Sensors in agriculture and forestry

Agriculture and Forestry are two broad and promising areas demanding technological solutions with the aim of increasing production or accurate inventories for sustainability while the environmental impact is minimized by reducing the application of agro-chemicals and increasing the use of environmental friendly agronomical practices. In addition, the immediate consequence of this “trend” is the reduction of production costs. Sensors-based technologies provide appropriate tools to achieve the above mentioned goals. The explosive technological advances and development in recent years enormously facilitates the attainment of these objectives removing many barriers for their implementation, including the reservations expressed by the farmers themselves. Precision Agriculture is an emerging area where sensor-based technologies play an important role.RHEA project [42], which is funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement NO.245986, which has been the platform for the two international conferences on Robotics and associated High-technologies and Equipment mentioned above.Peer Reviewe

Earth resources: A continuing bibliography with indexes (issue 55)

This bibliography lists 368 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1987. Emphasis is placed on the use of remote sensing and geographical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Multi-wavelength, multi-beam, photonic based sensor for object discrimination and positioning

Over the last decade, substantial research efforts have been dedicated towards the development of advanced laser scanning systems for discrimination in perimeter security, defence, agriculture, transportation, surveying and geosciences. Military forces, in particular, have already started employing laser scanning technologies for projectile guidance, surveillance, satellite and missile tracking; and target discrimination and recognition. However, laser scanning is relatively a new security technology. It has previously been utilized for a wide variety of civil and military applications. Terrestrial laser scanning has found new use as an active optical sensor for indoors and outdoors perimeter security. A laser scanning technique with moving parts was tested in the British Home Office - Police Scientific Development Branch (PSDB) in 2004. It was found that laser scanning has the capability to detect humans in 30m range and vehicles in 80m range with low false alarm rates. However, laser scanning with moving parts is much more sensitive to vibrations than a multi-beam stationary optic approach. Mirror device scanners are slow, bulky and expensive and being inherently mechanical they wear out as a result of acceleration, cause deflection errors and require regular calibration. Multi-wavelength laser scanning represent a potential evolution from object detection to object identification and classification, where detailed features of objects and materials are discriminated by measuring their reflectance characteristics at specific wavelengths and matching them with their spectral reflectance curves. With the recent advances in the development of high-speed sensors and high-speed data processors, the implementation of multi-wavelength laser scanners for object identification has now become feasible. A two-wavelength photonic-based sensor for object discrimination has recently been reported, based on the use of an optical cavity for generating a laser spot array and maintaining adequate overlapping between tapped collimated laser beams of different wavelengths over a long optical path. While this approach is capable of discriminating between objects of different colours, its main drawback is the limited number of security-related objects that can be discriminated. This thesis proposes and demonstrates the concept of a novel photonic based multi-wavelength sensor for object identification and position finding. The sensor employs a laser combination module for input wavelength signal multiplexing and beam overlapping, a custom-made curved optical cavity for multi-beam spot generation through internal beam reflection and transmission and a high-speed imager for scattered reflectance spectral measurements. Experimental results show that five different laser wavelengths, namely 473nm, 532nm, 635nm, 670nm and 785nm, are necessary for discriminating various intruding objects of interest through spectral reflectance and slope measurements. Various objects were selected to demonstrate the proof of concept. We also demonstrate that the object position (coordinates) is determined using the triangulation method, which is based on the projection of laser spots along determined angles onto intruding objects and the measurement of their reflectance spectra using an image sensor. Experimental results demonstrate the ability of the multi-wavelength spectral reflectance sensor to simultaneously discriminate between different objects and predict their positions over a 6m range with an accuracy exceeding 92%. A novel optical design is used to provide additional transverse laser beam scanning for the identification of camouflage materials. A camouflage material is chosen to illustrate the discrimination capability of the sensor, which has complex patterns within a single sample, and is successfully detected and discriminated from other objects over a 6m range by scanning the laser beam spots along the transverse direction. By using more wavelengths at optimised points in the spectrum where different objects show different optical characteristics, better discrimination can be accomplished

Earth Resources: A continuing bibliography with indexes

This bibliography lists 475 reports, articles and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Finnish Remote Sensing Days 2012 : Book of abstracts

Peer reviewe

Recent remote sensing applications for hydro and morphodynamic monitoring and modelling

