ON THE APPLICATION OF REMOTE SENSING TIME SERIES ANALYSIS FOR LAND COVER MAPPING: SPECTRAL INDICES FOR CROPS CLASSIFICATION

Abstract. This study aims to introduce a semi-automatic classification workflow for the production of a land use/land cover (LULC) map of the island of Sardinia (Italy) following the CORINE legend schema, and a ground spatial resolution compatible with a scale of 1:25.000. The classification is based on free high-resolution satellite imagery from Sentinel-1 and Sentinel-2 collected in 2020, ancillary data derived from Sardinian Geoportal, Joint Research Centre (JRC) and OpenStreetMap. The LULC map production includes three steps: 1) pixel-based classification, realized with two different approaches, that use i) information derived from existing thematic maps eventually re-coded in case of incoherencies observed between datasets and/or satellite data products, and ii) spectral indices and parameter thresholds defined on the basis of multitemporal analysis; 2) segmentation of Sentinel-1 and 2 annual composites, and pre-labelling of segments with the pixel-based classified map, obtaining the preliminary map; 3) visual inspection procedure in order to confirm, or re-assign, classes to polygons. The accuracy of the preliminary map was tested in a sample area and on specific class of non-irrigated crops through ground truth data collected from a detailed photo-interpretation, estimating 97% of overall accuracy. The results show a great improvement from existing thematic maps in terms of detail, with the possibility of a yearly updating of the map via automatic processes. However, some limitations were found, due to the high fragmentation of Sardinian landscape and the high variety of crop types and agricultural practices, that could affect the efficiency of the classifier

Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version

The use of remotely sensed data for forest biomass monitoring : a case of forest sites in north-eastern Armenia

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn recent years there has been an increasing interest in the use of synthetic aperture radar (SAR) data and geospatial technologies for environmental monitoring․ Particularly, forest biomass evaluation was of high importance, as forests have a crucial role in global carbon emission. Within this study we evaluate the use of Sentinel 1 C-band multitemporal SAR data with combination of Alos Palsar L-band SAR and Sentinel 2 multispectral remote sensing (RS) data for mapping forest aboveground biomass (AGB) of dry subtropical forests in mountainous areas. Field observation from National Forest Inventory was used as a ground truth data. As the SAR data suffers greatly by the complex topography, a simple approach of aspect and slope information as forestry ancillary data was implemented directly in the regression model for the first time to mitigate the topography effect on radar backscattering value․ Dense time-series analysis allowed us to overcome the SAR saturation by the forest phenology and select the optimal C-band scene. Image texture measures of SAR data has been strongly related to the biomass distribution and has robustly contributed to the prediction․ Multilinear Stepwise Regression allowed to select and evaluate the most relevant variables for AGB. The prediction model combining RS with ancillary data explained the 62 % of variance with root-mean-square error of 56.6 t ha¯¹. The study also reveals that C-band SAR data on forest biomass prediction is limited due to their short wavelength. Further, the mountainous condition is a major constraint for AGB estimation. Additionally, this research demonstrates a positive outcome in forest AGB prediction with freely accessible RS data

Combining multitemporal optical and SAR data for LAI imputation with BiLSTM network

The Leaf Area Index (LAI) is vital for predicting winter wheat yield. Acquisition of crop conditions via Sentinel-2 remote sensing images can be hindered by persistent clouds, affecting yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery, and the ratio between its cross- and co-polarized channels (C-band) shows a high correlation with time series LAI over winter wheat regions. This study evaluates the use of time series Sentinel-1 VH/VV for LAI imputation, aiming to increase spatial-temporal density. We utilize a bidirectional LSTM (BiLSTM) network to impute time series LAI and use half mean squared error for each time step as the loss function. We trained models on data from southern Germany and the North China Plain using only LAI data generated by Sentinel-1 VH/VV and Sentinel-2. Experimental results show BiLSTM outperforms traditional regression methods, capturing nonlinear dynamics between multiple time series. It proves robust in various growing conditions and is effective even with limited Sentinel-2 images. BiLSTM's performance surpasses that of LSTM, particularly over the senescence period. Therefore, BiLSTM can be used to impute LAI with time-series Sentinel-1 VH/VV and Sentinel-2 data, and this method could be applied to other time-series imputation issues

SAR Sentinel 1 imaging and detection of palaeo-landscape features in the mediterranean area

