Over the last few years, multispectral and thermal remote sensing imagery from unmanned aerial vehicles (UAVs) has found application in agriculture and has been regarded as a means of field data collection and crop condition monitoring source. The integration of information derived from the analysis of these remotely sensed data into agricultural management applications facilitates and aids the stakeholder’s decision making. Whereas agricultural decision support systems (DSS) have long been utilised in farming applications, there are still critical gaps to be addressed; as the current approach often neglects the plant’s level information and lacks the robustness to account for the spatial and temporal variability of environmental parameters within agricultural systems. In this paper, we demonstrate the use of a custom built autonomous UAV platform in providing critical information for an agricultural DSS. This hexacopter UAV bears two cameras which can be triggered simultaneously and can capture both the visible, near-infrared (VNIR) and the thermal infrared (TIR) wavelengths. The platform was employed for the rapid extraction of the normalized difference vegetation index (NDVI) and the crop water stress index (CWSI) of three different plantations, namely a kiwi, a pomegranate, and a vine field. The simultaneous recording of these two complementary indices and the creation of maps was advantageous for the accurate assessment of the plantation's status. Fusion of UAV and soil scanner system products pinpointed the necessity for adjustment of the irrigation management applied. It is concluded that timely CWSI and NDVI measures retrieved for different crop growing stages can provide additional information and can serve as a tool to support the existing irrigation DSS that had so far been exclusively based on telemetry data from soil and agrometeorological sensors. Additionally, the use of the multi-sensor UAV was found to be beneficial in collecting timely, spatio-temporal information for the fusion with ground-based proximal sensing data. This research work was designed and deployed in the frame of the project "AGRO_LESS: Joint reference strategies for rural activities of reduced inputs".
Adoption of precision agriculture techniques requires the development of specialized tools that provide spatially distributed information. Both flying platforms and airborne sensors are being continuously evolved to cover the needs of plant and soil sensing at affordable costs. Due to restrictions in payload, flying platforms are usually limited to carry a single sensor on board. The aim of this work is to present the development of a vertical take-off and landing autonomous unmanned aerial vehicle (VTOL UAV) system for the simultaneous acquisition of high resolution vertical images at the visible, near infrared (VNIR) and thermal infrared (TIR) wavelengths. A system was developed that has the ability to trigger two cameras simultaneously with a fully automated process and no pilot intervention. A commercial unmanned hexacopter UAV platform was optimized to increase reliability, ease of operation and automation. The designed systems communication platform is based on a reduced instruction set computing (RISC) processor running Linux OS with custom developed drivers in an efficient way, while keeping the cost and weight to a minimum. Special software was also developed for the automated image capture, data processing and on board data and metadata storage. The system was tested over a kiwifruit field in northern Greece, at flying heights of 70 and 100m above the ground. The acquired images were mosaicked and geo-corrected. Images from both flying heights were of good quality and revealed unprecedented detail within the field. The normalized difference vegetation index (NDVI) was calculated along with the thermal image in order to provide information on the accurate location of stressors and other parameters related to the crop productivity. Compared to other available sources of data, this system can provide low cost, high resolution and easily repeatable information to cover the requirements of precision agriculture.
Leaf Area Index (LAI) is considered to be a key parameter of ecosystem processes and it is widely used as input to biogeochemical process models that predict net primary production (NPP) or can be a useful parameter for crop yield prediction and crop stress assessment as well as estimation of the exchanges of carbon dioxide, water, and nutrients in forests. LAI can be derived from satellite optical data using models referred to physical-based approaches, which describe the physical processes of energy flow in the soil-vegetation-atmosphere system, and models using empirically derived regression relationships based on spectral vegetation indices (VIs). The first category of models are more general in application because they can account for the different sources of variability, although in many cases the information needed to constrain model inputs is not available. In contrast, empirical models depend on the site and time. The aim of this paper is to create a reliable semi-empirical method, applied in two Mediterranean sites, to estimate LAI with high spatial resolution images. The model uses a minimum dataset of a Landsat 5 TM or SPOT 4 XS image, land cover map and DEM for each area. Specifically, this model calculates the reflectance of initial bands implementing topographic correction with the aid of DEM and metadata of the images and afterwards uses a list of NDVI values that correspond to certain LAI values on different land cover types which has been proposed by the MODIS Land Team. This model has been applied in two areas; in the river basin of Nestos (Greece and Bulgaria) and in the river basin of Tamega (Portugal). The predicted LAI map was validated with ground truth data from hemispherical images showing high correlation, with r reaching 0.79 and RMSE less than 1 m2/m2.
The aim of this work was to produce water quality parameter maps for the marine area of the Danube Delta using remotely sensed data and to validate the results with in-situ measurements. For this reason, satellite images from ENVISAT/MERIS and Aqua/MODIS were used along with collocated in-situ measurements. The latter were in-sync with the satellite images acquisition so that rigorous and validation could be performed. Chlorophyll-a concentration and total suspended matter were estimated using the CASE-II algorithm and MERIS satellite images, while sea surface temperature was estimated from MODIS Ocean Team products. The results show that the satellite images covered the study area completely, with some data gaps due to cloud coverage. Comparisons show a good correspondence with in-situ measurements. Thus, the time series of satellite images that was produced suggests that it is possible to monitor the biological changes on an operational basis. The produced maps described a detailed spatial pattern of chlorophyll-a and total suspended matter that could not have been identified from the sparse in-situ measurements.