Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.     

Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots,UAV-LiDAR data and Sentinel-2 imagery

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha⁻¹. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R² = 0.62 > 0.52, RMSE = 50.36 Mg ha⁻¹<56.63 Mg ha⁻¹). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R² = 0.62, RMSE = 51.03 Mg ha⁻¹). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.     

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.     

融合多源时空数据的城市火灾危险性评估

城市层面的火灾风险评估主要包括火灾危险性、危害性及救援能力等方面。本文选取火灾危险性评估进行针对性研究,在大数据思维的指导下,以相关关系代替因果关系,采用多源数据对评估指标权重、分值进行率定,得出福州市城区火灾危险性时空分布图。首先利用高德地图API对消防历史出警记录进行地址解析,将近万条火灾出警地址空间落点,获得福州市历史火灾空间分布;然后综合城市用地性质现状、用地开发性质、人口分布热力图等多源异构数据,探索其与历史火灾空间分布的相关性;最后以福州城区为例,初步实现具有充分数理支撑的火灾危险性评估方法,形成火灾危险性动态评估成果,为城市消防规划等提供支撑和依据。     

FY-3D/MERSI-II全球火点监测产品及其应用

FY-3D/MERSI-II全球火点监测产品主要包括全球范围内的火点位置、亚像元火点面积和火点强度等信息,可用于实时监测全球范围的森林草原火灾、秸秆焚烧等生物质燃烧状况。火点判识算法主要根据中红外通道对高温热源的敏感特性,即含有火点的中红外通道像元辐亮度和亮温较远红外通道的辐亮度和亮温偏高,同时较周边非火点的中红外像元偏高,建立合适的阈值可探测含有火点的像元。亚像元火点面积估算主要使用中红外单通道估算,根据亚像元火点面积估算结果对火点强度进行分级,不同的级别表示不同程度的火点辐射强度。基于全球火点自动判识结果,每日生成0.01°分辨率的卫星遥感日全球火点产品,每月生产0.25°×0.25°格点的全球月火点密度图。在利用FY-3D/MERSI-II火点产品开展的全球火点监测应用中,对多起全球重大野火事件进行了监测,为防灾减灾、全球气候变化研究、生态环境保护等方面提供卫星遥感信息支持。     

基于机器学习的海洋环境预报订正方法研究

结合中尺度数值模式 WRF 预报数据和 ERA5 再分析资料,利用机器学习方法对 WRF 预报场的风场、温度、气压进行预报订正。采用 ERA5 作为真值,与原始 WRF 预报相比,利用随机森林模型可以将预报结果整体均方根误差降低 44%以 上,利用深度神经网络模型可以将预报结果整体均方根误差降低 34%以上。通过随机森林模型实验得到不同输入特征对预报要素的影响程度,分析了关键的预报订正因子。     

Changes in air pollutants during the COVID-19 lockdown in Beijing:Insights from a machine-learning technique and implications for future control policy

基于2015-2020年北京35个环境空气站和20个气象站观测资料,应用机器学习方法(随机森林算法)分离了气象条件和源排放对大气污染物浓度的影响.结果发现,为应对疫情采取的隔离措施使北京2020年春节期间大气污染物浓度降低了35.1％-51.8％;其中,背景站氮氧化物和一氧化碳浓度的降幅最大,超过了以往报道较多的交通站...     

中国碳强度关键影响因子的机器学习识别及其演进

碳强度影响因子数量众多,通过在众多因子中评估其重要性以识别出关键影响因子进而解析碳强度关键因子的变化规律,是中国2030年碳强度能否实现比2005年下降60%~65%目标的科学基础。传统的回归分析方法对于评估众多因子的重要性存在多重共线性等问题,而机器学习处理海量数据则具有较好的稳健性等优点。本文从能源结构、产业结构、技术进步和居民消费等方面选取了56个中国碳强度影响因子指标,采用随机森林算法基于信息熵评估了1980-2014年逐年各项因子的重要性,通过指标数量与信息熵的对应关系统一筛选出每年重要性最大的前22个指标作为相应年度关键影响因子,最终依据关键影响因子的变化趋势划分了3个阶段作了演进分析。结果发现：1980-1991年,碳强度的关键因子主要以高耗能产业规模及占比、化石能源占比和技术进步为主;1992-2007年,中国经济进入快车道增长时期,服务业占比和化石能源价格对碳强度的影响作用开始显现,居民传统消费的影响作用在增大;2008年全球金融危机后,中国进入经济结构深化调整时期,节能减排力度大大增强,新能源占比和居民新兴消费的影响作用迅速显现。为实现2030年碳强度下降60%~65%目标,优化能源结构和产业结构,促进技术进步,提倡绿色消费,强化政策调控是未来需要采取的主要措施。     

