Changes of multiple cropping in Huang-Huai-Hai agricultural region,China

Multiple cropping index (MCI) is the ratio of total sown area and cropland area in a region, which represents the regional time intensity of planting crops. Multiple cropping systems have effectively improved the utilization efficiency and production of cropland by increasing cropping frequency in one year. Meanwhile, it has also significantly altered biogeochemical cycles. Therefore, exploring the spatio-temporal dynamics of multiple cropping intensity is of great significance for ensuring food and ecological security. In this study, MCI of Huang-Huai-Hai agricultural region with intensive cropping practices was extracted based on a cropping intensity mapping algorithm using MODIS Enhanced Vegetation Index (EVI) time series at 500-m spatial resolution and 8-day time intervals. Then the physical characteristics and landscape pattern of MCI trends were analyzed from 2000–2012. Results showed that MCI in Huang-Huai-Hai agricultural region has increased from 152% to 156% in the 12 years. Topography is a primary factor in determining the spatial pattern dynamics of MCI, which is more stable in hilly area than in plain area. An increase from 158% to 164% of MCI occurred in plain area while there was almost no change in hilly area with single cropping. The most active region of MCI change was the intersection zone between the hilly area and plain area. In spatial patterns, landscape of multiple cropping systems tended to be homogenized reflected by a reduction in the degree of fragmentation and an increase in the degree of concentration of cropland with the same cropping system.     

Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves

Green-leaf phenology describes the development of vegetation throughout a growing season and greatly affects the interaction between climate and the biosphere. Remote sensing is a valuable tool to characterize phenology over large areas but doing at fine- to medium resolution (e.g., with Landsat data) is difficult because of low numbers of cloud-free images in a single year. One way to overcome data availability limitations is to merge multi-year imagery into one time series, but this requires accounting for phenological differences among years. Here we present a new approach that employed a time series of a MODIS vegetation index data to quantify interannual differences in phenology, and Dynamic Time Warping (DTW) to re-align multi-year Landsat images to a common phenology that eliminates year-to-year phenological differences. This allowed us to estimate annual phenology curves from Landsat between 2002 and 2012 from which we extracted key phenological dates in a Monte-Carlo simulation design, including green-up (GU), start-of-season (SoS), maturity (Mat), senescence (Sen), end-of-season (EoS) and dormancy (Dorm). We tested our approach in eight locations across the United States that represented forests of different types and without signs of recent forest disturbance. We compared Landsat-based phenological transition dates to those derived from MODIS and ground-based camera data from the PhenoCam-network. The Landsat and MODIS comparison showed strong agreement. Dates of green-up, start-of-season and maturity were highly correlated (r 0.86-0.95), as were senescence and end-of-season dates (r > 0.85) and dormancy (r > 0.75). Agreement between the Landsat and PhenoCam was generally lower, but correlation coefficients still exceeded 0.8 for all dates. In addition, because of the high data density in the new Landsat time series, the confidence intervals of the estimated keydates were substantially lower than in case of MODIS and PhenoCam. Our study thus suggests that by exploiting multi-year Landsat imagery and calibrating it with MODIS data it is possible to describe green-leaf phenology at much finer spatial resolution than previously possible, highlighting the potential for fine scale phenology maps using the rich Landsat data archive over large areas.     

夏玉米生长过程曲线重建研究——以鹤壁市为例

增强型植被指数(EVI)时间序列数据(即植被生长曲线)是整个生育期内植被各种生物学特征的综合反映。由于太阳位置、大气、地表和传感器位置与性能等的影响,根据遥感数据计算的EVI值往往比实际值偏低(存在大量噪声),并不能反映植被生长的真实情况,应用前需进行去噪重建工作。针对目前生长曲线重建研究大多是针对MODIS等国外遥感数据的情况,在综合分析重建方法的基础上,利用风云3号卫星的MERSI中分辨率遥感卫星数据构建鹤壁市夏玉米的EVI生长过程曲线。首先,用最大值合成法(MVC)对原始EVI时间序列数据进行初步的去云处理。接着,利用基于时间域的Savitzky-Golay滤波(简称SG滤波)对该EVI序列进行进一步的平滑去噪处理,结果发现,在噪声点EVI数值提高了,但同时在其他不是噪声点的地方EVI的值降低了。针对这种不合理的情况,利用基于SG迭代滤波取上包络线的改进方法进行处理,很好地克服了上述缺陷,在非噪声点EVI数值适当提高,且曲线平滑,达到了生长曲线重建的目的。然后,采用基于频率域的小波变换方法进行实验对比,结果发现,小波变换存在着与经典SG滤波类似的缺陷,而且在曲线末端存在突变情况。经过比较分析发现,针对研究区的实际情况,改进SG迭代滤波是较优的去噪方法。     

