Fish assemblages respond to forest cover in small Amazonian basins     

《Limnologica》2020

The removal of native forest affects stream characteristics, processes, and organisms at the local scale. We compared the structure of fish assemblages between microbasins impacted by deforestation and those in pristine condition in the Amazonian Machado River basin, Brazil. Fish were collected with seine and dip nets along an 80-m stretch of 28 streams. At each site, we recorded physical, chemical, and land-water ecotone variables. We collected 6,586 specimens of 109 species, being 39 and 18 of them exclusively of forested and deforested streams, respectively. Non-significant differences were found for abundance and species richness between forested and deforested streams. A total of four main trophic groups were identified. Carnivores were more abundant in forested streams, whereas herbivores, omnivores and detritivores species were the most abundant in deforested streams. The deforested streams showed higher abundance and richness of algae and periphyton consumers, while forested streams presented higher abundance and richness of invertebrate consumers. Forested streams presented longer foodchains, higher occurrence and abundance of species that have more specialized habits and are intolerant to degraded environments, whereas generalist and tolerant species predominated in deforested streams. We conclude that species composition in Amazonian streams predictably responds to the degree of forest cover.  相似文献   

Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.  相似文献   

The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil     

《International Journal of Applied Earth Observation and Geoinformation》2020

On 25th January 2019, the tailings dam of the Brumadinho iron mine operated by Vale S/A failed catastrophically. The death toll stood at 259 and 11 people remained missing as of January 2020. This tragedy occurred three years after Mariana’s tailings dam rupture – the most significant tailing dam disaster in Brazilian history. Thus far, a systematic investigation on the cause and effect of the failure has yet to be conducted. Here, we use satellite-driven soil moisture index, multispectral high-resolution imagery and Interferometric Synthetic Aperture Radar (InSAR) products to assess pre-disaster scenarios and the direct causes of the tailings dam collapse. A decreasing trend in the moisture content at the surface and the full evanescence of pond water through time (2011–2019) suggest that the water was gradually penetrating the fill downwards and caused the seepage erosion, saturating the tailings dam. Large-scale slumping of the dam (extensional failure) upon the rupture indicates that the materials of the fill were already saturated. InSAR measurements reveal a dramatic, up to 30 cm subsidence in the dam (at the rear part) within the past 12 months before the dam collapse, signifying that the sediments had been removed from the fill. Although the information on the resistance level of the tailings dam to infiltrations is not available, these pieces of evidence collectively indicate that the seepage erosion (piping) is the primary cause for the chronic weakening of the structure and, hence, the internal “liquefaction” condition. Upon the collapse, the fully saturated mud tailings flowed down the gentle slope area (3.13 × 10⁶ m²), where 73 % were originally covered by tree, grass or agricultural tracts. The toxic mud eventually reached the Paraopeba River after travelling 10 km, abruptly increasing the suspended particulate matter (SPM) concentration and the toxic chemical elements in the river, immediately affecting the local livelihoods that depend on its water. The Paraopeba River is a major tributary of the San Francisco River, the second-longest river in Brazil reaching the Atlantic Ocean. We anticipate that the environmental repercussions of this toxic seepage will be felt throughout the entire basin, especially riverine communities located downstream.  相似文献   

锚固站对BDS自主导航精度的影响分析     

龚晓颖  蔡诗响 《测绘通报》2020,(4):68-75

介绍了自主导航的轨道确定及时间同步观测方程。以北斗仿真全星座为对象，通过采用仿真星间及卫星与地面锚固站间观测值，进行了60 d自主导航解算，分别探讨了锚固站数量及锚固站观测连续性对北斗卫星导航系统（BDS）3类卫星自主导航精度的影响。结果表明：锚固站数量及观测连续性对RERR及CERR无影响；加入1个锚固站即可显著改进URE结果精度，继续增加锚固站数量虽然可进一步提高URE精度但其改进效果有限；锚固站观测中断时间越长，其对应自主导航精度越低。因此，在BDS自主导航运行模式下应保持较高的锚固站观测频次以保证自主导航精度；另外，锚固站数量及观测连续性对北斗系统3类卫星自主导航精度的影响无显著差异。  相似文献   