The use of satellite radar in landscape archaeology offers great potential for manifold applications, such as the detection of ancient landscape features and anthropogenic transformations. Compared to optical data, the use and interpretation of radar imaging for archaeological investigations is more complex, due to many reasons including that: (i) ancient landscape features and anthropogenic transformations provide subtle signals, which are (ii) often covered by noise; and, (iii) only detectable in specific soil characteristics, moisture content, vegetation phenomenology, and meteorological parameters. In this paper, we assessed the capability of SAR Sentinel 1 in the imaging and detection of palaeo-landscape features in the Mediterranean area of Tavoliere delle Puglie. For the purpose of our investigations, a significant test site (larger than 200 km2) was selected in the Foggia Province (South of Italy) as this area has been characterized for millennia by human frequentation starting from (at least) the Neolithic. The results from the Sentinel 1 (S-1) data were successfully compared with independent data sets, and the comparison clearly showed an excellent match between the S-1 based outputs and ancient anthropogenic transformations and landscape features

Mapping a European spruce bark beetle outbreak using sentinel-2 remote sensing data

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetle

Mapping intra- and inter-annual dynamics in wetlands with multispectral, thermal and SAR time series

Kartierung der intra- und interannuellen Dynamik von Feuchtgebieten mit multispektralen, thermischen und SAR-Zeitreihen Die Analyse der aktuellen räumlichen Verbreitung und der zeitlichen Entwicklung von Feuchtgebieten stellt eine äußerst komplexe Aufgabe dar, welche durch die Saisonalität, die schwierige Zugänglichkeit und die besonderen Eigenschaften als Ökoton bedingt ist. Erdbeobachtungssysteme sind somit das am besten geeignete Werkzeug, um zeitliche und räumliche Muster von Feuchtgebieten auf globaler Ebene zu beobachten (saisonale Veränderungen und Langzeit-Trends) und um den Einfluss der menschlichen Aktivitäten auf ihre physischen und biologischen Eigenschaften zu untersuchen. Zur Kartierung von raum-zeitlichen Mustern wurden Zeitreihen von Radar- (Sentinel-1), Multispektral- (Sentinel-2) und Thermal-Satellitendaten (MODIS) in fünf Untersuchungsgebieten, mit für Feuchtgebiete unterschiedlichen typischen Charakteristika, untersucht. In Kapitel 1 werden die Problematik in Bezug auf die Definition von Feuchtgebieten erläutert und allgemeine Degradations-Trends beschrieben. Die Kapitel 2 und 3 behandeln einen Algorithmus, der Veränderungen mithilfe von SAR-Zeitreihen feststellt, sowie die Vorteile des Cloud-Computings für das operationelle Monitoring saisonaler Muster und die Erkennung kurzfristig auftretender Veränderungen. In den Kapiteln 4 und 5 werden die zwei Hauptursachen für den Verlust von Feuchtgebieten betrachtet: der Staudammbau und die Ausdehnung landwirtschaftlicher Flächen. In Kapitel 4 werden dichte Zeitreihen multispektraler (Sentinel-2) und SAR-Daten (Sentinel-1) verwendet, um die Feuchtgebiete Albaniens – eines Landes in dem konträre Pläne zum Ausbau seines Wasserkraftpotentials und dem Schutz intakter Flussökosysteme zu Spannungen führen – landesweit zu kartieren. Die synergetischen Vorteile, die sich durch die Fusionierung von multispektralen und SAR-Daten für die Klassifikation ergeben, werden dabei herausgestellt. Kapitel 5 veranschaulicht, dass die Kilombero-Überschwemmungsebene in Tansania ein großes und bedeutendes Feuchtgebiet ist, das in den vergangenen Jahren infolge der weitgehend unkontrollierten Ausbreitung landwirtschaftlicher Flächen in seiner Ausdehnung und seiner Ökologie stark beeinträchtigt wurde. Um die Auswirkungen der Landnutzungsänderungen des Feuchtgebietes während der vergangenen 18 Jahre zu analysieren, wurden eine Zeitreihe (2000 bis 2017) thermaler Daten (MODIS) analysiert. Die drei für die Zeitreihenanalyse angewandten Modelle zeigen, wie landwirtschaftliche Praktiken die Landoberflächentemperatur in den landwirtschaftlich genutzten Gebieten sowie in den angrenzenden natürlichen Feuchtgebieten erhöht haben.Due to wetlands’ seasonality, their difficult access and ecotone character, determining their actual extension and trends over time is a complex task. Earth Observation systems are the most appropriate tool to monitor their spatio-temporal patterns (seasonal changes and long term trends) at global scales, and to study the effects that human activities have in their physical and biological properties. In this work I use time series of radar (Sentinel-1), multispectral (Sentinel-2) and thermal (MODIS) imagery to map the spatio-temporal patterns in 5 wetlands of different characteristics. First, I introduce in chapter 1 the problematic of wetlands’ definitions and their degradation trends. I continue with a brief introduction on remote sensing, time series analysis, and their applications on wetlands’ research and management. In chapters 2 and 3 I implement an algorithm for change detection of time series of Sentinel-1 images and demonstrate the advantages