Detection of Ecballium elaterium in hedgerow olive orchards using a low-cost uncrewed aerial vehicle and open-source algorithms

BACKGROUND

Ecballium elaterium (common name: squirting cucumber) is an emerging weed problem in hedgerow or superintensive olive groves under no tillage. It colonizes the inter-row area infesting the natural or sown cover crops, and is considered a hard-to-control weed. Research in other woody crops has shown E. elaterium has a patchy distribution, which makes this weed susceptible to design a site-specific control strategy only addressed to E. elaterium patches. Therefore, the aim of this work was to develop a methodology based on the analysis of imagery acquired with an uncrewed aerial vehicle (UAV) to detect and map E. elaterium infestations in hedgerow olive orchards.

RESULTS

The study was conducted in two superintensive olive orchards, and the images were taken using a UAV equipped with an RGB sensor. Flights were conducted on two dates: in May, when there were various weeds infesting the orchard, and in September, when E. elaterium was the only infesting weed. UAV-orthomosaics in the first scenario were classified using random forest models, and the orthomosaics from September with E. elaterium as the only weed, were analyzed using an unsupervised algorithm. In both cases, the overall accuracies were over 0.85, and the producer's accuracies for E. elaterium ranged between 0.74 and 1.00.

CONCLUSION

These results allow the design of a site-specific and efficient herbicide control protocol which would represent a step forward in sustainable weed management. The development of these algorithms in free and open-source software fosters their application in small and medium farms. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

基于多源遥感数据的高寒草地生物量反演模型精度——以夏河县桑科草原试验区为例

基于MODIS、Landsat-8 OLI和HJ-1A/1B CCD卫星遥感资料,结合2013-2014年甘南州夏河县桑科草原试验区野外实测数据,建立了高寒草地地上生物量遥感反演模型,筛选出基于不同遥感资料植被指数的生物量最优反演模型,比较分析了生物量最优模型的空间效应。同时,分析了2000-2013年基于MODIS植被指数估算的试验区产草量的年际变化特征。结果表明,草地生物量最优反演模型为基于Landsat-8 OLI NDVI数据的对数模型(y=727.54lnx1+495.23,R2=0.772,RMSE=31.333 kg DM·hm-2);在30和250 m空间分辨率下,基于MODIS NDVI及EVI、Landsat-8 OLI NDVI和HJ-1A/1B CCD NDVI最优模型估算的生物量均高于实测生物量,其中Landsat-8 OLI NDVI数据估算的草地生物量与实测生物量值最接近;2000-2013年试验区草地总生物量整体上具有显著增加的趋势(R2=0.590 7,P0.001),平均增加速率达50.57 kg DM·hm-2·a-1。     

利用遥感技术对江边林业局进行森林资源调查

选取美国陆地卫星Land-sat-stm1999年3月成像比例尺为1：5万的TM453卫生数据，于2000年1－3月，对江边林业局森林资源进行了外业建标、室内判读和现地验证。结果判读一致率达92．3％，正判率达97．5％。     

QuickBird影像目视判读在土地利用类型调查中的应用研究

利用Q u ickB ird影像数据进行土地利用类型调查过程中采用人工目视判读或人机交互式判读方法能获取相对准确的土地利用类型信息。目视判读的难点是对易混淆地类和森林类型的判读,包括草地与农田、灌木林与果园地、经济林与用材林、陡坡地与常年旱地等。通过适当的前期图像处理,以建立解译标志为基础,采取综合的判读方法可将上述类型大部分判读出来。     

川东北地区天然林资源保护工程实施现状及对策

通过对长江中上游天然林资源保护工程川东北地区的遥感监测，全面分析了工程实施以来取得的成绩和存在的问题，提出了相应的对策措施。     

SPOT5卫星遥感信息在森林资源调查中的应用现状与解决思路

介绍利用SPOT5卫星遥感在云南省森林资源调查中的应用情况,总结所取得的成果经验。分析存在的包括对遥感的内涵认识不足、特殊地类不易分辨、树种组成难判读、蓄积量不能直接判读、龄组和郁闭度判读因子粗放等问题,提出了解决问题的思路和建议。     

森林灾害监测方法研究──以西南地区火灾监测为例*

森林灾害监测方法研究──以西南地区火灾监测为例纪平，易浩若，白黎娜关键词人工神经元网络，ＮＯＡＡ数据，遥感，专家系统目前，世界上许多国家都已开展了森林灾害监测方面的研究。１９８７年大兴安岭森林大火之后，我国也充分认识到了森林防火的重要性，并积极开展了...     

遥感植被指数在重点国有林区“三总量”伐区检查中的应用研究——以对鹤立林业局的研究为例

以鹤立林业局为研究区域,以2007年"三总量"检查的TM遥感判读区划结果和抽样现地检查结果为基础资料,分析了同期遥感数据之间、不同年份遥感数据之间采伐地块的植被指数变化,以及不同采伐方式和不同采伐强度与植被指数变化的关系,为利用遥感数据进行"三总量"检查进行了有益的探索和实践。     

遥感技术在森林病虫害监测中的应用

本文结合雷达、航空和卫星遥感的技术特点 ,从 3个方面介绍了遥感技术在森林病虫害早期监测及预测中的应用。     

Monitor Forest Fires with FY Serial Satellites

Remote sensing as the measure to monitor disasters has the advantage of temporal resolution and large scale. Since "5.6 catastrophe" in 1987, China began to monitor forest fires broadly. In the summer of 2002, many forest/grass fires occurred in the Daxing'anling Mountains, and the damage was very heavy. In the forest fires fighting, the meteorological satellites play an important role in monitoring the fires. Especially the FY serial meteorological satellites have the advantage of large scale monitoring and characteristics of being in time and exact, and get good results. Remote sensing techniques have been used in many aspects of forest/grass fires research, including burned area and fireline intensity evaluation, post fire environment monitoring etc.