Object-oriented Natural and Artificial Oasis Distinguishing in Landsat Imagery: Taking Minqin Oasis as an Example

Taking Minqin Oasis in the downstream area of the Shiyang River Basin which is located in the east of Hexi Corridor as an example, the Landsat 8 OLI image was chosen as the data source. Under the consideration of the basic concept of the artificial oasis and natural oasis in this paper, combining with the information of the spectrum, texture, shape and context basing on the image data preprocessing and multi-scale segmentation, we introduce a series of indexes such as NDVI、maximum difference, compactness, shape index, the space adjacency relation and so on to construct a rule set for distinguish between natural oasis and artificial oasis. The obtained results were further compared with the results based on the maximum likelihood method. As a result, the total accuracy of using the object-oriented image analysis method to distinguishing between natural oasis and artificial oasis is 91.75%, and the Kappa coefficient is 0.65 by using the rule set established in this paper. Compared with the results based on the maximum likelihood method, the overall accuracy is improved by 10.40% and the Kappa coefficient is 0.13. The Kappa coefficient of the artificial oasis is increased by 0.19, and the Kappa coefficient of the natural oasis condition is increased by 0.30. The results showed that the object-oriented image analysis method can overcome the limitations of the classification method that only using spectral feature to a certain extent, avoid the confusion caused by the phenomenon of “same object with different spectrums” and “same spectrum with different objects”, and increase the accuracy of distinguishing between the artificial oasis and natural oasis.     

青藏高原1982~2015年FPAR时空变化分析

植被吸收利用太阳光合有效辐射比率反映了植被固碳释氧能力,根据青藏高原GIMMS NDVI3g（1982~2015年）和MODIS NDVI（2001~2015年）数据,采用非线性半理论半经验模型进行FPAR反演及时空变化分析。结果表明：①2001~2015年GIMMS NDVI3g和MODIS NDVI反演FPAR在空间分布上具有较高的一致性,相关系数为0.82（P<0.01）,年际变化趋势一致至少6年的区域占80%;②青藏高原FPAR受坡度和海拔影响较大,其中15~35坡度FPAR变化最快,700~2 100 m海拔区间FPAR值最大;不同坡向对应的FPAR除南坡方向偏低外其他方向差异不大。③1982~2015年青藏高原四季FPAR时空变化研究中,冬季FPAR年际变化最明显,约78.5%的区域表现为增长趋势;秋季FPAR下降区域最多,但超过71.5%区域变化不显著;④基于MODIS NDVI和GIMMS NDVI两数据反演的所有植被类型的FPAR都在2012年间出现小幅度下降趋势,且不同植被类型FPAR的年际变化趋势各不相同。     

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.     

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...     

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R² = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R² = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R² = 0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.