生态环境敏感区空间分布格局。结果表明：1）在1990~2021年间西安市秦岭段鄠邑区土地利用变化为林地和建设用地面积分别增加了39.31 km²和4.01 km²;耕地、水域、草地和未利用土地的面积分别缩减了41.29 km²、1.16 km²、0.79 km²和0.04 km²;30年间该区土地利用变化速率较大,土地利用程度综合指数从208.48降至204.21,与退耕还林政策的实施及城市建设紧密相关。2）在斑块类型水平上,耕地和建设用地破碎化程度高,分布趋于集中连片;林地和耕地的景观主导性不断降低。在景观水平上,整体格局趋于破碎和离散,景观形状不规则性强,总体异质性和均衡性降低,说明大力发展城乡建设推动了区域景观格局的动态变化,在人为干预和规划下,当地生态环境有所恢复。3）生态敏感性由城市向外围逐渐增加,高敏感区分布在山区和水域区,低敏感区位于城镇规划区周围,不敏感区在城镇中心,是人类活动区,说明生态敏感性和环境抗压能力受人类活动的影响。本研究结果旨在为秦岭区域土地资源可持续利用、生态环境保护提供科学依据,有效防止城市建设的无序蔓延和人类活动的过度干扰,对最大限度保持自然本底具有指导意义。     

MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+

Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd.     

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r² = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions.     

The influence correction of boreal forest vegetation on SRTM data

The digital elevation model based on SRTM is very convenient for a wide range of studies but requires correction due to the influence of forest vegetation. The present study was conducted to analyse the effect of boreal forests on altitudes, aspects and slopes calculated from the SRTM. A DEM based on topographic maps at 1:100 000 scale was used as a reference. The linear regression analysis showed low data correlation in forested areas. The presence of different types of forests and felling in the woods leads to a complex distribution of deviations from the SRTM. A simple correction method was proposed, using a forest mask, built according to Landsat, and forest heights indicated on the topographic maps. After correction, the correlation coefficient between the altitudes increased by 0.05–0.14, the share of matching aspects by 1–4% and the share of matching slopes by 2–8%.     

撂荒耕地的提取与分析——以山东省庆云县和无棣县为例

由于城镇化的快速发展和农村劳动力不断流失,中国部分地区出现大面积的撂荒现象。利用遥感技术可以间接提取撂荒耕地的规模和数量,对耕地的保护和粮食安全有重要意义。以山东省庆云县和无棣县为研究区,基于Landsat数据和HJ1A数据,采用CART决策树分类方法,制作了1990-2017年的土地利用图,制定了撂荒地的识别规则,在此基础之上提取了研究区的撂荒地空间分布、持续撂荒时间分布和撂荒地复垦区域。结果显示：1990-2017年基准期影像的CART决策树分类精度高于85%;1992-2017年间,研究区撂荒地面积最大值为5503.86 hm²,最大撂荒率为5.37%,其中1996-1998年撂荒率最高,2006-2017年撂荒地面积的整体趋势逐年降低;1992-2017年间最大持续撂荒时间为15年,大部分耕地持续撂荒时间在4年之内,少数耕地持续撂荒时间超过10年;1993-2017年撂荒耕地复垦面积最大为2022.3 hm²,最小复垦面积约为20 hm²,复垦率最大值为67.44%,年均复垦率为31.83%。研究结果不仅能够为研究区撂荒驱动因素分析提供数据支撑,而且也可以为其他地区的撂荒耕地识别提供参考。     

2012-2015年中缅油气管道重要节点土地利用/覆被时空变化分析

"中缅油气管道"是我国陆上第三大能源通道,该项目的建设将改变其沿线周边的土地利用/覆盖现状,同时对沿线地区的自然环境和社会经济发展产生重要影响。本文选取中缅油气管道沿线的国内11个重要节点和缅甸境内4个重要节点作为研究区,以2012年和2015年2期Landsat7 ETM+和Landsat8 OLI影像数据作为数据源,利用决策树分类算法提取2012年和2015年2期中缅油气管道沿线重要节点的土地利用/覆被信息,分析2012年和2015年2个时期的中缅油气管道沿线15个重要节点土地利用/覆被的时空变化。研究结果表明：①15个节点中,除德宏芒市、保山隆阳区、缅甸若开邦外,其他11个节点的土地覆被变化均在20%左右;②15个节点中,最主要的土地覆被变化为植被和裸土的相互转换,其次为其他土地覆被类型向建筑的转换;③由于中缅油气管道项目的辐射作用,带动当地经济发展,改变当地的经济作物结构,因此造成大量的植被和裸土的相互转换,并造成建筑用地需求增加,出现大量的其他地表覆被类型向建筑的转换。     

内蒙古呼伦贝尔草原湖泊变化研究

内蒙古呼伦贝尔地区湖泊数量多,面积大,占内蒙古湖泊总面积的58％.近年来该地区湖泊趋于萎缩,但是已有研究主要关注大型湖泊,缺乏对该地区湖泊整体,尤其是小型湖泊(<1 km2)的研究.通过利用Landsat系列(TM、ETM+、OLI)卫星数据,参照该地区湖泊图集、湖泊名录以及Google Earth高清影像,分析了19...     

基于影像的Landsat TM/ETM~+数据正规化技术

阐述了基于影像的Landsat TM/ETM+的数据正规化技术及其发展。该技术通过将Landsat影像的亮度值转换成传感器处的辐射值和反射率来对影像进行辐射校正。实例表明,使用正规化技术处理后的影像可以明显削弱日照和大气的影响,去除它们产生的噪声;其所求的传感器处的反射率与地面实测反射率的RMS值非常小。     

山东巨野龙固煤矿地表水体范围提取设计及演变分析

高潜水位煤矿区开采后极易形成的积水区,对其进行提取设计以及演变分析具有重要科学意义。本研究基于Landsat系列影像数据,利用遥感云计算平台GEE,采用水体指数法提取巨野龙固煤矿2001—2020年的水体面积变化,同时与GSW数据集提取的水面进行辅助验证以及利用Sentinel-2数据监督分类提取水体进行精度验证,然后对其演变进行分析。结果表明：研究区20年来水体范围一直在呈稳定增长的趋势,2001—2014年呈缓慢增长趋势,2015—2020年呈现出迅速增长的趋势,造成矿区水体范围面积在2015年迅速扩大的原因主要为煤矿开采活动。