景谷县森林蓄积量遥感估测及其动态变化分析

以云南省景谷县为研究区,基于Landsat5和Landsat8遥感数据,利用2012年和2017年两期全国森林资源连续清查实地调查数据,建立多元逐步回归和随机森林模型对景谷县森林蓄积量进行遥感估测对比研究。结果显示:综合建模精度来看,随机森林法在相同样地数量条件下具有更好的估测效果;从估测结果与林地保护利用规划和林地变更数据结果相比较,随机森林法估测值接近真实值,估测精度较高。     

基于辐射和温度的ET0模型在吐鲁番地区的比较与修正

利用吐鲁番地区3个气象站2000—2015年逐日气象资料,以FAO-56 Penman-Monteith(FAO-56 PM)模型为标准,对6种ET_0模型(M-A模型、P-T模型、M-H模型、H-S模型、Traj模型和B-H模型)进行评价并修正,采用均方根误差(RMSE)、绝对平均误差(MAE)、平均相对误差(MRE)评价指标和Wilcoxon非参数检验法比较年、月尺度上各模型修正前后的估算精度,以筛选适用吐鲁番地区ET_0简化估算模型。结果表明:吐鲁番地区ET_0的主要影响因子是R_s(太阳辐射),其次是e_s(饱和水汽压)和R_n(作物表面净辐射);修正前,年尺度上,M-H模型的估算精度最高;月尺度上,各模型误差较大且与FAO-56 PM模型存在显著差异,适用性较差;修正后,各模型在年、月尺度上的精度均有明显提高,无显著差异,其中修正后的P-T、M-H和B-H模型估算精度最高,可作为吐鲁番地区ET_0简化估算模型。     

农地确权对小农户信贷可得性的影响——基于双稳健估计方法的平均处理效应分析

基于我国9省小农户调查数据,运用双稳健的平均处理效应模型,研究了农地确权对小农户信贷可得性的影响及其作用机理。结果表明:农地确权可以提升小农户信贷可得性。进一步研究发现,需求方面的影响路径是:确权提高了小农户的土地安全性,增加了农业长期投资,并提高了信贷意愿。供给方面的影响路径是:确权增加了小农户的财产权并使其拥有了可流转大额财产,传递了还款可能性增加的信号;而且可将农地作为抵押品获得农地抵押贷款,增加了金融机构的贷款供给。不过,农地的信号传递作用大于抵押作用,农地抵押作用的发挥需要进行进一步的制度和组织创新。     

Measuring fish catch and consumption: Practical methods for small‐scale fisheries based on length as an alternative to weight‐based approaches

Small‐scale fisheries are recognised as making important contributions to nutrition and economic development despite a lack of accurate quantitative information on catches and consumption. While direct measurement remains the most appropriate way of collecting such data, it is impractical at large scales. Instead, household surveys based upon informant recall of fish caught and/or consumed are frequently used. However, the accuracy of weight recall by informants (even over short recall periods) has not been established. Using data from household surveys, the accuracy and precision of catch and consumption estimates derived from: (a) asking informants to recall weights of fish caught and (b) asking respondents to recall lengths of fish caught and converting to weight were tested. Length‐based methods, using visual aids to assist recall, were more accurate, precise and correctable. These methods could be useful for catch estimation, especially where fish are processed, sold or consumed shortly after capture.     

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.     

Research advances of SAR remote sensing for agriculture applications: A review

Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.     

Density‐ and species‐dependent errors in single‐pass timed electrofishing assessment of riverine salmonids

Electrofishing techniques are widely used for the estimation of the size of stream‐dwelling fish populations for both fishery management and scientific study. In contrast to multiple pass, population depletion methods, single‐pass catch‐per‐unit‐effort (CPUE) methods are less time‐consuming and labour‐intensive. A possible issue with the commonly used fixed total time CPUE protocol is that it does not differentiate between the time spent actively fishing and the time incurred while not actively fishing (e.g. removing fish from nets and navigating the site). This issue is likely related to fish density. This study compared two methods of CPUE electrofishing and tested the hypothesis that time spent handling fish and navigating a site can be a source of error in the commonly used fixed total time electrofishing method. Seventy‐one sites were sampled across three subcatchments in the Foyle catchment in Northern Ireland. We found a difference in the catch per unit time between the two methods and that this difference increased with fish density. The fixed‐time CPUE method also failed to detect a species presence in low‐density sites.     

