Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data

In the 21st century, the establishment of efficient water resource management is crucial for ensuring world water and food security. Irrigation is a significant artificial process in the hydrological cycle and presents the only way to balance between mentioned issues, where collecting knowledge is essential for developing adaptive and sustainable strategies. Considering that, the precise information about the spatio-temporal distribution of irrigated fields on a national scale is thus the initial key step for agricultural water resource management. With a high spatial, spectral, and temporal resolution, Sentinel-2 provides new possibilities in this field. This research focuses on using multispectral satellite imagery and advanced machine learning models for detecting irrigation and rainfed fields on a plot scale. Dry year images during irrigation season were used for vegetation indices calculation for three crop types: maize, soybean, and sugar beet. These three databases were used separately for training the Random Forest classifier. The results showed high overall accuracy for each three crops where soybean reached the highest 0.91, maize 0.89, while sugar beet reached 0.76. According to the results, the assumption is that the difference in accuracy between crops could be caused by the difference in the geospatial characteristic of the area, amount of data, omission in labeling crop types and rainfed fields. Irrigated agricultural fields present a challenge for classification and mapping considering the heterogeneity of the area, climate impact, and diverse crop types. This study showed that classification could be done using Sentinel-2 images, but further analysis including climate and soil data could improve the classification. This methodology has the potential to produce an annual irrigation map which is very important information for optimizing water use and making sustainable agricultural policy

D3.5 Farming System Archetypes for each CS

This deliverable provides an overview of the methods and data used for developing the Farming System Archetypes (FSAs) in the five case studies - Humber, Mulde, SouthMoravia, Bačka and Catalonia. Additionally, it discusses limitations as well as problems and presents solutions. The FSAs are a generalized typology of farming systems that are assumed to have similar response to policy change. FSAs are a major component of the BESTMAP modelling architecture because they provide linkages between many aspects of the project, especially connecting the biophysical and agent-based modelling in the case studies (CS), based on local data (e.g. IACS/LPIS, for explanation see Methodology), with the modelling of policy effects at the EU level, based on FADN micro-data within the FADN regions. The FSA framework defines the main farm characteristics determined by two main dimensions: firstly farm specialization and secondly economic size, both calculated and mapped for each farm in the CSs. ‘Farmer agents’ who belong to the same FSA are then assumed to have similar decision patterns regarding the adoption of agri-environmental schemes, based on the relationships revealed in the CS agent-based models

V.Crnojevic, “Mining Web Videos for Video Quality Assessment

Abstract: Correlating estimates of objective measures related to the presence of different coding artifacts with the quality of video as perceived by human observers is a non-trivial task. There is no shortage of data to learn from, thanks to the Internet and web-sites such as YouTube tm . There has, however, been little done in the research community to try to use such resources to advance our understanding of perceived video quality. The problem is the fact that it is not easy to obtain the Mean Opinion Score (MOS), a standard measure of the perceived video quality, for more than a handful of videos. The paper presents an approach to determining the quality of a relatively large number of videos obtained randomly from YouTube tm . Several measures related to motion, saliency and coding artifacts are calculated for the frames of the video. Programmable graphics hardware is used to perform clustering: first, to create an artifacts-related signature of each video; then, to cluster the videos according to their signatures. To obtain an estimate for the video quality, MOS is obtained for representative videos, closest to the cluster centers. This is then used as an estimate of the quality of all other videos in the cluster. Results based on 2,107 videos containing some 90,000,000 frames are presented in the paper

Remote Sensing of Poplar Phenophase and Leaf Miner Attack in Urban Forests

Remote sensing of phenology is adopted as the practice in greenery monitoring. Now research is turned towards the fusion of data from various sensors to fill in the gap in time series and allow monitoring of pests and disturbances. Poplar species were monitored for the determination of the best approach for detecting phenology and disturbances. With the adjustments that include a choice of indices, wavelengths, and a setup, a multispectral camera may be used to calibrate satellite images. The image processing pipeline included different denoising and interpolation methods. The correlation of the changes in a signal of top and lateral imaging proved that the contribution of the whole canopy is reflected in satellite images. Normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) successfully distinguished among phenophases and detected leaf miner presence, unlike enhanced vegetation index (EVI). Changes in the indices were registered before, during, and after the development of the disease. NDRE is the most sensitive as it distinguished among the different intensities of damage caused by pests but it was not able to forecast its occurrence. An efficient and accurate system for detection and monitoring of phenology enables the improvement of the phenological models’ quality and creates the basis for a forecast that allows planning in various disciplines

