遥感法与高怠速法尾气排放状况对照检测试验研究

募集100辆点燃式发动机在用汽车进行遥感法和高怠速法尾气排放对照检测试验。结果表明,多数车辆CO遥测值比高怠速值大,HC则相反。通过对高怠速值(y)与遥测值(x)进行回归分析,得到CO的回归方程为y=0.81x+453.03,t检验显示方程具有线性(α=0.10)。如果将两种方法配合使用,可以有效提高机动车尾气排放日常管理的效果。     

湖泊富营养化遥感评价模型的建立方法

中国处于不同富营养状态的湖泊已达75%,湖泊富营养化监测已成为我国迫切需要解决的技术问题。遥感技术能够低成本地对水体实现大尺度、快速监测,实现传统点状监测到面状监测的延伸。充分考虑我国现有的湖泊富营养化评价方法、参评指标相互关系的同时,通过研究参评指标的光谱特性分析其遥感可行性,提出了从水体的叶绿素a和悬浮物浓度入手,综合评价湖泊富营养化的遥感评价方法。     

基于改进U-Net网络与联合损失函数的海南自然保护区高分辨率遥感变化检测模型

海南自然保护区遥感监测对森林资源监测、生态保护及热带亚热带地表研究具有重要意义。基于深度学习方法,针对海南省自然保护区大范围变化检测问题,对U-Net网络结构进行了改进,在每一个卷积层后加入标准化层,以跳线连接的形式将原有卷积模块改进为优化模块,同时在编码器底端添加金字塔池化模块以更好提取全局信息,形成了改进U-Net网络模型。模型训练采用基于交叉熵损失函数和广义骰子损失函数构建的联合损失函数,配合多种优化策略实现端到端的地物变化信息提取。该模型应用于公开数据集和研究构建的海南自然保护区数据集的变化检测任务,总体精度分别为97.21%(Kappa系数0.88)和95.12%(Kappa系数0.90),相比原始U-Net效果提升显著。     

环境DNA宏条形码监测水生态系统变化与健康状态

开展快速可靠的水生态监测并预测其变化趋势,对保护水生态环境具有重要的价值。近年来,环境DNA宏条形码技术(简称环境DNA技术)的快速发展弥补了传统形态学生物监测的缺陷,显著提升了水生生物群落的监测能力。与机器学习、遥感和云服务等技术结合,环境DNA技术不仅能大尺度、高频率、高灵敏度、自动化地获取生态监测信息,而且能准确地识别水生态系统的变化趋势,进而改变对水生态系统的认识与管理方式。因此,研究着重总结了环境DNA技术在水生态监测中的应用,分析了环境DNA技术与机器学习、卫星遥感等跨学科合作的潜在机遇,基于环境DNA技术简单、便捷的优势,提出了社会公民参与水环境保护的生态监测新思路。     

Comparing wildlife habitat suitability models based on expert opinion with camera trap detections

Expert knowledge is used in the development of wildlife habitat suitability models (HSMs) for management and conservation decisions. However, the consistency of such models has been questioned. Focusing on 1 method for elicitation, the analytic hierarchy process, we generated expert-based HSMs for 4 felid species: 2 forest specialists (ocelot [Leopardus pardalis] and margay [Leopardus wiedii]) and 2 habitat generalist species (Pampas cat [Leopardus colocola] and puma [Puma concolor]). Using these HSMs, species detections from camera-trap surveys, and generalized linear models, we assessed the effect of study species and expert attributes on the correspondence between expert models and camera-trap detections. We also examined whether aggregation of participant responses and iterative feedback improved model performance. We ran 160 HSMs and found that models for specialist species showed higher correspondence with camera-trap detections (AUC [area under the receiver operating characteristic curve] >0.7) than those for generalists (AUC < 0.7). Model correspondence increased as participant years of experience in the study area increased, but only for the understudied generalist species, Pampas cat (β = 0.024 [SE 0.007]). No other participant attribute was associated with model correspondence. Feedback and revision of models improved model correspondence, and aggregating judgments across multiple participants improved correspondence only for specialist species. The average correspondence of aggregated judgments increased as group size increased but leveled off after 5 experts for all species. Our results suggest that correspondence between expert models and empirical surveys increases as habitat specialization increases. We encourage inclusion of participants knowledgeable of the study area and model validation for expert-based modeling of understudied and generalist species.     

Analysis of satellite imagery using a simple algorithm supports evidence that Trichodesmium supplies a significant new nitrogen load to the GBR lagoon

This work supports previous studies in the Great Barrier Reef lagoon that show the new nitrogen (N) load introduced by Trichodesmium is similar to or greater than that from riverine discharges. However, the current management programs aimed at improving the chronic eutrophic state of the GBR ignore the N load from Trichodesmium. These programs also ignore the evidence that Trichodesmium blooms could promote the bioavailability of heavy metals and be a source of toxins in the ciguatera food chain. Further work is urgently required to better quantify the potential impacts of Trichodesmium and develop management plans to reduce those impacts. A simple algorithm that uses MODIS imagery is developed for not only monitoring the spatial extent of Trichodesmium blooms but also for quantifying the concentration of those blooms. The algorithm is based on the readily available MODIS L2 data. A management plan that includes the harvesting of Trichodesmium is outlined.     