应用时间序列EVI的MERSI多光谱混合像元分解

针对风云3数据的特点,本文将EVI生长曲线引入多光谱混合像元的分解。首先,利用Landsat8 OLI影像,采用支持向量机的分类方法,提取研究区域的耕地信息,利用该信息对风云MERSI数据进行掩膜处理,获得研究区域的耕地影像。接着,利用MERSI时序影像,计算像元EVI值,通过SG滤波,构建农作物(端元)和混合像元的EVI生长曲线。通过实地调查,获取研究区的农作物端元,尤其对主要的农作物玉米,在空间上均匀选取了14个端元。然后,采用传统的方法,将14种玉米端元生长曲线分别与其它端元组合,进行混合像元分解。发现分解的效果差异很大,提取的玉米种植面积从191.90 km2到574.83 km2不等。为提高分解精度,借用光谱匹配(光谱夹角最小)的方法(用生长曲线代替光谱曲线)自适应选择与混合像元EVI曲线最相似的玉米端元作为组合端元,进行混合像元分解。结果得到玉米的种植面积为589.95 km2,比传统方法的最好(相对)精度提高了2%。     

基于MODIS资料的宁夏LST反演方法新探索

为快速、宏观、全面地获取陆面生态重要参数陆面温度(LST),避免分裂窗算法中诸多参数的估计和参数的适用范围限制,加快计算速度,更好地利用中国气象局"三站四网"的建设成果,利用宁夏2005-2007年13个时次过境晴空地表MODIS资料及对应过境时17个自动气象站观测数据,筛选、优化引入对LST影响较大的水汽通道、NDVI和EVI参数,建立基于MODIS遥感和地面自动气象站观测数据反演陆面温度(LST)的统计模式.研究结果表明:引入相关参数后,宁夏各季及全年模式的相关性和精度有较大提高,且水汽通道和EVI的参数组合最优.与分裂窗算法相比,省去了对大气透过率的估算以及对地表比辐射率估计的繁琐计算,与地面自动站观测真实值误差70.1%能够控制在4.0℃以内,计算速度快,能够满足一般业务的需求,易于推广使用.     

一种新的遥感影像变化检测相对辐射校正方法

分析了相对辐射校正在变化检测中的重要性,以基于植被指数的变化检测为例,比较常用相对辐射校正方法及其对变化检测的影响,并且提出一种基于相关系数稳健的相对辐射校正的新方法。通过试验发现相对辐射校正能够减小多时相遥感图像间由于大气、照度和传感器标度等存在差异而造成的影响,提高了基于植被指数变化检测的精度。自动稳健的相对辐射校正方法能够减少辐射误差,提高植被变化检测的精度,与传统的方法相比具有结果稳定、不容易受到误差干扰的特点。     

基于MODIS 数据的长江三角洲地区土地覆盖分类

长江三角洲地区是我国经济最发达的地区之一, 人类活动对自然环境产生了很大影响。为了研究该地区人类活动与生态环境的相互作用, 利用250 m 分辨率MODIS 数据进行土地 覆盖制图研究, 采用的主要数据为增强型植被指数EVI 数据、反射率数据和DEM 数据。通过基于时间序列的滤波方法消除EVI 的噪声, 通过PCA 变换压缩数据量, 并计算均质度来表征空间维的纹理信息, 构造了一个综合性的分类数据矩阵, 依据高分辨率影像选取了训练区, 采用最大似然法进行分类, 并采用缓冲区分析技术进行分类修正, 得到长江三角洲地区的土地覆盖分类结果。利用高分辨率影像解译信息对分类结果进行了精度评价, 并将分类结果与 MODIS 土地覆盖产品进行了对比, 精度分析表明分类结果很好的反映了研究区的土地覆盖信息, 显示了本研究分类方法与技术处理在实践中的可行性及250 m 分辨率EVI 时间序列数据在区域尺度土地覆盖分类方面的优势与潜力。     