It is not new to recognise that data from remote sensing platforms is transforming the way we characterise and analyse our environment. The ability to collect continuous data spanning spatial scales now allows geomorphological research in a data rich environment and this special issue (coming just 7 years after the 2010 special issue of ESPL associated with the remote sensing of rivers) highlights the considerable research effort being made to exploit this information, into new understanding of geomorphic form and process. The 2010 special issue on the remote sensing of rivers noted that fluvial remote sensing papers made up some 14% of the total river related papers in ESPL. A similar review of the papers up to 2017 reveals that this figure has increased to around 25% with a recent proliferation of articles utilising satellite based data and structure from motion derived data. It is interesting to note, however that many studies published to date are proof of concept, concentrating on confirming the accuracy of the remotely sensed data at the expense of generating new insights and ideas on fluvial form and function. Data is becoming ever more accurate and researchers should now be concentrating on analysing these early data sets to develop increased geomorphic insight challenging paradigms and moving the science forward. The prospect of this occurring is increased by the fact that many of the new remote sensed platforms allow accurate spatial data to be collected cheaply and efficiently. This is providing the individual researcher or small research grouping with tremendous opportunity to move the science of fluvial geomorphology forward unconstrained to a large degree of the need to secure substantial research funding. Fluvial geomorphologists have never before been in such a liberated position! As techniques and analytical skills continue to improve it is inevitable that Marcus and Fondstad's (2010) prediction that remotely sensed data will revolutionising our understanding of geomorphological form and process will prove true, altering our ideas on the very nature of system functioning in the process

Remote Sensing of Earth Resources (1970 - 1973 supplement): A literature survey with indexes. Section 2: Indexes

Documents related to the identification and evaluation by means of sensors in spacecraft and aircraft of vegetation, minerals, and other natural resources, and the techniques and potentialities of surveying and keeping up-to-date inventories of such riches are cited. These documents were announced in the NASA scientific and technical information system between March 1970 and December 1973

Terrestrial laser scanning for crop monitoring. Capturing 3D data of plant height for estimating biomass at field scale

Terrestrial laser scanning (TLS) is a young remote sensing method, but the trustworthiness of such measurements offers great potential for accurate surveying. TLS allows non-experts to rapidly acquire 3D data of high density. Generally, this acquisition of accurate geoinformation is increasingly desired in various fields, however this study focuses on the application of TLS for crop monitoring. The increasing cost and efficiency pressure on agriculture induced the emergence of site specific crop management, which requires a comprehensive knowledge about the plant development. An important parameter to evaluate this development or rather the actual plant status is the amount of plant biomass, which is however directly only determinable with destructive sampling. With the aim of avoiding destructive measurements, interest is increasingly directed towards non-contact remote sensing surveys. Nowadays, different approaches address biomass estimations based on other parameters, such as vegetation indices (VIs) from spectral data or plant height. Since the plants are not taken it is feasible to perform several measurements across a field and across the growing season. Hence, the change of spatial and temporal patterns can be monitored. This study applies TLS for objectively measuring and monitoring plant height as estimator for biomass at field scale. Overall 35 TLS campaigns were carried out at three sites over four growing seasons. In each campaign a 3D point cloud, covering the surface of the field, was obtained and interpolated to a crop surface model (CSM). A CSM represents the crop canopy in a very high spatial resolution on a specific date. By subtracting a digital terrain model (DTM) of the bare ground from each CSM, plant heights were calculated pixel-wise. Manual measurements aligned well with the TLS data and demonstrated the main benefit of CSMs: the highly detailed acquisition of the entire crop surface. The plant height data were used to estimate biomass with empirically developed biomass regression models (BRMs). Validation analyses against destructive measurements were carried out to confirm the results. The spatial and temporal transferability of crop-specific BRMs was shown. In one case study, the estimations from plant height and six VIs were compared and the benefit of fusing both parameters was investigated. The analyses were based on the TLS-derived CSMs and spectral data measured with a field spectrometer. The important role of plant height as a robust estimator was shown in contrast to a varying performance of BRMs based on the VIs. A major benefit through the fusion of both parameters in multivariate BRMs could not be concluded in this study. Nevertheless, further research should address this fusion, with regard to the capability of VIs to assess information about the vegetation cover or biochemical and biophysical parameters