UAV and satellite imagery applied to alien species mapping in NW Spain

Image classification stands as an essential tool for automated mapping, that is demanded by agencies and stakeholders dealing with geospatial information. Decreasing costs or UAV-based surveying and open access to high resolution satellite images such as that provided by European Union’s Copernicus programme are the basis for multi-temporal landscape analysis and monitoring. Besides that, invasive alien species are considered a risk for biodiversity and their inventory is needed for further control and eradication. In this work, a methodology for semi-automatic detection of invasive alien species through UAV surveying and Sentinel 2 satellite monitoring is presented and particularized for Acacia dealbata Link species in the province of Pontevedra, in NW Spain. We selected a scenario with notable invasion of Acaciae and performed a UAS surveying to outline feasible training areas. Such areas were used as bounds for obtaining a spectral response of the cover from Sentinel 2 images with a level of processing 2A, that was used for invasive area detection. Sparse detected areas were treated as a seed for a region growing step to obtain the final map of alien species.Deputación de Pontevedra | Ref. 17/410.1720.789.0

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI bands to 15-m using the spatial detail provided by the Landsat-8 15-m panchromatic band and then reprojects and resamples the downscaled 15-m data into registration with Sentinel-2A 20-m data. The LPAD method is demonstrated using pairs of contemporaneous Landsat-8 OLI and Sentinel-2A MSI images sensed less than 19 min apart over diverse geographic environments. The LPAD method is shown to introduce less spectral and spatial distortion and to provide visually more coherent data than conventional bilinear and cubic convolution resampled 20-m Landsat OLI data. In addition, results for a pair of Landsat-8 and Sentinel-2A images sensed one day apart suggest that image fusion should be undertaken with caution when the images are acquired under different atmospheric conditions. The LPAD source code is available at GitHub for public use

Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping

International audienc

Система автоматизованої кореєстрації оптичних та радарних супутникових знімків земної поверхні

Незважаючи на зростаючий інтерес до спільного використання оптичних і радарних знімків, проблема їх кореєстрації залишається актуальною. Метою магістерської дисертації є розроблення модифікованого алгоритму для підвищення якості кореєстрації оптичних і супутникових знімків. Для досягнення мети було виконано наступні завдання: проаналізовано об’єкт та предмет дослідження, проведено аналіз існуючих методів кореєстрації, визначено вимоги до системи, створено модифікований алгоритм кореєстрації, реалізовано програмне забезпечення на основі розробленого алгоритму та проведено аналіз для реалізації проекту як стартапу. Об’єктом дослідження є супутникові знімки. Предметом дослідження є кореєстрація оптичних і радарних супутникових знімків. Результати магістреської дисертації впроваджені та активно використовуються ТОВ «АГРО ФЛОУ СИСТЕМ».Despite the growing interest in sharing optical and radar images, the problem of registering them remains relevant. The purpose of the master's thesis is to develop a modified algorithm for improving the quality of registration of optical and satellite images. To achieve this goal, the following tasks were performed: the object and subject of the study were analyzed, the existing methods of registration were analyzed, the requirements for the system were determined, the modified algorithm of registration was created, the software was developed on the basis of the developed algorithm, and the analysis was carried out for realization. project as a startup. The object of the study is satellite imagery. The subject of the study is the registration of optical and radar satellite images. The results of the master's thesis are implemented and are being actively used by AGRO FLOW SYSTEM LLC

Remote Sensing of Environment: Current status of Landsat program, science, and applications

Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat- 1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and followup with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat

Análisis de tendencias temporales del índice mejorado de la vegetación (EVI) en tres ecosistemas de la subcuenca del río Chambo durante el período 2013-2020.