石羊河下游人工梭梭林土壤呼吸变化特征及其与水热因子的关系

土壤呼吸不仅是反映土壤生物活性的重要指标,也是全球碳循环研究中备受关注的热点问题。在地处典型干旱区的石羊河下游,以流动沙丘和去除土壤结皮人工梭梭林为对照,采用LI-8100土壤碳通量监测系统研究了栽植约40 a、30 a、10 a和5 a的人工梭梭林生长季和非生长季的土壤呼吸日变化,并分析了土壤水分和温度对土壤呼吸的影响。结果表明：（1）不同林龄梭梭林生长季和非生长季土壤呼吸速率的日变化均为明显的单峰曲线,且呈现出一定的波动性,日最大排放速率出现在12:00～14:00时,最小值出现在8:00时左右。（2）梭梭林营造和去结皮处理显著提高了沙漠土壤呼吸速率,而且不同林龄土壤呼吸速率大体上随着种植年限的增加而递增,表现为MC >40 a>30 a>10 a>MS >5 a,非生长季表现为MC >40 a>10 a>5 a>30 a>MS。（3）不同林龄梭梭林土壤呼吸速率均具有明显的季节变化特征,生长季（8 月）的土壤呼吸作用明显强于非生长季（1月）。（4）相关性分析表明,生长季和非生长季土壤呼吸均与0～5 cm土壤水分显著相关,且均呈二次曲线关系,分别为Y =-0.205 8X ²+0.946 5X-0.316 6（R ² =0.506 ²,P= 0.041 7）和Y= 0.118 7 X ²+0.156 3X+0.118 8（R ²=0.675 7,P =0.001 1）;但与10 cm土壤温度的相关性不显著,土壤水分是影响人工梭梭林土壤呼吸的关键因素。该研究进一步证明了人工梭梭林的营造有效改善了沙漠土壤的生物活性,提高了土壤碳通量水平,以土壤结皮破坏为基本特征的人工梭梭林退化和沙漠化必然在短期内加剧碳排放。因此,需要在沙漠地区合理营造人工林,并在造林和林业管理过程中注意保护土壤结皮,以减少CO²排放。     

天山雪岭云杉个体生物量分配及其变化规律的研究

为了探讨天山雪岭云杉林生物量在个体组织中的分配情况及其变化规律,在研究区进行了大量的野外测量,利用已有的雪岭云杉林估算方程,分析了天山雪岭云杉林生物量在各器官（干、枝、叶、皮、根）中的分配及其变化规律。结果表明：（1） 研究区雪岭云杉林的平均生物量为388.74 t·hm^-2,树木各器官中,干、枝、根、叶和皮分别占生物量的43.65%、28.60%、13.49%、11.08%和3.18%。（2）各径级生物量所占百分比为：33.53%（40~50 cm）、20.13%（20~30 cm）、19.59%（30~40 cm）、18.19%（50~60 cm）和2.05%（10~20 cm）;树木生物量在不同树高中的分配表现为：48.78%（20~30 m）＞35.27%（10~20 m）＞14.70%（30~40 m）＞1.25%（0~10 m）;地上和地下生物量的分配比例为：87.54%和12.46%,分别为340.30 t·hm^-2和48.44 t·hm^-2。（3） 随海拔升高,天山雪岭云杉林生物量呈“单峰”变化,在海拔2 100~2 400 m处达到最大值611.58 t·hm^-2;干、皮生物量所占比例随海拔升高而减小,枝生物量逐渐增加,叶、根生物量呈先减小后增加的趋势;径级20~30 cm、30~40 cm和50~60 cm的生物量随海拔升高均呈“单峰型”变化趋势,都在海拔2 100~2 400 m处达到最大;雪岭云杉林不同树高生物量随海拔的升高呈现的趋势不同。天山雪岭云杉林生物量和年均降水量随经纬度的升高均呈降低变化,研究区林分生物量自西向东总体呈现逐渐降低的趋势;林分密度、海拔和降水共同决定了森林生物量的大小及其变化规律,海拔2 100~2 400 m是本研究区雪岭云杉林生长的最适宜场所。结果可为雪岭云杉林生态系统的恢复和重建提供基础资料,对研究区进行综合管理与生态健康分析具有重要意义。