Spatio-temporal Analysis of Remote Sensing and Field Measurements for Smart Farming

AbstractFor the optimization of crop yield and quality, there is an ongoing development in improving crop management advice, in order to cope with the spatial variability of the growth process, caused by local variations in, amongst others, soil composition, moisture and nutrition content. To achieve this improvement, reliable information is required on the actual status of the vegetation and the expected development and yield given different management scenarios. Remote sensing observations form a valuable information source for assessing the location of suboptimal growth, but hardly ever provide the cause of the arrearage. In order to determine this cause, the observations must be combined with other observations and models and analyzed integrally.This article presents the followed approach and initial results of a pilot project Smart Farming carried out in the Dutch North East Polder. Observations and data from several sources have been collected for a number of potato parcels in 2014. The collected data includes multi-temporal satellite and UAS observations, field based soil, vegetation and yield observations, soil type maps, height maps, historic parcel and crop information and meteorological data. A data driven approach was followed to determine the presence of relations between the various observations in order to couple location and probable cause of sub-optimal crop growth and determine temporal developments in series of observations. The available data was analyzed integrally using correlation, regression and histogram analysis techniques. All resulting spatial layers are visually presented in a GIS based web service environment, so that the advisor or farmer can view the raw and derived information interactively and form his/her conclusions

Real-Time UAV based geospatial video integrated into the Fire Brigades crisis management GIS system

During a fire incident live airborne video offers the fire brigade an additional means of information. Essential for the effective usage of the daylight and infra red video data from the UAS is that the information is fully integrated into the crisis management system of the fire brigade. This is a GIS based system in which all relevant geospatial information is brought together and automatically distributed to all levels of the organisation. In the context of the Dutch Fire-Fly project a geospatial video server was integrated with a UAS and the fire brigades crisis management system, so that real-time geospatial airborne video and derived products can be made available at all levels during a fire incident. The most important elements of the system are the Delftdynamics Robot Helicopter, the Video Multiplexing System, the Keystone geospatial video server/editor and the Eagle and CCS-M crisis management systems. In discussion with the Security Region North East Gelderland user requirements and a concept of operation were defined, demonstrated and evaluated. This article describes the technical and operational approach and results

Multi-Sensor Remote Sensing for Obtaining Geographical Information

We have studied remote sensing data from sensors in different wavelength regions (optical, thermal infrared and microwave) and from different platforms (airborne and spaceborne) in order to extract geographical information. By comparing the extracted information with an existing geographical database ofa test area in the Netherlands we find that to obtain military relevant cartographic information from remote sensing images resolutions of 5 meter or less are required. For appropriate classification of extended objects like agricultural fields multi-layer imagery is necessary

Cloud and shadow detection using sequential characteristics on multi-spectral satellite images

For land and sea surface monitoring applications that rely on optical Earth Observation satellite images, it is required that cloud and cloud shadow areas in the images are detected and removed. As a consequence, the frequency of obtaining new images can be increased and short-term changes can be studied more effectively. In this paper, an algorithm is designed that is able to automatically detect clouds and shadows in medium-resolution optical multi-spectral images. The developed system is a frame-based image processing technique utilizing multiple spectral bands for feeding a cloud and shadow detector, where possible cloud contamination is recursively removed from the input images. The cloud detector utilizes Brightness Temperature Differences in the spectral regions of Far IR (FIR) and Thermal IR (TIR). After careful considerations, the reflective band FIR was adopted for usage in this Difference Image. The shadow detector uses Background Subtraction, which iteratively constructs its Reference Image automatically. This iterative nature is exploited to utilize time-sequential characteristics among the input images. After experiments, 94.6% of the clouds are detected, with a precision of 86.5%, as determined using per-pixel ground-truth data. For shadows, these statistics are 77.1% and 75.8%, respectively and may be further improved in future work. Selected mid-resolution Landsat images have been used for the validation

Cloud and shadow detection using sequential characteristics on multi-spectral satellite images

For land and sea surface monitoring applications that rely on optical Earth Observation satellite images, it is required that cloud and cloud shadow areas in the images are detected and removed. As a consequence, the frequency of obtaining new images can be increased and short-term changes can be studied more effectively. In this paper, an algorithm is designed that is able to automatically detect clouds and shadows in medium-resolution optical multi-spectral images. \u3cbr/\u3eThe developed system is a frame-based image processing technique utilizing multiple spectral bands for feeding a cloud and shadow detector, where possible cloud contamination is recursively removed from the input images. The cloud detector utilizes Brightness Temperature Differences in the spectral regions of Far IR (FIR) and Thermal IR (TIR). After careful considerations, the reflective band FIR was adopted for usage in this Difference Image. The shadow detector uses Background Subtraction, which iteratively constructs its Reference Image automatically. This iterative nature is exploited to utilize time-sequential characteristics among the input images. \u3cbr/\u3eAfter experiments, 94.6% of the clouds are detected, with a precision of 86.5%, as determined using per-pixel ground-truth data. For shadows, these statistics are 77.1% and 75.8%, respectively and may be further improved in future work. Selected mid-resolution Landsat images have been used for the validation