of cloud computation for operational monitoring. In chapters 4 and 5 I address two of the main causes of wetland degradation: dam building and agricultural expansion. In chapter 4 I use dense time series of Sentinel-1 and Sentinel-2 images map all the wetlands of Albania; a country struggling between developing its large hydropower potential or preserving its intact and valuable river ecosystems. I evaluate the synergic advantages of fusing multispectral and radar imagery in combination with knowledge-based rules to produce classification of higher thematic and spatial resolutions. In chapter 5 I present how the Kilombero Floodplain, in Tanzania, has been degraded during the last years due to uncontrolled farmland expansion. I use a time series of thermal imagery (MODIS) from 2000 until 2017 to analyze the effect of land use changes on the wetland. I compare three models for time series analysis and reveal how farming practices have increased the surface temperature of the farmed area, as well as in adjacent natural wetlands.Mapeo de las dinámicas inter- e intra-anuales en humedales con series temporales de imágenes multiespectrales, termales y de radar Debido a la estacionalidad de los humedales, su difícil acceso y sus características de ecotono, determinar su actual extensión y sus tendencias a lo largo del tiempo es una tarea compleja. Los sistemas de observación terrestres son la herramienta más apropiada para monitorear sus patrones espacio-temporales (estacionalidad y tendencias a largo plazo) a escalas globales, y para estudiar los efectos que las actividades humanas causan en sus propiedades físicas y biológicas. En esta tesis uso series temporales de imágenes radar (Sentinel-1), multiespectrales (Sentinel-2) y termales (MODIS) para mapear los patrones espacio-temporales de 5 humedales de diferentes características. En el capítulo 1 describo los retos que derivan de las diferentes definiciones que existen de los humedales. También presento las tendencias globales de degradación que la mayoría de los humedales continúan experimentando en los últimos años. Continúo con una breve introducción de los sistemas de teledetección remota, análisis de series temporales, y sus aplicaciones a la investigación y gestión de los humedales. En los capítulos 2 y 3 implemento un algoritmo de detección de cambios para series temporales de imágenes radar, y muestro las ventajas de usar sistemas de computación en la nube para monitorear cambios en la cobertura del suelo a corto plazo. En los capítulos 4 y 5 trato con dos de las causas más comunes de degradación de humedales: la construcción de presas y la expansión de la agricultura. En el capítulo 4 uso series temporales de imágenes multiespectrales (Sentinel-2) y radar (Sentinel-1) para mapear todos los humedales Albania; un país que se debate entre desarrollar su potencial hidroenergético o preservar sus valiosos e intactos ecosistemas de rivera. Mediante la fusión de imágenes radar y multiespectrales y el uso de reglas de decisión genero un mapa de suficiente resolución espacial y temática para que pueda ser usado por sectores interesados y gestores. En el capítulo 5 presento como las llanuras inundables de Kilombero, en Tanzania, han sido degradadas durante los últimos años debido a la expansión incontrolada de la agricultura. Usando series temporales de imágenes termales (MODIS) desde 2000 hasta 2017 y mapas de cambios de usos del suelo, determino los efectos que estos cambios han tenido en el humedal. Comparo 3 modelos diferentes de análisis de series temporales y muestro cómo la expansión de la agricultura ha incrementado la temperatura superficial terrestre, no solo de la zona cultivada, sino también de zonas adyacentes aún naturales

Use of Sentinel-2 for forest classification in Mediterranean environments

Spatially-explicit information on forest composition provides valuable information to fulfil scientific, ecological and management objectives and to monitor multiple changes in forest ecosystems. The recently developed Sentinel-2 (S2) satellite imagery holds great potential for improving the classification of forest types at medium-large scales due to the concurrent availability of multispectral bands with high spatial resolution and quick revisit time. In this study, we tested the ability of S2 for forest type mapping in a Mediterranean environment. Three operational S2 images covering different phenological periods (winter, spring, summer) were processed and analyzed. Ten 10 m and 20 m bands available from S2 and four vegetation indices (VIs) were used to evaluate the ability of S2 to discriminate forest categories (conifer, broadleaved and mixed forests) and four forest types (beech forests; mixed spruce-fir forests; chestnut forests; mixed oak forests). We found that a single S2 image acquired in summer cannot discriminate neither the considered forest categories nor the forest types and therefore multitemporal images collected at different phenological periods are required. The best configuration yielded an accuracy > 83% in all considered forest types. We conclude that S2 can represent an effective option for repeated forest monitoring and mapping