Developing the framework for a risk map for mite vectored viruses in wheat resulting from pre-harvest hail damage

There is a strong economic incentive to reduce mite-vectored virus outbreaks. Most outbreaks in the central High Plains of the United States occur in the presence of volunteer wheat that emerges before harvest as a result of hail storms. This study provides a conceptual framework for developing a risk map for wheat diseases caused by mite-vectored viruses based on pre-harvest hail events. Traditional methods that use NDVI were found to be unsuitable due to low chlorophyll content in wheat at harvest. Site-level hyperspectral reflectance from mechanically hailed wheat showed increased canopy albedo. Therefore, any increase in NIR combined with large increases in red reflectance near harvest can be used to assign some level of risk. The regional model presented in this study utilized Landsat TM/ETM+ data and MODIS imagery to help gap-fill missing data. NOAA hail maps that estimate hail size were used to refine the area most likely at risk. The date range for each year was shifted to account for annual variations in crop phenology based on USDA Agriculture statistics for percent harvest of wheat. Between 2003 and 2013, there was a moderate trend (R² = 0.72) between the county-level insurance claims for Cheyenne County, Nebraska and the area determined to be at risk by the model (excluding the NOAA hail size product due to limited availability) when years with low hail claims (<400 ha) were excluded. These results demonstrate the potential of an operational risk map for mite-vectored viruses due to pre-season hail events.     

昆明市植被覆盖变化特征分析——基于Landsat遥感影像数据

以昆明市主城区及呈贡新区为研究区域,其于1992年Landsat 5 TM、2001年Landsat 7 ETM+、2014年Landsat 8遥感影像数据,运用归一化植被指数(NDVI)和植被覆盖度遥感定量模型,在ENVI 5.1、ArcGIS10.1软件的支持下,提取3个时相的植被覆盖度等级,定量分析了该地区的植被覆盖度变化情况.结果表明,昆明市主城区及呈贡新区植被覆盖等级以高植被覆盖为主;1992 ～ 2014年植被覆盖以高→中等、高→中高为主要转换类型;植被覆盖等级与环境、农业、城市建设政策息息相关.     

北京山区森林叶面积指数季相变化遥感监测

森林叶面积指数(LAI)遥感反演,对于区域环境生态监测具有重要意义。该文以北京市西北山区鹫峰国家森林公园为研究区,获取多时相Landsat5 TM数据,并利用半球形照相机(Hemispherical Photography)同步获取森林LAI。使用3种植被指数(归一化植被指数NDVI、增强植被指数EVI和三梯度差植被指数TGDVI),分别建立单个观测时期及整个时期的LAI反演模型,通过相关性分析筛选出最佳模型。研究表明利用整个时期的LAI建立的模型精度较高,其中最好的是基于NDVI的LAI指数模型。利用该模型反演森林LAI,生成基于时间序列的北京山区森林LAI分布图。该研究进一步分析了阔叶林、针叶林和混交林3种情况,结果表明,与不分植被类型的LAI反演模型精度比较,阔叶林和混交林有所提高,而针叶林稍微下降,但模型精度均达到显著水平。     

Characterizing stand-replacing disturbance in western Alberta grizzly bear habitat, using a satellite-derived high temporal and spatial resolution change sequence

Timely and accurate mapping of anthropogenic and natural disturbance patterns can be used to better understand the nature of wildlife habitats, distributions and movements. One common approach to map forest disturbance is by using high spatial resolution satellite imagery, such as Landsat 5 Thematic Mapper (TM) or Landsat 7 Enhanced Thematic Mapper plus (ETM+) imagery acquired at a 30 m spatial resolution. However, the low revisit times of these sensors acts to limit the capability to accurately determine dates for a sequence of disturbance events, especially in regions where cloud contamination is a frequent occurrence. As wildlife habitat use can vary significantly seasonally, annual patterns of disturbance are often insufficient in assessing relationships between disturbance and foraging behaviour or movement patterns.The Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) allows the generation of high-spatial (30 m) and -temporal (weekly or bi-weekly) resolution disturbance sequences using fusion of Landsat TM or ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The STAARCH algorithm is applied here to generate a disturbance sequence representing stand-replacing events (disturbances over 1 ha in area) for the period 2001-2008, over almost 6 million ha of grizzly bear habitat along the eastern slopes of the Rocky Mountains in Alberta. The STAARCH algorithm incorporates pairs of Landsat images to detect the spatial extent of disturbances; information from the bi-weekly MODIS composites is used in this study to assign a date of disturbance (DoD) to each detected disturbed area. Dates of estimated disturbances with areas over 5 ha are validated by comparison with a yearly Landsat-based change sequence, with producer's accuracies ranging between 15 and 85% (average overall accuracy 62%, kappa statistic of 0.54) depending on the size of the disturbance event. The spatial and temporal patterns of disturbances within the entire region and in smaller subsets, representative of the size of a grizzly bear annual home range, are then explored. Disturbance levels are shown to increase later in the growing season, with most disturbances occurring in late August and September. Individual events are generally small in area (<10 ha) except in the case of wildfires, with, on average, 0.4% of the total area disturbed each year. The application of STAARCH provides unique high temporal and spatial resolution disturbance information over an extensive area, with significant potential for improving understanding of wildlife habitat use.     