An effective approach for gap-filling continental scale remotely sensed time-series

The archives of imagery and modeled data products derived from remote sensing programs with high temporal resolution provide powerful resources for characterizing inter- and intra-annual environmental dynamics. The impressive depth of available time-series from such missions (e.g., MODIS and AVHRR) affords new opportunities for improving data usability by leveraging spatial and temporal information inherent to longitudinal geospatial datasets. In this research we develop an approach for filling gaps in imagery time-series that result primarily from cloud cover, which is particularly problematic in forested equatorial regions. Our approach consists of two, complementary gap-filling algorithms and a variety of run-time options that allow users to balance competing demands of model accuracy and processing time. We applied the gap-filling methodology to MODIS Enhanced Vegetation Index (EVI) and daytime and nighttime Land Surface Temperature (LST) datasets for the African continent for 2000–2012, with a 1 km spatial resolution, and an 8-day temporal resolution. We validated the method by introducing and filling artificial gaps, and then comparing the original data with model predictions. Our approach achieved R² values above 0.87 even for pixels within 500 km wide introduced gaps. Furthermore, the structure of our approach allows estimation of the error associated with each gap-filled pixel based on the distance to the non-gap pixels used to model its fill value, thus providing a mechanism for including uncertainty associated with the gap-filling process in downstream applications of the resulting datasets.     

基于MODISEVI序列典型指标组合的土地覆盖分类研究

以2008年经Savitzky-Golay滤波技术平滑处理的MODIS EVI时间序列数据为数据源,采用支持向量机分类法对年EVI矢量模、年EVI最大值和年EVI最小值3个指标组成的影像进行土地覆盖分类,并利用验证样本对分类结果进行精度评价.试验结果表明:采用选用的3个指标不仅能减少分类的数据量、加快分类速度,而且还能...     

美国南达科他州Black Hills国家森林公园2000年森林火灾后植被恢复过程研究(英文)

森林火灾在景观上往往造成不同程度的森林冠层损失,而冠层影响光合作用和蒸散,因此刻画灾后森林冠层恢复的轨迹对于了解生态系统过程具有重要意义。森林冠层的损失和恢复通常采用叶面积指数(LAI)或其它能够反映冠层光合能力的植被指数进行表征。本研究中,我们采用Terra卫星搭载的中分辨率成像光谱仪(MODIS)的长时间序列影像(2000-2009年)来重建火灾后森林冠层恢复的过程。以美国南达科他州布莱克山国家森林公园(The Black Hills National Forest, South Dakota)为例,该地区在2000年8月24日经历了一次大的自然火灾,烧毁了近33 785 ha森林,其中大部分是美国黄松林。基于LAI的研究表明,植被冠层光合能力在3年内(2001-2003年)基本恢复,这主要来自于林下未烧毁草地在灾后的快速生长;火烧迹地的NDVI和EVI在这3年内也呈现恢复的态势。可见,LAI、NDVI和EVI在火灾几年之后便难以有效地识别火烧迹地。然而,陆地表面水分指数(基于近红外和短波红外波段的遥感标准化指数,简称LSWI),能够有效地识别和追踪火烧迹地至今的整个过程(2000-2009年)。这一研究结果也使得采用其它具有近红外和短波红外波段的传感器研究森林火灾迹地恢复和干扰过程成为可能,其中包括Landsat 5 TM影像(可追溯至1984年)。更长时间序列的数据对于研究森林火灾灾后生态系统干扰和恢复过程、森林演替模拟以及碳循环具有重要的支撑作用,LSWI指标证明能够有效地刻画这一过程。