基于亮温—植被指数—气溶胶光学厚度的MODIS火点监测算法研究

MODIS火灾产品的火点检测算法主要以4和11μm通道亮温数据来识别火点,在应用于不同地区和不同季节时有一定局限性。在分析MODIS现有火点检测算法的基础上,对算法相关阈值及参数进行适当调整,同时考虑火灾前后NDVI的变化,以及林火燃烧过程中伴生烟羽使火点下风方气溶胶光学厚度明显增加的特点,构建了基于亮温—植被指数—气溶胶光学厚度的火点识别算法,并应用多次火灾个例对本算法进行验证。结果表明:算法提高了对高温热点和低温焖烧火点的识别能力,有效降低了高温热点的误报率和低温火点的漏报率,使火点检测算法在不同环境的适应性有所增强。     

An Anti noise Identification Methodfor Winter Wheat based on Satellite Time series Data

China As one of the major crops in the world,the spatial distribution information of winter wheat plays an important role in monitoring winter wheat growth,assisting economic decision making and addressing regional food security under climate change.This paper proposed a new anti noise identification method for winter wheat identification based on the 250 m MODIS NDVI time series dataset during the period from September 30,2014 to June 26,2015.With the method,the spatial distribution of winter wheat in Henan province was extracted based on the analysis of winter wheat phenology.Results indicated that the total identification accuracy of winter wheat was 93.0%,94.0% and 86.0% for the whole study area,fragmentary land area and regular land area,respectively.Compared with the traditional identification method for winter wheat based on satellite time series data,the identification accuracies with the proposed method in different filtering scenarios were not only high but also similar to each other.It strongly proved that the new method had a good performance in noise immunity and stability and can be applied to the rapid extraction of winter wheat in a large scale based on satellite time series dataset.This new method provided a new technical support for the operational extraction of winter wheat.     

南水北调中线工程水源区植被时空演化特征

研究南水北调中线水源区植被时空分布和演化特征,对水源区生态环境保护与社会经济长期稳定发展具有重要意义。利用2010—2019年南水北调中线水源区中分辨率成像光谱仪(MODIS)归一化植被指数(NDVI)数据,基于NDVI最大值合成和一元线性回归方法研究水源区植被时空演化特征,并结合水源区地表温度、降水与土壤湿度等数据探讨植被变化原因。结果表明:①水源区NDVI空间分布与地形强相关,2010—2019年,水源区NDVI均值为整体上升趋势;②2013—2015年水源区NDVI呈现显著的“下降-恢复”特征,可能与2014年水源区地表温度和降水异常导致的旱情、2015年水源区降水恢复以及丹江口水库蓄水水位上升有关;③2010—2019年,水源区植被发生显著变化的区域主要集中在低海拔的人口密集区,反映这些地区的植被更易受气候变化、城镇扩张和“退耕还林、植树造林”政策的影响。此外,南水北调中线工程通水后,丹江口水库水位上升是导致水库区域NDVI降低的原因。研究结果可为南水北调中线水源区生态环境保护提供科学依据。     

Satellite passive microwave remote sensing for monitoring global land surface phenology

Vegetation phenology characterizes seasonal life-cycle events that influence the carbon cycle and land-atmosphere water and energy exchange. We analyzed global phenology cycles over a six year record (2003-2008) using satellite passive microwave remote sensing based Vegetation Optical Depth (VOD) retrievals derived from daily time series brightness temperature (T_b) measurements from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and other ancillary data inputs. The VOD parameter derives vegetation canopy attenuation at a given microwave frequency (18.7 GHz) and varies with canopy height, density, structure and water content. An error sensitivity analysis indicates that the retrieval algorithm can resolve the VOD seasonal cycle over a majority of global vegetated land areas. The VOD results corresponded favorably (p < 0.01) with vegetation indices (VIs) and leaf area index (LAI) information from satellite optical-infrared (MODIS) remote sensing, and phenology cycles determined from a simple bioclimatic growing season index (GSI) for over 82% of the global domain. Lower biomass land cover classes (e.g. savannas) show the highest correlations (R = 0.66), with reduced correspondence at higher biomass levels (0.03 < R < 0.51) and higher correlations for homogeneous land cover areas (0.41 < R < 0.83). The VOD results display a unique end-of-season signal relative to VI and LAI series, and may reflect microwave sensitivity to the timing of vegetation biomass depletion (e.g. leaf abscission) and associated changes in canopy water content (e.g. dormancy preparation). The VOD parameter is independent of and synergistic with optical-infrared remote sensing based vegetation metrics, and contributes to a more comprehensive view of land surface phenology.     