Remote Sensing of Poplar Phenophase and Leaf Miner Attack in Urban Forests

Remote sensing of phenology is adopted as the practice in greenery monitoring. Now research is turned towards the fusion of data from various sensors to fill in the gap in time series and allow monitoring of pests and disturbances. Poplar species were monitored for the determination of the best approach for detecting phenology and disturbances. With the adjustments that include a choice of indices, wavelengths, and a setup, a multispectral camera may be used to calibrate satellite images. The image processing pipeline included different denoising and interpolation methods. The correlation of the changes in a signal of top and lateral imaging proved that the contribution of the whole canopy is reflected in satellite images. Normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) successfully distinguished among phenophases and detected leaf miner presence, unlike enhanced vegetation index (EVI). Changes in the indices were registered before, during, and after the development of the disease. NDRE is the most sensitive as it distinguished among the different intensities of damage caused by pests but it was not able to forecast its occurrence. An efficient and accurate system for detection and monitoring of phenology enables the improvement of the phenological models’ quality and creates the basis for a forecast that allows planning in various disciplines

Soybean varieties portfolio optimisation based on yield prediction

One of the biggest problems in agriculture is concerned with seed selection. Wrong choice of seed variety cannot be compensated with fertilisation, spraying or the use of mechanisation later in the season. The purpose of this work was to design the strategy for selecting soybean varieties that should be planted on the test farm in order to maximise yield in the following season, based on the knowledge acquired from heterogeneous historical data. We propose weighted histograms regression to predict the yield of different varieties and compare our method to conventional regression algorithms. Based on the predicted yield, we perform portfolio optimisation to come up with the optimal selection of seed varieties that is to be planted. Presented algorithms and results were produced within the Syngenta Crop Challenge. (C) 2016 Elsevier B.V. All rights reserved

Uav-based land cover classification for hoverfly (Diptera : Syrphidae) habitat condition assessment: A case study on mt. stara planina (Serbia)

Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms—support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)—were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository

Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study

Remote sensing applications have gained in popularity in recent years, which has resulted in vast amounts of data being produced on a daily basis. Managing and delivering large sets of data becomes extremely difficult and resource demanding for the data vendors, but even more for individual users and third party stakeholders. Hence, research in the field of efficient remote sensing data handling and manipulation has become a very active research topic (from both storage and communication perspectives). Driven by the rapid growth in the volume of optical satellite measurements, in this work we explore the lossy compression technique for multispectral satellite images. We give a comprehensive analysis of the High Efficiency Video Coding (HEVC) still-image intra coding part applied to the multispectral image data. Thereafter, we analyze the impact of the distortions introduced by the HEVC’s intra compression in the general case, as well as in the specific context of crop classification application. Results show that HEVC’s intra coding achieves better trade-off between compression gain and image quality, as compared to standard JPEG 2000 solution. On the other hand, this also reflects in the better performance of the designed pixel-based classifier in the analyzed crop classification task. We show that HEVC can obtain up to 150:1 compression ratio, when observing compression in the context of specific application, without significantly losing on classification performance compared to classifier trained and applied on raw data. In comparison, in order to maintain the same performance, JPEG 2000 allows compression ratio up to 70:1

Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data

Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA’s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%

D4.2 Trade-off/synthesis analyses including spatial co-occurrence of ESS / biodiversity socio-economic

This document describes the interrelationships between the ecosystem services, biodiversity and socio-economic outputs modelled in the Work Package 3 (WP3), to identify bundles of co-occurring services. Furthermore, this document presents an analysis of how different types of Agri-Environmental Measures (AEM) drive trade-offs and synergies among different services. The analysis spans two AEM adoption scenarios, one without AEM and one reflecting the current AEM adoption levels, for all five Case Studies (CS) of BESTMAP