基于时相变化的晴空条件下Himawari-8 AHI火点检测     

鄢俊洁  瞿建华  冉茂农  张芳芳 《遥感学报》2020,24(5):571-577

Himawari-8静止气象卫星具有高空间分辨率、高观测频次和高时效特点,对于火点检测具有很强优势。对Himawari-8卫星的3.9μm和11.2μm两通道亮温进行了连续时相变化研究,得出两通道的亮温在时间上的变化差值稳定且规律明显。根据两通道的亮温时相特征,考虑白天可见光对3.9μm通道的影响,并结合火点产生时引起的亮温变化特征,提出了适用于晴空条件下改进的火点检测算法。在多处进行了此算法的实验,例如2018-11-27 T 16:40(UTC时)河北张家口市桥东区一化工厂附近发生的严重爆炸起火事件以及2019-02-28澳大利亚西南部发生的火灾事件,均快速有效的检测到了火点。实验表明,改进的火点检测算法能很好的进行火点检测,并能解决晨昏交界、冰雪下垫面、常规火源点、太阳耀光等火点检测的难题。  相似文献   

Sentinel-2卫星落叶松林龄信息反演   总被引：1，自引：0，他引：1  

唐少飞  田庆久  徐凯健  徐念旭  岳继博 《遥感学报》2020,24(12):1511-1524

林龄结构信息能够有效反映区域森林群落不同生长阶段的固碳能力，对于评估森林生态系统的健康状况具有重要意义。本研究以中国温带典型优势树种落叶松林为研究对象，分别选择其芽萌动期、展叶期和落叶期时段的Sentinel-2影像，采用多元线性回归（MLR）、随机森林（RF）、支持向量机回归（SVR）、前馈反向传播神经网络（BP）以及多元自适应回归样条（MARS）等5种方法依次构建落叶松林龄反演模型。通过相关性分析首先确定最佳遥感反演物候期，并在此基础上根据相关性差异筛选出5个最优特征变量用于模型反演，分别为冠层含水量（CWC），归一化水体指数（NDWI），叶面积指数（LAI），光合有效辐射吸收率（FAPAR）和植被覆盖度（FVC）。研究结果表明，展叶期为落叶松林最佳遥感反演物候期。除植被衰减指数（PSRI）以及落叶期的NDVI、RVI外，落叶松林龄与各指标之间均呈负相关关系，其中与冠层含水量（CWC）的相关性最高，pearson相关系数达到-0.74（p<0.01）。此外，不同模型反演结果表明，随机森林模型（RF）为最佳落叶松林龄估测模型，其平均决定系数R²和平均均方根误差RMSE分别为0.89和2.91 a；多元线性回归模型（MLR）的林龄估测结果最差，其平均决定系数R²和平均均方根误差RMSE仅为0.57和5.69 a，非线性模型能更好的解释林龄与建模变量之间的关系。  相似文献   

广西土壤有机质空间变异特征及其影响因素研究   总被引：2，自引：0，他引：2  

钟聪  李小洁  何园燕  邱微文  李杰  张新英  胡宝清 《地理科学》2020,40(3):478-485

基于广西第二次土壤普查的270个土壤剖面资料,结合1∶50万数字化土壤类型图、土地利用类型图和气象监测数据等资料,利用地统计学和逐步回归分析等方法对广西表层土壤有机质空间变异特征及其影响因素进行了探究。结果表明：广西表层土壤有机质平均含量为3.11±2.19%,变异系数为70.72%,空间分布呈北高南低的趋势。广西表层土壤有机质空间分布受到自然和人为因素的共同影响,土壤类型、成土母质、海拔、土地利用、气候和坡度6个环境因子对全区土壤有机质含量变异的综合解释能力为47.9%。其中,土壤类型是最重要的影响因素,能独立解释其变异的36.0%,海拔和成土母质分别能独立解释28.5%和15.8%。气温对广西土壤有机质空间分布的影响比降水量更加显著,从而造成了广西土壤有机质整体呈南低北高的趋势。同时,土壤有机质对气温的敏感性在一定程度上受到降雨量的制约。此外,研究区农业耕作管理等因素对土壤有机质的影响也不容忽视。  相似文献   