La presente investigación tuvo por objetivo analizar el índice de vegetación EVI en un periodo de tiempo (2013 al 2020) se utilizó imágenes satelitales, el cual permitió monitorear el estado de la vegetación, aumentando su sensibilidad en casos de altas densidades de biomasa. La metodología que se utilizó para el primer objetivo se basó en la descarga de imágenes satelitales Landsat 8 mediante la plataforma Google Earth Engine, para delimitar el área de estudio en el cual se determinó el índice EVI se empleó software ArcGIS 10.2 con la fórmula: 2.5*(img1["B5"]-img1["B4"])/(img1["B5"]+6*img1["B4"]-(7.5*img1["B2"])+1). Para el segundo objetivo se utilizó el software TREND el cual permitió detectar el análisis de tendencias más fiables entre las precipitaciones de los ecosistemas. Y para el tercer objetivo se elaboró un árbol de problemas, objetivos y alternativas mediante la matriz del marco lógico consistió en desarrollar una serie de paso en el cual se va a recolectar y analizar la información necesaria para la investigación. Los resultados mostraron un valor máximo EVI promedio anual para el ecosistema Arbustal siempre verde y herbazal del páramo de 0.28 al año 2015 y mínimo de 0.26 al año 2020. Para el ecosistema Herbazal del páramo el valor máximo EVI de 0.28 al año 2015 - 2017 y su valor mínimo 0.25 para el año 2013 - 2018. Finalmente, el ecosistema Herbazal y Arbustal siempre verde subnival del páramo, presentó un valor máximo de 0.24 para el año 2014 y valor mínimo de 0.22 para el año 2013. Se concluye también que la presente investigación seria una parte dentro de la elaboración de la matriz de marco lógico de la propuesta para el manejo y conservación de los ecosistemas. Se recomienda dar seguimiento a este tipo de investigaciones en periodos continuos para ver la variación de los ecosistemas.The aim of this research was to analyze the EVI vegetation index over a period of time (2013 to 2020) using satellite images, which allowed monitoring the state of vegetation, increasing its sensitivity in cases of high biomass densities. The methodology used for the first objective was based on downloading Landsat 8 satellite images using the Google Earth Engine platform, to delimit the study area in which the EVI index was determined, ArcGIS 10.2 software was used with the formula: 2.5*(img1["B5"]- img1["B4"])/(img1["B5"]+6*img1["B4"]- (7.5*img1["B2"])+1). For the second objective, the TREND software was used it allowed detecting the most reliable trend analysis between ecosystem precipitation. And for the third objective, a tree of problems, objectives and alternatives was elaborated using the logical framework matrix, which consisted on developing a series of steps in where the necessary information for the research would be collected and analyzed. The results showed a maximum annual average EVI value for the evergreen shrubland and páramo grassland ecosystem of 0.28 in 2015 and a minimum of 0.26 in 2020. For the páramo grassland ecosystem, the maximum EVI value was 0.28 in 2015 - 2017 and its minimum value was 0.25 in 2013 - 2018. Finally, the ecosystem Herbazal y Arbustal evergreen subnival of the páramo, presented a maximum value of 0.24 for the year 2014 and minimum value of 0.22 for the year 2013. It is concluded that this research would be a part of the elaboration of the logical framework matrix of the proposal for the management and conservation of ecosystems. It is recommended to follow up this research in uninterrupted periods to observe the ecosystems variation

The role of dispersal in range change in birds

Eurasian reed warbler Acrocephalus scirpaceus expanded its range in Great Britain in the late 20th and early 21st centuries. The role of dispersal in this range expansion was investigated. Inference of the mechanisms underlying the range dynamics drew on fieldwork, analysis of large observational datasets, and a simulation model; this model was run in a reedbed map of Britain, generated from satellite data using machine learning. Breeding season temperature sets up reed warbler’s range limit in Britain directly, by influencing occupancy in the current year, perhaps mediated through reed Phragmites australis phenology. Although components of productivity were positively related to temperature, these and adult survival did not decline to the range edge. There was therefore no evidence that demography plays a role in limiting reed warbler’s range in Britain; however, not all aspects of demography were investigated. Survival was negatively related to temperature, and simulations suggested that this may allow reed warbler to maintain a more northerly range limit than without such a relationship. Reed warbler’s range expansion can be explained by a gradual equilibration with climate space, enabled by long-distance dispersal: only rare long-distance dispersing individuals matched the rate of range expansion. Reed warbler’s range edge tracked climate change, but the bulk of the population lagged behind. This could be due to dispersal-limitation, or perhaps newly established populations grow too slowly to generate sufficient emigrants. Simulations suggested that reed warbler’s range size is more sensitive to demography than to dispersal. The number of fledglings per breeding attempt increased over time, probably due to climate warming, and could have increased emigration; if so, this may be the cause of a more rapid movement in the range centroid later in the study period. Emigration, transition and immigration may therefore play different roles in reed warbler’s range dynamics in space and time

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change