Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images

The study presents the analysis of the possible use of limited number of the Sentinel-2 and Sentinel-1 to check if crop declarations that the EU farmers submit to receive subsidies are true. The declarations used in the research were randomly divided into two independent sets (training and test). Based on the training set, supervised classification of both single images and their combinations was performed using random forest algorithm in SNAP (ESA) and our own Python scripts. A comparative accuracy analysis was performed on the basis of two forms of confusion matrix (full confusion matrix commonly used in remote sensing and binary confusion matrix used in machine learning) and various accuracy metrics (overall accuracy, accuracy, specificity, sensitivity, etc.). The highest overall accuracy (81%) was obtained in the simultaneous classification of multitemporal images (three Sentinel-2 and one Sentinel-1). An unexpectedly high accuracy (79%) was achieved in the classification of one Sentinel-2 image at the end of May 2018. Noteworthy is the fact that the accuracy of the random forest method trained on the entire training set is equal 80% while using the sampling method ca. 50%. Based on the analysis of various accuracy metrics, it can be concluded that the metrics used in machine learning, for example: specificity and accuracy, are always higher then the overall accuracy. These metrics should be used with caution, because unlike the overall accuracy, to calculate these metrics, not only true positives but also false positives are used as positive results, giving the impression of higher accuracy. Correct calculation of overall accuracy values is essential for comparative analyzes. Reporting the mean accuracy value for the classes as overall accuracy gives a false impression of high accuracy. In our case, the difference was 10–16% for the validation data, and 25–45% for the test data

Metodyka kalibracji pomiaru powierzchni działki rolnej na ortofotomapie

Tyt. z nagłówka.Bibliogr. s. 173-174.Dostępny również w formie drukowanej.STRESZCZENIE: W niniejszym artykule zaprezentowano wybrane zagadnienia będące przedmiotem badań w ramach projektu UE Validation of methods for measurement of land parcel areas realizowanego i koordynowanego w AGH (Kraków) w 2005 roku. W ramach tego projektu wykonywano pomiary teledetekcyjne (dwa eksperymenty pomiarowe - AGH) oraz pomiary GPS (jeden eksperyment - UWM w Olsztynie). Pomiary zostały zaplanowane i opracowane zgodnie z normą ISO 5725 przez USI Gemblaux w Belgii. Celem projektu było opracowanie metodyki kalibracji pomiarów powierzchni działek rolnych. W publikacji przedstawiono krótką analizę obowiązującego podejścia do tego zagadnienia oraz opisano proponowaną przez autorkę alternatywną metodę kalibracji. W metodzie tej parametrem wykorzystywanych dla określania dokładności powierzchni działki rolnej jest błąd położenia punktu charakteryzujący dokładność pomiaru. Do eksperymentu teledetekcyjnego wykorzystano lotnicze i satelitarne ortofotomapy o rozdzielczości 0,2-2,5 m. 36 działek rolnych było wektoryzowanych przez 6-12 operatorów. W 1 eksperymencie wykonano 3888 pomiarów (w 2 eksperymencie - 1296). Wyniki opracowano zgodnie z normą SO 5725. Dla ortofotomap o bardzo wysokiej rozdzielczości (wielkość piksela O,2-1 m) otrzymano błąd położenia punktu: ok. +/-2 m, dla obrazów satelitarnych EROS (2 m) i SPOT (2,5 m) uzyskano błąd położenia punktu: ok. +/-5 m. Jako optymalny zestaw dla kalibracji metodyki określania powierzchni działek rolnych w oparciu o ortofotomapy zaproponowano: 30-40 działek, 3 operatorów, 3 dni pomiarowe i 3 powtórzenia. SŁOWA KLUCZOWE: IACS, ortofotomapa, kalibracja pomiaru powierzchni działki rolnej. ABSTRACT: In the paper chosen results of the UE project Validation of methods for measurement of land parcel areas are presented. The project was realized and coordinated at the AGH in Krakow. During the project 3 measurement experiments were performed: 2 remote sensing (AGH Kraków) and 1 GPS (UWM Olsztyn). The experiment was prepared and statistical analyzed at USI Gembleux. The aim of the study was to elaborate the validation method for land parcel measurements. In the paper short discussion of the existing approach is presented and alternative method proposed by author is described. Results of remote sensing experiments are shown. Point position error, characterizing measurements technique, was assumed as a parameter for area accuracy assessment. In RS experiments airborne and satellite ortophotomaps, with pixel size of 0.2-2.5 m were applied. 36 land parcels were digitized by 6-12 operators. In experiment 1 - 3888 measurements were made (1296 - in experiment 2). Data were according ISO 5725 analyzed. For VHR ortophotomaps (pixel size 0.2-1 m) we obtained the point position error of ca. +/- 2m. For EROS and SPOT (pixel size 2 and 2.5 m) point position error was ca +/-5 m. An optimal measurement set for proposal of validation method for RS is: 30-40 parcels, 3 operators, 3 days and 3 repetitions. KEYWORDS: ortophotomap, IACS, validation, land parcel area

A new approach to DTM error estimation basing on Laplacian probability distribution function