西双版纳热带森林景观破碎化地形差异性分析

以西双版纳2015年Landsat 8 OLI和DEM为基础研究数据,采用基于像元的分类方法,经试验对比分析筛选出了森林提取的最佳指数,运用多种景观指数对西双版纳热带雨林在海拔、坡向及坡度的破碎化程度进行分析。结果表明：以NDVI为指标,能够有效提取热带森林的空间分布(精度为95.66%);2015年西双版纳的森林盖度为77.8%,森林面积在海拔及坡度上呈现出正偏态分布,在坡向上分布较为均匀;森林景观在地形上的破碎化程度差异性较大。海拔低于500 m及平坡处的森林破碎化程度最为严重;随着海拔的上升及坡度的增加,PD值不断下降、AI值持续上升,森林破碎化程度逐渐降低。坡向上以北坡向的PD值最高、AI值最低,森林景观破碎化程度明显高于其他坡向。     

基于Landsat的高山松地上生物量动态变化估测模型构建

以香格里拉市高山松为研究对象,基于1987—2017年的国家森林资源清查固定样地和对应年的Landsat TM/OLI数据提取对应的遥感因子,计算30 a期间生物量和遥感因子的5、10 a定期变化量、年平均变化量、5、10 a变化率;采用多元线性回归（MLR）和随机森林（RF）方法构建地上生物量模型,提高高山松森林AGB动态变化遥感估测的精度。结果表明：基于5种变化量类型的RF模型效果均优于相应的MLR模型,RF模型中采用5 a变化率效果最好,其拟合R²为0.956,RMSE为0.664 t/(hm²·a),预测结果中RMSE为2.285 t/(hm²·a);纹理因子在高山松地上生物量变化量建模中贡献最大。采用遥感因子变化率构建的高山松地上生物量动态变化估测模型,提高了生物量变化的估测精度,可为森林地上生物量的动态估测研究提供参考。     

长汀县1988—2013年森林采伐时序遥感监测

利用1988—2013年Landsat TM/ETM+/OLI时间序列分类数据,根据采伐迹地的时序变化特征,提取长汀县1988—2013年森林采伐信息,并对监测结果进行精度评价。结果表明：长汀县各时相森林采伐监测的总体精度达90%以上,除1994-11-02、2001-12-31外,其余时相的kappa系数均接近或者超过0.8,利用采伐迹地特有的地表覆盖变化特征,能将光谱特征极为相近的采伐迹地、裸地、休耕期的耕地做进一步区分;长汀县采伐迹地分布最密集的区域集中在西南部,其次是东南部、东北部;长汀县1988—2001年森林采伐的面积为500~1 000 hm²,2003年以后随着森林覆盖面积持续增加,森林采伐面积增加,其中2010年森林采伐的面积超过2 800 hm²,中心区域采伐迹地面积增加与该区域森林覆盖条件显著改善有直接关系。     

基于GIS和RS的广州NDVI变化研究

以广州为研究区域，选取了1988，1993，1995，1998，2003，2005这6个时相的TM影像数据（轨道号122—44），利用遥感和地理信息系统技术相结合的方法。解译和分析植被指数NDVI的时空变化特征，以期为定量化监测城市生态动态变化提供方便的分析工具。根据TM数据计算和分析了广州、广州市区以及新白云国际机场的植被指数，进而得出区域植被指数的空间和时间变化图，并探讨其时空分布规律。结果表明，广州的NDVI变化有其特定的时空特征。总体而言，时间上表现为1995年和2005年较低，空间上表现为郊区比市区高。     

官厅水库库区水生植物演变格局时空变化特征分析

利用中等分辨率长时间序列的TM遥感影像(1995—2007年),采用面向对象的分类方法,以多尺度分割技术为核心,提取了官厅水库水生植物空间分布信息,在斑块类型和景观级别尺度上,共筛选了8个高度浓缩景观格局信息的定量指标,结合水生植物空间分布的质心变化,分析了水生植物演变格局的时空变化特征。研究表明:(1)1995—2007年,受水域面积的扩张和萎缩的影响,库区水生植物面积及其平均斑块面积呈明显地增加和减少趋势:沉水植物波动幅度比较明显,呈先小幅扩张后急剧萎缩的趋势,2000年之前,随水域的扩张,其面积增加了30.2 km2,相对动态度达116.87%;之后,随着水域面积的持续萎缩,沉水植物面积以每年4.2%呈大幅递减趋势,减少了43.02 km2;挺水植物所占比例较小,其面积呈小幅扩张趋势,增加了4.58 km2。(2)沉水植物和挺水植物随水域面积的波动呈现不同的景观格局演变特征。库区沉水植物的景观优势度高,最高达56.09%,占库区水生植物景观类型的主体;挺水植物的平均斑块面积、聚集度指数均低于同年沉水植物,具有离散度高、破碎度高、连通性低的特点。挺水植物的斑块形状相对复杂,其分维度指数平均比同年沉水植物高0.29。(3)水生植物的质心12 a来发生了不同程度的偏移变化,挺水植物质心整体向库区东北方向移动了1.88 km,偏移方向为东偏北12.78°;沉水植物质心整体向库区西南方向移动了4.66 km,偏移方向为南偏西43.04°。     

Clear-cut Detection in Boreal Forest Aided by Remote Sensing

The study compares the applicability of different remote sensing data and digital change detection methods in detecting clear-cut areas in boreal forest. The methods selected for comparisons are simple and straightforward and thus applicable in practical forestry. The data tested were from Landsat satellite imagery and high-altitude panchromatic aerial orthophotographs. The change detection was based on image differencing. Three different approaches were tested: (1) pixel-by-pixel differencing and segmentation; (2) pixel block-level differencing and thresholding; and (3) presegmentation and unsupervised classification. The study shows that the methods and data sources used are accurate enough for operational detection of clear-cut areas. The study suggests that predelineated segments or pixel blocks should be used for image differencing to decrease the number of misinterpreted small areas. For the same reason the use of a digital forest mask is crucial in operational applications.