Uas Remote Sensing Methods For Mapping Monitoring And Modeling Crops

The advances in unmanned aerial vehicle (UAV) platforms and onboard sensors in the past few years have greatly increased our ability to monitor and map crops. The ability to register images at ultrahigh spatial resolution at any moment has made remote sensing techniques increasingly useful in crop management. These technologies have revolutionized the way in which remote sensing is applied in precision agriculture, allowing for decision-making in a matter of days instead of weeks. However, it is still necessary to continue research to improve and maximize the potential of UAV remote sensing in agriculture. This Special Issue of Remote Sensing includes different applications of UAV remote sensing for crop management, covering RGB, multispectral, hyperspectral and light detection and ranging (LiDAR) sensor applications aboard UAVs. The papers reveal innovative techniques involving image analysis and cloud points. However, it should be emphasized that this Special Issue is a small sample of UAV applications in agriculture and that there is much more to investigate.

Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling, provide insight into the diversity and the complexity of developments of RS applications in agriculture. Five thematic focuses have emerged from the published papers: yield estimation, land cover mapping, soil nutrient balance, time-specific management zone delineation and the use of UAV as agricultural aerial sprayers. All contributions exploited the use of remote sensing data from different platforms (UAV, Sentinel, Landsat, QuickBird, CBERS, MODIS, WorldView), their assimilation into crop models (DSSAT, AQUACROP, EPIC, DELPHI) or on the synergy of Remote Sensing and modeling, applied to cardamom, wheat, tomato, sorghum, rice, sugarcane and olive. The intended audience is researchers and postgraduate students, as well as those outside academia in policy and practice.

This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proves the wide scope of UAVs in very diverse vegetation applications, both in agricultural and forestry scenarios, ranging from the characterization of relevant vegetation features to the detection of plant or crop stressors. New methods and techniques are developed and applied to diverse vegetation scenarios to meet the main challenge of sustainability.

Unmanned Aircraft Systems (UAS) are a rapidly evolving technology with an expanding array of diverse applications. In response to the continuing evolution of this technology, this book discusses unmanned aerial vehicles (UAVs) and similar systems, platforms and sensors, as well as exploring some of their environmental applications. It explains how they can be used for mapping, monitoring, and modeling a wide variety of different environmental aspects, and at the same time addresses some of the current constraints placed on realizing the potential use of the technology such as s flight duration and distance, safety, and the invasion of privacy etc. Features of the book: Provides necessary theoretical foundations for pertinent subject matter areas Introduces the role and value of UAVs for geographical data acquisition, and the ways to acquire and process the data Provides a synthesis of ongoing research and a focus on the use of technology for small-scale image and spatial data acquisition in an environmental context Written by experts of the technology who bring together UAS tools and resources for the environmental specialist Unmanned Aerial Remote Sensing: UAS for Environmental Applications is an excellent resource for any practitioner utilizing remote sensing and other geospatial technologies for environmental applications, such as conservation, research, and planning. Students and academics in information science, environment and natural resources, geosciences, and geography, will likewise find this comprehensive book a useful and informative resource.

Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance from a satellite, drone or aircraft. It is used in several fields including photography, geology, and surveying. Remote sensing plays an instrumental role in the field of crop production and management. It is used extensively for forecasting crop production, assessing crop damage and crop progress, identifying pests and disease infestation, soil mapping, monitoring and managing irrigation, and collecting past and current weather data. Crop intensification is another important application of remote sensing that involves collecting important data related to crops including crop rotation needs, cropping pattern, and crop diversity over a given piece of land. The remote sensing data can also be used in crop models, which show the relationships among plant, water and soil. Such models help reveal water stress and its effects on crop growth and development. This book is a detailed explanation of the various applications of remote sensing in crop plants. It is a vital tool for all researching or studying the role of remote sensing in crop production as it gives incredible insights into emerging modeling methods and applications.