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

This paper discusses an assessment of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data products for detecting forest defoliation from European gypsy moth (Lymantria dispar). This paper describes an effort to aid the United States Department of Agriculture (USDA) Forest Service in developing and assessing MODIS-based gypsy moth defoliation detection products and methods that could be applied in near real time without intensive field survey data collection as a precursor. In our study, MODIS data for 2000-2006 were processed for the mid-Appalachian highland region of the United States. Gypsy moth defoliation maps showing defoliated forests versus non-defoliated areas were produced from temporally filtered and composited MOD02 and MOD13 data using unsupervised classification and image thresholding of maximum value normalized difference vegetation index (NDVI) datasets computed for the defoliation period (June 10-July 27) of 2001 and of the entire time series. These products were validated by comparing stratified random sample locations to relevant Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reference data sets. Composites of 250 m daily MOD02 outperformed 16-day MOD13 data in terms of classifying forest defoliation, showing a lower omission error rate (0.09 versus 0.56), a similar Kappa (0.67 versus 0.79), a comparable commission error rate (0.22 versus 0.14), and higher overall classification agreement (88 versus 79%). Results suggest that temporally processed MODIS time-series data can detect with good agreement to available reference data the extent and location of historical regional gypsy moth defoliation patches of 0.25 km² or more for 250-meter products. The temporal processing techniques used in this study enabled effective broad regional, “wall to wall” gypsy moth defoliation detection products for a 6.2 million ha region that were not produced previously with either MODIS or other satellite data. This study provides new, previously unavailable information on the relative agreement of temporally processed, gypsy moth defoliation detection products from MODIS NDVI time series data with respect to higher spatial resolution Landsat and ASTER data. These results also provided needed timely information on the potential of MODIS data for contributing near real time defoliation products to a USDA Forest Service Forest Threat Early Warning System.     

Quantification of aboveground rangeland productivity and anthropogenic degradation on the Arabian Peninsula using Landsat imagery and field inventory data

The productivity of semi-arid rangelands on the Arabian Peninsula is spatially and temporally highly variable, and increasing grazing pressure as well as the likely effects of climatic change further threatens vegetation resources. Using the Al Jabal al Akhdar mountains in northern Oman as an example, our objectives were to analyse the availability and spatial distribution of aboveground net primary production (ANPP) and the extent and causes of vegetation changes during the last decades with a remote sensing approach. A combination of destructive and non-destructive biomass measurements by life-form specific allometric equations was used to identify the ANPP of the ground vegetation (< 50 cm) and the leaf and twig biomass of phanerophytes. The ANPP differed significantly among the life forms and the different plant communities, and the biomass of the sparsely vegetated ground was more than 50 times lower (mean = 0.22 t DM ha^− 1) than the biomass of phanerophytes (mean = 12.3 t DM ha^− 1). Among the different vegetation indices calculated NDVI proved to be the best predictor for rangeland biomass.Temporal trend analysis of Landsat satellite images from 1986 to 2009 was conducted using a pixel-based least square regression with the annual maximum Normalized Differenced Vegetation Index (NDVI_max) as a dependent variable. Additionally, linear relationships of NDVI_max and annual rainfall along the time series were calculated. The extent of human-induced changes was analysed using the residual trends method. A strongly significant negative biomass trend detected for 83% of the study area reflected a decrease in annual rainfall but even without clear evidence of deforestation of trees and shrubs, human-induced vegetation degradation due to settlement activities were also important.     

HJ-1-CCD与MODIS数据的NDVI比较分析

利用辐射传输模型对HJ-1-CCD数据进行大气校正并反演出植被指数,和MODIS标准产品数据进行对比,并对典型区域的结果相关性和差异性进行了统计分析.结果显示:①当下垫面为相对均一的地表时,如耕地、有植被覆盖的山地、水体等,两传感器反演出的植被指数相关性(r)和相对差异分别为耕地(0.74,10％)、山地(0.69,12％)、水体(0.78,5％);②当下垫面为较为复杂的地表时,如建筑用地(含居民住宅区)等,两传感器的相关性(r)仅为0.58、相对差异达到18％;③在估算小范围区域且地表覆盖较为复杂的下垫面植被指数时,HJ-1-CCD因其相对较高的空间分辨率,能有效减弱混合像元的影响,进而提供更加丰富的植被指数信息.     

基于NDVI的人工植被碳储量估算

人工植被是吸收CO²维护生态系统健康的重要生物成分,干旱区人工碳汇林在CO²减排方面具有重要的作用。应用2009年8月TM数据,提取克拉玛依人工减排林生态景观格局信息,并应用NDVI指数估算植被碳密度。通过测定乔木层及草本层生物量,估算出人工植被乔木层及草本层碳密度。结果表明,克拉玛依人工减排林乔木层的平均碳密度值为37.04 mg/hm²,1 m×1 m样方内草本层平均碳密度为59.65 g/m²,地上植被碳密度约为37.64 mg/hm²,植被层碳储量为250 915.5 mg;随着植被的生长发育及生物量累积效应的发挥,人工植被的碳汇功能还将进一步增大。     

基于SAILH模型的植被冠层NDVI二向性分析

NDVI是植被遥感中最为常用的一种植被指数,建立NDVI与其他冠层参数模型必须考虑其方向性问题.本文基于SAILH模型讨论了连续植被冠层NDVI的二向性特征,并分析了叶面积指数、叶倾角分布、热点参数以及太阳天顶角和相对方位角对NDVI的影响.研究表明冠层NDVI在主平面观测方向存在一个明显的负热点,前向散射方向的NDV...     

Relationships between the normalised difference vegetation index and temperature fluctuations in post-mining sites

The effects of age, vegetation density (normalised difference vegetation index [NDVI]) and vegetation type on temperature fluctuations were studied in post-coal-mining sites in the Czech Republic. NDVI and surface temperature were obtained from an airborne multispectral aerial photograph and FLIR infrared camera. The difference between morning and afternoon temperature was assumed as a measure of the temperature buffering ability of the ecosystem. Buffering increased with increasing NDVI but also depended on the type of vegetation, reclaimed sites have significantly higher buffering than unreclaimed sites. In contrary to grasslands, woody vegetation gradually increased temperature buffering capacity with plot age. Topsoil application substantially increased buffering capacity.