MODIS数据在黑龙江省农业上的应用

MODIS数据具有时间分辨率高、光谱范围广、更新频率快、数据产品多等众多优点。为充分利用MODIS数据,更好的为黑龙江省属地服务,该研究从MODIS1B数据出发,根据数据的特点逐一阐述该数据在黑龙江省农业旱情、长势、火点监测和冰雪覆盖等方面的应用优势。在介绍数据应用研究的原理和主要指标后,提出未来该数据在黑龙江省农业方面需要加强研究的方向。     

基于GreenSeeker的水稻氮素估测

为研究水稻植株氮素指标与GreenSeeker植被指数的定量关系,通过设置不同年份、不同氮肥水平的田间试验,于移栽后定期使用GreenSeeker获取冠层归一化差值植被指数(NDVI)和比值植被指数(RVI),并同步破坏性取样获取植株生物量和氮含量,分析不同品种和不同氮营养条件下氮素和植被指数(NDVI和RVI)变化规律,建立基于NDVI和RVI的氮素监测模型。结果表明,植株氮含量可以基于NDVI和RVI分时期进行估算,植株氮积累量可以被分阶段反演。利用GreenSeeker可以实现水稻氮素快速无损监测,为水稻氮肥精确管理提供技术支持。     

利用多源时序遥感数据提取大范围水稻种植面积

为避免大范围作物面积提取中的分区问题,提出了一种新的采用时序MODIS日反射率产品（MOD09GA）和HJ-1CCD数据的大范围水稻种植面积提取方法。利用样区多时相高分辨率HJ-1CCD数据,进行高分辨率水稻制图,获取水稻移栽期和生长期的特征。再利用时序MODIS日反射率产品自动提取移栽期稻田水分信息和生长期生长信息,实现大范围的水稻种植面积测量。以江苏省为试验区对该方法进行了试验,结果表明该方法可以较高精度的测量大范围的水稻种植面积,水稻提取精度为93.3%,Kappa系数0.88。     

基于时序归一化植被指数的冬小麦收获指数空间信息提取

为获取农作物收获指数（HI）空间分布信息,该研究充分利用遥感技术,以冬小麦为例,利用时序归一化植被指数NDVI构成的作物生长过程曲线提取MODIS NDVI阶段性累积特征参数,并用生殖生长关键阶段和营养生长关键阶段对应的NDVI累积参数比值HINDVI_SUM构建了用于反演冬小麦收获指数的参数,并建立了参数HINDVI_SUM与冬小麦实测收获指数的定量关系,利用上述定量关系实现作物收获指数空间信息的提取。经过对反演冬小麦收获指数的精度验证,结果表明,利用构建参数HINDVI_SUM在区域范围内反演冬小麦收获指数取得了较好的效果。其中,冬小麦收获指数预测的平均相对误差为2.40%,均方根误差（RMSE）为0.02,证明了该研究利用时序NDVI构建参数HINDVI_SUM反演区域冬小麦收获指数空间信息的方法准确性和可行性。     

冬小麦遥感估产回归尺度分析

将统计业务和遥感估产结合起来,以北京市统计局提供的实割实测产量数据作为野外样方,利用抽样村和地块两种尺度的实测数据,用抽样村整体回归、地块整体回归和地块分层回归3种方法进行遥感估产,将所得结果与北京市统计局发布的统计单产从不同级别进行比较分析。结果表明,利用抽样村和地块两种尺度的实测数据进行回归估产都可以得到高精度的市级单产;在区县级别上利用地块尺度的实测数据进行估产得到的区县级单产精度高于抽样村尺度;在村级上利用地块实测数据进行单产预测能够较抽样村尺度更好的反映实际单产,模型更加稳定。因此,利用地块尺度的实测产量数据建立整体回归和分层回归模型都是可行,有效的,可以得到小区域尺度高精度的单产结果。     

基于GIS和RS的中国西北NDVI变化特征及其与气候变化的耦合性

整合遥感和地理信息技术,对中国西北地区近25 a来NDVI时空变化特征及其与气候变化的耦合关系进行了研究,结果表明：高寒草甸和落叶针叶林的NDVI增加趋势较明显,线性倾向率p=0.2%/10 a。枯黄期推后导致NDVI明显增加,线性倾向率达0.27%/10 a;青藏高原、天山南脉的春季气温和北疆、汉中地区的秋季气温上升较明显,半湿润和半干旱过渡地区降水变幅较大;江河源地区NDVI和气温的相关系数达到0.6,河西-阿拉善、南疆等干旱地区NDVI和降水的相关性较高,相关系数为0.65。夏、秋季汉中、祁连和天山     