Supporting habitat conservation with automated change detection in Google Earth Engine

A significant limitation in biodiversity conservation has been the effective implementation of laws and regulations that protect species’ habitats from degradation. Flexible, efficient, and effective monitoring and enforcement methods are needed to help conservation policies realize their full benefit. As remote sensing data become more numerous and accessible, they can be used to identify and quantify land-cover changes and habitat loss. However, these data remain underused for systematic conservation monitoring in part because of a lack of simple tools. We adapted 2 algorithms that automatically identify differences between pairs of images. We used free, publicly available satellite data to evaluate their ability to rapidly detect land-cover changes in a variety of land-cover types. We compared algorithm predictions with ground-truthed results at 100 sites of known change in the United States. We also compared algorithm predictions to manually created polygons delineating anthropogenic change in 4 case studies involving imperiled species’ habitat: oil and gas development in the range of the Greater Sage Grouse (Centrocercus urophasianus); sand mining operations in the range of the dunes sagebrush lizard (Sceloporus arenicolus); loss of Piping Plover (Charadrius melodus) coastal habitat after Hurricane Michael (2018); and residential development in St. Andrew beach mouse (Peromyscus polionotus peninsularis) habitat. Both algorithms effectively discriminated between pixels corresponding to land-cover change and unchanged pixels as indicated by area under a receiver operating characteristic curve >0.90. The algorithm that was most effective differed among the case-study habitat types, and both effectively delineated habitat loss as indicated by low omission (min. = 0.0) and commission (min. = 0.0) rates, and moderate polygon overlap (max. = 47%). Our results showed how these algorithms can be used to help close the implementation gap of monitoring and enforcement in biodiversity conservation. We provide a free online tool that can be used to run these analyses ( https://conservationist.io/habitatpatrol ).     

基于植被指数和地表温度的冬小麦遥感干旱监测

针对河北省冬小麦主要种植区2007年发生的一次春旱过程,利用MODIS8天合成遥感资料,结合地面站点数据,分析植被供水指数及其多年距平值在冬小麦生长旺期对站点和区域尺度的旱情响应,探求其在河北省冬小麦旱情日常监测中的可操作性。研究结果表明：在三个典型干旱站点,植被供水指数多年距平值与土壤相对湿度多年距平值有较为一致的时间动态;在区域尺度,植被供水指数对水分亏缺状况敏感,与20cm土壤相对湿度的相关性较好。利用植被供水指数建立的遥感监测模型可用于反演冬小麦种植区旱情空间格局,结果与地面观测资料基本吻合。     

基于Sentinel-3 OLCI影像的秦皇岛海域悬浮物浓度遥感反演

基于2013~2021年期间秦皇岛海域遥感反射率、悬浮物浓度及叶绿素a浓度等实测数据,开展了该海域Sentinel-3 OLCI影像的悬浮物浓度遥感反演模型研究.结果表明,文献中常用的典型经验模型形式均不适用于秦皇岛海域,以490、620及708.75nm为悬浮物反演的敏感波段,以560nm为参比波段,将各敏感波段与参比波段的比值作为自变量,最终建立了适用于秦皇岛海域的Sentinel-3 OLCI四波段悬浮物浓度遥感反演模型（R²=0.69,MAPE=24.79%,RMSE=2.82mg/L）;并采用2021年7月24日Sentinel-3 OLCI影像进行悬浮物浓度遥感反演产品的真实性检验,得到反演值与实测值的平均相对误差为13.24%.将上述四波段模型用于2021年1~12月秦皇岛海域的Sentinel-3 OLCI影像,反演得到月均悬浮物浓度,发现秦皇岛海域悬浮物浓度整体呈现沿岸海域高、离岸海域低,秋冬季高、春夏季低的时空变化特征;且2018~2021年秦皇岛海域悬浮物浓度的年均值逐年递减,水体越来越澄清.     

Changes in vegetation persistence across global savanna landscapes, 1982–2010

We present a global analysis of the changing face of vegetation persistence in savanna ecosystems by boreal seasons. We utilized nearly 30 years of monthly normalized difference vegetation index data in an innovative time-series approach and developed associated statistical significance tests, making the application of continuous vegetation metrics both more rigorous and more useful to research. We found that 8,000,000–11,000,000 km² of savanna have experienced significant vegetation decline during each season, while 20,000,000–23,000,000 km² have experienced an increase in vegetation persistence during each season, relative to the baseline period (1982–1985). In addition, with the exception of the March–April–May season, which is mixed, the pattern of significant vegetation persistence in the Northern Hemisphere is almost exclusively positive, while it is negative in the Southern Hemisphere. This finding highlights the increasing vulnerability of the Southern Hemisphere savanna landscapes; either resulting from changing precipitation regimes (e.g., southern Africa) or agricultural pressures and conversions (e.g., South America).