A Digital Terrain Model (DTM) derived from Airborne Laser Scanning (ALS) was the subject of our research. In this paper, the vertical accuracy of the DTM was analyzed on the basis of the commonly used statistics, i.e. mean error and standard deviation, assuming a normal (Gauss) error distribution. The further approach, the so-called robust method (Höhle, Höhle 2009), was also tested, where the median was a substitute for the mean error and the Normalized Median Absolute Deviation (NMAD) for the standard deviation. An alternative method based on the Laplace function is proposed in the paper for describing the probability density function, where the parameters of the Laplace function are proposed for DTM error estimation. The test area was near the Joint Research Centre in Ispra, Italy; raw ALS data covering the test area were collected in 2005 and processed for DTM generation. Accuracy analysis was performed based on the comparison of DTM with the raw ALS data and with in-situ height measurements. The distribution of DTM errors calculated from ALS data was significantly non-normal, confirming other results reported in the literature. The Gauss distribution function considerably overestimated the vertical DTM errors; however, the robust method underestimated them. The Laplace function matched the error histograms the best, and accuracy parameters derived from this function could be considered as an alternative method for DTM accuracy evaluation.JRC.H.4-Monitoring Agricultural Resource

Influence by the number of measured parcel boundary points on the accuracy of land parcel area calculation

The question undertaken in the research was: how does the number of measurement points influence the error of the land parcel's area calculation? The field measurements relied on continuous land parcel's GPS measurement with various intervals of the record. Simulated area accuracy calculated from Gauss's formula increases with the increase number of points. The experiment of continuously GPS measurement did not confirm simulated accuracy in all points¿ number range. The largest difference was observed for few number of measuring points, for which the prognosis error was much bigger (almost 2 times) than the error obtained during measurements. A relationship between amount of the points and area accuracy was also compared with the literature (Bogaert i et. al. 2005). The results of our research also only partly confirmed results published in the literature. Main discrepancy was observed for small point¿s amount. In our research, area error increases with decreasing number of points, in the literature on the contrary area error decreases. Explanation of this phenomenon requires further research especially because that our field measurements not correspond full to the simulation from the literature.JRC.DDG.H.4-Monitoring agricultural resource

The Application of Remote Sensing Techniques and Spectral Analyzes to Assess the Content of Heavy Metals in Soil – A Case Study of Barania Góra Reserve, Poland

The understanding of the spatial and temporal dynamics of farmland processes is essential to ensure the proper crop monitoring and early decision making needed to support efficient resource management in agriculture. By creating appropriate crop management strategies, one can increase harvest efficiency while reducing costs, waste, chemical spraying, and inhibiting the impact of biotic and abiotic factors on crop stress. Only reliable spatial information makes it possible to comprehend the influence of various factors on the environment. The main objective of the research presented in the paper was to assess the possibility of using maps of vegetation and soil indices, such as NDVI, SAVI, IRECI, CIred-edge, PSRI and HMSSI, calculated on the basis of images from the Sentinel-2 satellite, to qualitatively determine the increased amount of heavy metals in the soil in the areas of small agricultural plots around the Barania Góra nature reserve in Poland. The conducted pilot project shows that the spectral indices: NDVI, SAVI, IRECI, CIred-edge, PSRI, and HMSSI, calculated on the basis of images from Sentinel-2, have the potential to assess the content of nickel zinc, chromium and cobalt in the soil on agricultural plots. However, the confirmation of the obtained results requires continuation of the research

UAS Applications With Societal Benefi ts JRC’s UAS-Related Activities

Unmanned Aerial Systems (UAS) emerged in recent years as a promising technology with potential applications to different fields of JRC’s remit, ranging from maritime surveillance to monitoring of agricultural resources. Since 2010 the JRC-IPSC carried out several UAS maritime surveillance campaigns aimed assessing the potential of UAS for maritime surveillance, in particular the detection of small boats often involved in unlawful maritime activities, such as illegal immigration, drugs trafficking, smuggling and piracy. The IES is also planning to use UAS for monitoring of agricultural resources and management policies. The article briefly describes the JRC past, present and future UAS activities.JRC.G.4-Maritime affair

Geomatics in support of the Common Agricultural Policy - Proceedings of the 16th GeoCAP Annual Conference, 2010

The 2010 Annual Conference was the 16th edition. The conference entitled „Geomatics in support of the CAP. was held in Bergamo and organised by the GeoCAP action of the Joint Research Centre (Ispra, Italy) alone. The conference covered the 2010 Control with Remote sensing campaign activities and ortho-imagery use in all the CAP management and control procedures. There has been a specific focus on the Land Parcel Identification Systems quality assessment process. The conference was structured over three days – 24th to 26th November. The first day was mainly dedicated to future Common Agriculture Policy perspectives and futures challenges in Agriculture as well as overview of 2010 CwRS campaign. The second was shared in technical parallel sessions addressing the following topics: i) LPIS Quality Assurance and geo-databases features; ii) New sensors, new software, and their use within the CAP, and iii) Good Agriculture and Environmental Conditions (GAEC): control methods and implementing measures. The last day was dedicated to the GPS validation process and to the conclusions of the conference. The presentations were made available on line, and this publication represents the best presentations judged worthy of inclusion in a conference proceedings aimed at recording the state of the art of technology and practice of that time.JRC.DDG.H.4-Monitoring agricultural resource