基于landsat8的贵州省2015年农业干旱监测研究

在全球气候变暖背景下,干旱普遍发生,而农业干旱对国民经济发展的影响尤为明显,同时农业干旱还威胁着国家的粮食安全和生态安全。基于贵州省2015年不同季节的landsat8 OIL遥感数据,利用影像数据所获取的植被覆盖指数和地表温度数据,拟合植被指数(NDVI)和地表温度(LST)的特征空间,设计得到贵州省2015年春、夏、秋、冬四个不同季节的不同的土壤湿度,将TVDI作为监测农业干旱状况的指标,得到贵州省2015年的农业干旱时空分布图。结果表明,土壤含水率的高低与植被覆盖和地表温度有关,且TVDI更适宜中等植被覆盖的土壤湿度反演。贵州省2015年全年旱情较缓,各地区均不存在春旱或伏旱,只有冬季绝大部分地区土壤含水率较低,更有力地促进了农业干旱的发生。因此,对农业干旱的监测研究为贵州省农业干旱的监测管理提供有力依据,为今后减少农业干旱的影响和进一步促进农业社会经济可持续发展具有重要的现实意义。     

Peri-urbanization may vary with vegetation restoration: A large scale regional analysis

Over that past decade, ecological restoration practices have expanded globally. However, the effectiveness of ecological restoration depends on the complex interactions of various natural and socioeconomic factors, about which there is limited scientific understanding and thus provides an important research frontier. This paper analyzed the relationship between regional scale vegetation restoration and the process of urbanization using the Loess Plateau of China as a case study. This region has experienced both rapid urbanization and a high number of vegetation restoration activities. Urbanization and vegetation restoration can be considered as extremes on the spectrum of environment preservation activities. Three separate spatial correlation analyses between urbanization and vegetation restoration were identified, resulting in: 1) insignificant correlations in saturated urban areas; 2) significant negative correlations in peri-urban areas; and 3) significant positive correlations in undeveloped areas. The relationship between urbanization and vegetation restoration is thus stage-dependent. Impacts of urbanization on vegetation degradation has improved but has not been fully addressed by large scale vegetation restoration. Regardless of whether the county or grid scale is used, peri-urbanization was found to be the critical factor affecting the effectiveness of vegetation restoration over both time and space. Therefore, peri-urbanized areas are viewed as priorities for improving the coupling of urban development and vegetation restoration.     

黄土高原植被恢复过程中土壤表面电化学性质演变特征

土壤胶体表面所带电荷是土壤具有一系列物理、化学性质的根本原因。表面电荷数量、比表面积、表面电荷密度、表面电场强度以及表面电位是土壤胶体颗粒重要性质,影响土壤中物理、化学、生物化学过程。运用带电颗粒表面性质联合分析法,测定黄土高原子午岭地区不同植被类型下土壤表面电荷性质,研究自然植被恢复过程对土壤表面电荷性质的影响。结果表明:随着植被的演替,子午岭林区土壤表面电荷数量、比表面积、表面电荷密度均随植被的恢复增加,变化范围分别为10.88～19.85 cmol·kg~(–1)、40.67～61.71 m~2·g~(–1)和0.22～0.31 c·m~(–2),平均值分别为16.18 cmol·kg~(–1)、54.88 m2·g~(–1)和0.28 c·m~(–2),土壤表面电场强度达108 V·m~(–1)数量级;土壤黏粒、有机碳含量是影响表面电荷性质的主要因素,解释率分别为62.5%和27.9%;土壤基本性质对表面电荷性质的影响由强到弱依次为:黏粒、有机碳、砂粒、全氮、C/N、粉粒、碳酸钙、pH。研究结果对于进一步认识黄土高原土壤颗粒表面性质,加深理解土壤中发生的一系列物理化学过程具有重要意义。     

典型喀斯特洼地植被恢复过程中土壤碳氮储量动态及其对极端内涝灾害的响应

西南喀斯特地区是我国主要的生态脆弱区之一,石漠化严重,旱涝灾害频发。植被恢复是提升脆弱生态系统土壤碳氮固持的有效方式,但该区不同植被恢复方式土壤碳氮动态监测的研究还很缺乏。本研究以典型喀斯特峰丛洼地为对象,选取人工林、牧草地、人工林+牧草地、撂荒地自然恢复4种最主要的植被恢复方式为研究对象,以耕地作为对照,对比分析退耕前(2004年)、退耕10年(2014年)和13年后(2017年)土壤碳氮储量动态变化特征。其中2004—2014年研究区未发生极端内涝灾害, 2014—2017年连续发生2次极端内涝灾害事件。研究结果表明,退耕10年后, 4种恢复方式下土壤有机碳(SOC)储量均显著增加,但退耕13年后,除撂荒地SOC持续增加外,其他3种恢复方式下SOC表现出下降趋势。植被恢复后土壤全氮(TN)储量提升相对缓慢,退耕10年仅牧草地显著增加,退耕13年后人工林+牧草和撂荒地TN增加,且撂荒地在退耕后呈持续增加趋势。相关性分析结果表明,土壤交换性Ca~(2+)与SOC、TN均呈显著正相关关系,且与2014年相比, 2017年不同植物恢复方式下土壤交换性Ca~(2+)均显著下降,这可能与研究区2015年和2016年连续内涝灾害有关。以上结果说明,不同恢复方式均能显著提升喀斯特地区土壤碳氮固持,并以自然恢复最佳,其生态系统能有效抵御极端气候灾害带来的负面影响。     