中国农业面源污染排放的空间差异及其动态演变

以2003—2014年为研究时段,在测算农业面源污染排放强度的基础上,综合运用基尼系数和非参数估计方法,研究我国农业面源污染的空间差异及其动态演变,结果表明:1)我国农业面源污染排放总体下降,且表现出明显的空间差异,东部和中部地区的排放强度较高,西部和东北地区则相对较低。2)2003—2014年,中国农业面源污染排放强度的区域差异略微扩大,地区间差异是其总体差异的主要来源。3)核密度估计结果表明,中国农业面源污染排放总体差异表现为"下降-上升-下降"的波动变化趋势。4)马尔科夫链分析表明,中国农业面源污染在不同类型间的相互流动较为微弱,但从长期来看,存在向两极分化发展的趋势特征。     

无人机遥感影像面向对象分类方法估算市域水稻面积

针对如何高效地从无人机遥感影像中提取农作物样方数据,用于农作物面积遥感估算,该文以浙江省平湖市为例,利用面向对象分类方法对无人机影像进行水稻自动化识别,作为样方数据与卫星遥感全覆盖空间分布分类结果结合,采用分层联合比估计进行2014年单季晚稻面积估算。然后,与人工目视解译识别方法获取的水稻样方数据推断的区域水稻面积估算的结果进行精度、效率对比分析。研究结果表明:1)利用面向对象分类方法对无人机影像进行分类,总体分类精度达到93%以上,满足构建样本的要求;2)通过区域作物估算对比分析发现,面向对象分类方法对无人机影像进行水稻识别,构建平湖市单季晚稻的样方数据,能够替代人工目视解译样方准确推断区域作物种植面积,有效地提高了无人机影像在遥感面积估算中的应用效率。     

越冬期麦田根区水热耦合模型参数原位估算与检验

越冬期根区充足的蓄水量有利于春季冬小麦的生长并有助于增产,而冬季作物根区水分运移规律通常借助土壤冻融过程水热耦合模型来描述,但该模型的预测精度受参数确定方法与边界条件等多种因素的影响。为了提高模型预测精度,提出了一种改进型的土壤冻融过程水热耦合模型参数估算方法,即运用土壤冻融特征曲线(冻土未冻水含量和土壤温度的关系)来原位估算土壤水热耦合模型参数,并检验该方法的适用性。在此基础上,评价地表蒸发量对模型预测精度的影响。大田试验在北京市昌平区小汤山精准农业示范基地开展,历经两个越冬期(2011—2012年和2012—2013年),利用管式介电传感器、温度传感器和蒸渗仪分别采集了土壤剖面未冻水含量、土壤温度和地表蒸发量数据。利用第1个越冬期(2011—2012年)的数据拟合土壤冻融特征曲线,对土壤水热参数进行最优估算,利用第2个越冬期(2012—2013年)的数据评价估算参数和地表蒸发量对模型预测精度的影响。结果表明:利用估算参数的模型预测值整体上与实测值相符。考虑到地表蒸发量对模型水热上边界的影响,第2个越冬期10 cm处未冻水含量与温度预测值和实测值的RMSE分别为0.046 m3/m3和1.883℃,20 cm深度RMSE分别为0.071 m3/m3和2.347℃。相比之下,在未考虑地表蒸发量影响下,第2个越冬期10 cm处未冻水含量与温度的模拟值和实测值的RMSE为0.059 m3/m3和2.149℃,20 cm深度RMSE为0.081 m3/m3和2.666℃。提出的改进型模型参数估算方法能够保证模型的预测精度,且考虑地表蒸发量的影响能够进一步提高模型的预测精度,随着深度的增加,蒸发量对水分与温度的影响逐渐减小。