Extent and conservation of tropical dry forests in the Americas

This paper shows the results of an assessment on the current extent of Neotropical dry forests based on a supervised classification of MODIS surface reflectance imagery at 500-m resolution. Our findings show that tropical dry forests extend for 519,597 km² across North and South America. Mexico, Brazil and Bolivia harbor the largest and best-preserved dry forest fragments. Mexico contains the largest extent at 181,461 km² (38% of the total), although it remains poorly represented under protected areas. On the other hand, Brazil and Bolivia contain the largest proportion of protected tropical dry forests and the largest extent in continuous forest fragments. We found that five single ecoregions account for more than half of the tropical dry forests in the Americas (continental and insular) and these ecoregions are: the Chiquitano dry forests, located in Bolivia and Brazil (27.5%), the Atlantic dry forests (10.2%), the Sinaloan dry forests in Mexico (9.7%), the Cuban dry forests (7.1%) and the Bajio dry forests in Mexico (7%). Chiquitano dry forests alone contain 142,941 km² of dry forests. Of the approximately 23,000 km² of dry forest under legal protection, 15,000 km² are located in just two countries, Bolivia and Brazil. In fact, Bolivia protects 10,609 km² of dry forests, where 7600 km² are located within the Chiquitano dry forest ecoregion and protected by a single park. Low extent and high fragmentation of dry forests in countries like Guatemala, Nicaragua, Ecuador, Costa Rica and Peru means that these forests are at a higher risk from human disturbance and deforestation.     

Saiga antelope calving site selection is increasingly driven by human disturbance

Many terrestrial mammalian species aggregate to give birth. Such aggregations are likely to be a response to changing resource and water availability, for predator swamping and avoidance of disturbance. The critically endangered saiga antelope (Saiga tatarica) is one such species. We analysed spatio-temporal locations of saiga calving aggregations in Kazakhstan over the last four decades obtained from aerial and ground surveys, to identify the factors determining the selection of calving sites within the species’ range as well as any changes in these locations over time. Generalized mixed models were employed in a use - availability framework to assess the factors distinguishing calving from random sites and predict suitable areas for calving. Saigas selected sites, with lower than average productivity and low year to year variability in productivity, at an intermediate distance from water sources, and away from human settlements. A significant change in calving locations was observed during the last decade, with calving areas occurring further north and further away from settlements than previously. The results demonstrate that the choice of calving areas is largely driven by environmental factors. However, disturbance also has a significant impact on calving site selection and in recent decades, its influence overrides that of environmental factors. This increase in the influence of disturbance coincides with a precipitous decline in saiga numbers due to poaching, as well as substantial reductions in the intensity of land use for livestock grazing following the breakup of the Soviet Union. Predictive models based on such studies can improve species conservation by guiding the stratification of sampling for effective monitoring and deployment of rangers to protect the females at this critical time.     

基于小波融合的泉州市QuickBird影像绿地信息提取

以2006年泉州市部分QuickBird多光谱影像与全色影像为实验区数据基础，在遥感软件ERDASIMAGINE9．2平台支持下，应用小波融合算法及融合规则实现QuickBird融合处理，在此基础上进行归一化植被指数（NDVI）运算，通过人机交互式提取得到实验区5种主要的绿地类型：公园绿地、生产绿地、防护绿地、附属绿地及其他绿地．结果表明：1）QuickBird多光谱影像、全色影像的信息熵分别为3．975，4．162，而QuickBird融合影像的信息熵为7．251，明显高于前两者，融合后空间信息更丰富；2）QuickBird融合影像的平均梯度是3．328，多光谱影像、全色影像分别是1．552，2．965，融合影像纹理特征更明显；3）QuickBird融合影像与多光谱影像的偏差是0．0359，全色影像的偏差为0．0562，均接近于0，小波融合影像较好地保持了源图像的光谱特性；4）绿地信息分类提取的精度为89．6％，趋近90％．     

Exploring the impact of green space health on runoff reduction using NDVI

This study examines the impact of green space health on local flooding based on the analysis of eighty-two watersheds in four Texas metropolitan statistical areas: Dallas, Houston, San Antonio, and Austin. The runoff records in October 2007 and October 2012 were selected for the assessment. The study met the methodological challenge posed by comparison by using the Normalized Difference Vegetation Index (NDVI) datasets produced based on the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery of the 250-m resolution as a proxy to represent the health of green space. Two linear regression models were employed to explain the variation in mean daily runoff depth in 2007 and 2012, while controlling multiple contextual variables.Results indicate that watersheds containing healthier green spaces were likely to generate lower amounts of runoff in both periods. Standardized coefficients of green space health also show that the NDVI is a powerful and significant predictor to explain variation in runoff. These findings illustrate the important role of urban green spaces in attenuating local flooding and may provide planners and decision-makers with a method to consider, using this kind of objective greenery index in further developing local and regional green infrastructure and land-use plans.