新巴尔虎右旗植被覆盖变化研究

利用ArcGIS和ENVI的栅格空间分析工具,采用叠置分析、线性趋势分析法、均值法等分析方法,选用2001~2016年每年5~9月MODIS NDVI数据和该研究区域行政区划矢量数据来研究新巴尔虎右旗16年间的植被覆盖变化,为该地区植被趋势研究和生态建设提供一定的科学依据。结果表明:2001~2016年间新巴尔虎右旗生长季NDVI值呈增长趋势,但存在空间差异性,并且在不同时间段的具体表现不同;新巴尔虎右旗植被覆盖度呈增长趋势的面积占比,远大于植被覆盖度呈减小趋势的面积占比。     

运用植被指数时序特征对落叶松人工林分类

为探究植被指数时序特征是否有利于落叶松人工林提取,以孟家岗林场为研究试验区域,根据落叶松人工林季相和物候特征,利用Landsat8OLI影像数据提取研究区内5种植被的归一化植被指数(I NDV)、差值植被指数(I DV)、比值植被指数(I RV)、增强型植被指数(I EV),构建相应的植被指数时序特征。采用最大似然和随机森林两种方法对单一时相影像和加入植被指数时序特征的影像进行对比试验。结果表明:影像中加入植被指数时序特征后,最大似然算法的分类总体精度为89.53%,Kappa系数为0.87,比单一时序特征的影像分类精度提高了13.35%;随机森林算法的森林类型分类总体精度为93.22%,Kappa系数为0.92,比单一时序特征的影像分类精度提高了19.8%。因此,加入植被指数时序特征后能得到更高的落叶松人工林提取精度。     

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.     

基于无人机多光谱遥感的冬小麦冠层叶绿素含量估测研究

为探讨利用无人机多光谱影像监测冬小麦叶绿素含量的可行性,基于北京市大兴区中国水科院试验基地的2019年冬小麦无人机多光谱影像和田间实测冠层叶绿素含量数据,选取16种光谱植被指数,确定对冬小麦冠层叶绿素含量显著相关的植被指数,采用一元二次线性回归和逐步回归分析方法建立各生育时期及全生育期的SPAD值估测模型,通过精度检验确定对冬小麦冠层叶绿素含量监测的最优模型。结果表明,两种分析方法中逐步回归建模效果最佳。拔节期选取4个植被指数(MSR、CARI、NGBDI、TVI)建模效果最好,模型率定的决定系数(r~2)为0.73,模型验证的r~2、相对误差(RE)和均方根误差(RMSE)分别为0.63、2.83%、1.68;抽穗期选取3个植被指数(GNDVI、GOSAVI、CARI)建模效果最好,模型率定的r~2为0.81,模型验证的r~2、RE、RMSE分别为0.63、2.83%、1.68;灌浆期选取2个植被指数(MSR、NGBDI)建模效果最好,模型率定的r~2为0.67,模型验证的r~2、RE、RMSE分别为0.65、2.83%、1.88。因此,无人机多光谱影像结合逐步回归模型可以很好地监测冬小麦SPAD值动态变化。     

Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon

Nitrogen losses from intensive vegetal production systems are commonly associated with contamination of water bodies. Sustainable and optimal economic N management requires correct and timely on-farm assessment of crop N status to detect N deficiency or excess. Optical sensors are promising tools for the assessment of crop N status throughout a crop or at critical times. We evaluated optical sensor measurement of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop N status weekly throughout a muskmelon crop. The Crop Circle ACS 470 was used for reflectance measurement, the SPAD 502 for leaf chlorophyll, and the DUALEX 4 Scientific for leaf chlorophyll and flavonols. Four indices of canopy reflectance (NDVI, GNDVI, RVI, GVI), leaf flavonols and chlorophyll contents and the nitrogen balance index (NBI), the ratio of chlorophyll to flavonols contents, were linearly related to crop N content and to crop Nitrogen Nutrition Index (NNI) throughout most of the crop. NBI most accurately predicted crop N status; in five consecutive weekly measurements, R² values were 0.80–0.95. For NDVI during the same period, R² values were 0.76–0.87 in the first three measurements but R² values in the last two measurements were 0.39–0.45. Similar relationships were found with the three other reflectance indices. Generally, the relationships with NNI were equal to or slightly better than those with crop N content. These optical sensor measurements provided (i) estimation of crop N content in the range 1.5–4.5%, and (ii) an assessment of whether crop N content was sufficient or excessive for optimal crop growth for NNI ranges of 0.8–2.0. Composite equations that integrated the relationships between successive measurements with the optical sensors and crop N content or NNI for periods of ≥2 weeks (often 2–3 weeks) were derived for most indices/parameters. Overall, these results demonstrated the potential for the use of these optical sensor measurements for on-farm monitoring of crop N status in muskmelon.