海南省连片贫困地区农户致贫风险分析   总被引：4，自引：1，他引：3  

张金萍  宋伟  林丹  余珍鑫  李庭筠  罗邱戈  程叶青 《地理科学》2020,40(4):608-616

农村贫困与减贫是世界性难题,也是中国各级政府高度重视并着力解决的重大民生问题。基于农户及致贫风险的文献梳理,从区位、社会和劳动力3个要素维度构建了农户致贫风险分析的二元Logistic回归模型。采用484户农户问卷调查数据,分析了海南省连片贫困地区农户的致贫风险,提出有效减贫和持续发展对策。研究发现:① 海南连片贫困地区生态环境良好但贫困发生率较高,家庭劳动力较充裕但受教育水平较低,子女教育支出负担重,因病因残致贫比例较高,女性务工人口较多,农户自身脱贫致富的发展动力不足。② 海拔高度200 m以下、男性户主、拥有残疾或患病成员、务工人口比例低、女性务工人员占比高、以及单位劳动力供养学生数高的农户具有更大的致贫风险。③ 研究未发现女性户主、少数民族、低受教育水平户主、大型规模家庭有更高的致贫风险,女性成员比例、抚养比等因素对农户贫困影响较小。激发农户内生动力、大力发展特色化和规模化农业、增加农户就业机会、加强针对农民工、女性务工人员和病残群体的社会保障等减贫政策制定实施是实现脱贫攻坚目标的重要途径。  相似文献   

东北三省全面振兴的社会经济支撑能力评估     

马丽  田华征  康蕾  戚伟 《地理科学》2020,40(6):863-873

在解析支撑能力和东北问题特点的基础上，从“创新、协调、绿色、开放、共享”五大理念出发建立全面振兴社会经济支撑能力评价指标体系和综合评价方法，以地级市为单元对东北三省进行评估。研究发现：各地市社会经济支撑能力差异显著，哈大沿线核心城市的支撑能力较强，西翼城市以及朝阳、七台河、绥化、葫芦岛、铁岭、双鸭山和鹤岗等资源型城市支撑能力较弱。因此未来东北地区在振兴政策的区域分布上应有所侧重，对不同地区施以不同的振兴或扶持政策。在创新、绿色、开放方面需要集中力量重点突破，而在体制机制改革和共享服务建设方面需要全面覆盖，并重点加强黑龙江北部和辽宁、吉林西翼城市的共享能力建设。  相似文献   

中国区域水资源系统韧性与效率的发展协调关系评价   总被引：1，自引：0，他引：1  

孙才志  孟程程 《地理科学》2020,40(12):2094-2104

在界定水资源系统韧性概念的基础上，综合应用赋权法、SBM-DEA模型及发展协调度模型对2000—2016年全国31个省区的区域水资源系统的效率、韧性以及两者之间的发展协调关系进行评价。结果如下：① 中国区域水资源系统效率整体上处于非有效区，在研究期间呈现出在波动中上升的趋势。② 中国区域水资源系统韧性的平均值为0.39，总体水平较低，研究期间整体上呈波动上升趋势。③ 中国区域水资源系统效率与韧性的发展度总体呈现平稳上升—较快上升—急剧下降—上升的趋势，呈倒“U”型发展；协调度在研究期间呈现波动中上升趋势，大部分省区的发展度较好，而协调度较弱。④ 2000—2016年，水资源效率与韧性的发展协调度一直维持着“东?中?西”阶梯式递减格局，失调省区的数量降低，初级协调、中级协调、良好协调省区的数量逐渐提升，整体向协调趋势发展；空间格局上呈现由2000年倒“E”型对称式分布格局向如今北部围绕天津、中部围绕上海、南部围绕广东的三级格局演变，格局分布与中国三大经济区基本吻合，可知中国水资源系统效率与韧性的发展协调水平与经济发展水平之间有明显的关系。  相似文献