"The use of unmanned aerial systems (UAS)-based remote sensing methods in precision agriculture (PA) has seen rapid development in recent years. These technologies are expected to revolutionize crop management by capturing imagery data with a high spatial, temporal, and spectral resolution, thereby enabling the decision-making of farm inputs at the sub-field level and on an almost daily basis. However, in real-world operational applications, the potential of UAS-based remote sensing methods has not yet been fully exploited. One of the main research avenues is that of structural characterization of crops in order to assess plant density, leaf density, i.e., overall crop health, and ultimately, crop yield. Using a UAS-based imagery system, we concurrently collected multi-source imagery data. We used structure-from-motion (SfM; photogrammetry) and light detection and ranging (LiDAR) point clouds to observe snap bean fields across two years. We hypothesized that the 3D point clouds represent essential structural information of the crop and that by extracting various features from the oversampled (dense) 3D data, we could retrieve critical structural characteristics of the crops and eventually relate them to high-level objectives, including disease risk and yield modeling. We further explored the effectiveness of feature-level data fusion between LiDAR point clouds and multispectral imagery, coupled with machine learning algorithms, for yield modeling and disease detection applications. We found that both SfM and LiDAR point clouds achieved similar high accuracies for assessment of crop height (CH) and row width (RW) (RMSE of ~0.02 m for CH and ~0.05 m for RW). For measuring the leaf area index (LAI), the LiDAR-derived models achieved the highest accuracy (R2= 0.61, nRMSE = 19%), while the SfM-derived models exhibited slightly lower values with a predicted R2≈0.5 and nRMSE ≈22%. We found that the fusion of LiDAR and MSI data yielded good results for prediction of the snap bean yield, with an Adj. R2 = 0.827 and nRMSE = 9.4%. This work demonstrated the potential of 3D point cloud data in PA applications and the performance of a UAS-based remote sensing system in monitoring short broadacre crops, such as snap bean."--Abstract.

Applications of Remote Sensing in Agriculture contains the proceedings of the 48th Easter School in Agricultural Science, held at the University of Nottingham on April 3-7, 1989. The meeting invites 146 delegates from over 22 countries and contributions to this book come from nine countries. This book generally presents a review of the achievements of remote sensing in agriculture, establishes the state of the art, and gives pointers to developments. This text is organized into seven parts, wherein Parts I-III cover the principles of remote sensing, climate, soil, land classification, and crop inventories. Productivity; stress; techniques for agricultural applications; and opportunities, progress, and prospects in the field of remote sensing in agriculture are also discussed.

Precision irrigation management is critical to optimize the existing consumptive water usage and to balance the ever-increasing food as well as water demand. Therefore, technological advances are needed to better monitor and map the crop water use. Advent of small unmanned aerial systems (UAS) has shown potential to map the crop characteristics at high spatiotemporal resolution. This dissertation explored the application of small UAS based spectral imagery in the visible, near-infrared and longwave thermal infrared wavelengths to map the crop water use (Evapotranspiration [ET] or Transpiration [T]) of the field and perennial crops grown in the Pacific Northwest of the United States. Aerial imagery was acquired at 7 cm/pixel (Multispectral) and 13 cm/pixel (Thermal) and integrated to the modified satellite-standard Mapping ET at High Resolution with Internalized calibration (METRIC). Resulting energy balance model outputs were ET maps for commercially grown potato, spearmint, alfalfa, grapevines, and apple crops. Weather data from nearest automated stations of WSU-AgWeatherNet network was also used as inputs to the energy balance models. UAS-based ET mapping i.e., UASM-ET outperformed conventional satellite-based ET mapping (30 m/pixel) and standard crop-coefficient approaches in mapping spatial heterogeneities in ET. The UASM-ET estimates had strong and significant correlations with the in-situ ET calculations from soil water balance approach with relative deviations within 10% of the latter. The UASM-T maps could capture the impacts of deficit and direct-root-zone drip irrigation of grapevines. The vines irrigated at 100 or 80% of the commercial rates transpired significantly higher than the ones irrigated at 60 and 40% of the commercial rates. Similarly, ET and T was successfully mapped for a high-density apple orchard (cv. Buckeye Gala) throughout the growing season. Evaluated was also the effect of localized and non-localized weather on the apple canopy T estimates. As an alternative to the standard open-field weather stations, the in-situ weather data acquired along different canopy heights could be reliably used for water use quantifications as it can account for localized cropping conditions and management practices. Overall, approaches and data outputs from this dissertation would be useful to growers and crop consultants for site-specific irrigation scheduling and management.

Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones’ geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. Presents a well-integrated collection of chapters, with quality, consistency and continuity Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented Covers geospatial data, with codes available through shared links