31 32Although satellite-based sensors have made vegetation data series available for several decades, 33 the detection of vegetation trend and change is not yet straightforward. This is mainly due to the 34 quality of the available change detection algorithms, which seldom meet users' main need for 35 identifying and characterizing both abrupt and non-abrupt changes, without sacrificing accuracy 36 or computational speed. We propose a user-friendly program for analysing vegetation time 37 series, with two main application domains: generalising vegetation trends to main features, and 38 characterizing vegetation trend changes. This program, Detecting Breakpoints and Estimating 39 Segments in Trend (DBEST) uses a novel segmentation algorithm which simplifies the trend into 40 linear segments using one of three user-defined parameters: a generalisation-threshold parameter 41 δ, the m largest changes, or a threshold β for the magnitude of changes of interest for detection. 42The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and 43 estimates for the characteristics (time and magnitude) of the change. DBEST was tested and 44 evaluated using simulated Normalized Difference Vegetation Index (NDVI) data at two sites, 45 which included different types of changes. Evaluation results demonstrate that DBEST quickly 46 3 and robustly detects both abrupt and non-abrupt changes, and accurately estimates change time 47 and magnitude. 48 49DBEST was also tested using data from the Global Inventory Modeling and Mapping Studies 50 (GIMMS) NDVI image time series for Iraq for the period 1982-2006, and was able to detect and 51 quantify major change over the area. This showed that DBEST is able to detect and characterize changes over large areas. We conclude that DBEST is a fast, accurate and flexible tool for trend 53 detection, and is applicable to global change studies using time series of remotely sensed data 54 sets. 55 56 57

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Mangroves are among the most productive ecosystems in existence, with many ecological benefits. Therefore, generating accurate thematic maps from mangrove ecosystems is crucial for protecting, conserving, and reforestation planning for these valuable natural resources. In this paper, Sentinel-1 and Sentinel-2 satellite images were used in synergy to produce a detailed mangrove ecosystem map of the Hara protected area, Qeshm, Iran, at 10 m spatial resolution within the Google Earth Engine (GEE) cloud computing platform. In this regard, 86 Sentinel-1 and 41 Sentinel-2 data, acquired in 2019, were employed to generate seasonal optical and synthetic aperture radar (SAR) features. Afterward, seasonal features were inserted into a pixel-based random forest (RF) classifier, resulting in an accurate mangrove ecosystem map with average overall accuracy (OA) and Kappa coefficient (KC) of 93.23% and 0.92, respectively, wherein all classes (except aerial roots) achieved high producer and user accuracies of over 90%. Furthermore, comprehensive quantitative and qualitative assessments were performed to investigate the robustness of the proposed approach, and the accurate and stable results achieved through cross-validation and consistency checks confirmed its robustness and applicability. It was revealed that seasonal features and the integration of multi-source remote sensing data contributed towards obtaining a more reliable mangrove ecosystem map. The proposed approach relies on a straightforward yet effective workflow for mangrove ecosystem mapping, with a high rate of automation that can be easily implemented for frequent and precise mapping in other parts of the world. Overall, the proposed workflow can further improve the conservation and sustainable management of these valuable natural resources.

Nitrogen dioxide (NO2) is an important air pollutant with both environmental and epidemiological effects. The main aim of this study is to analyze spatial patterns and temporal trends in tropospheric NO2 concentrations globally using data from the satellite-based Ozone Monitoring Instrument (OMI). Additional aims are to compare the satellite data with ground-based observations, and to find the timing and magnitude of greatest breakpoints in tropospheric NO2 concentrations for the time period 2005–2018. The OMI NO2 concentrations showed strong relationships with the ground-based observations, and inter-annual patterns were especially well reproduced. Eastern USA, Western Europe, India, China and Japan were identified as hotspot areas with high concentrations of NO2. The global average trend indicated slightly increasing NO2 concentrations (0.004 × 1015 molecules cm−2 y−1) in 2005–2018. The contribution of different regions to this global trend showed substantial regional differences. Negative trends were observed for most of Eastern USA, Western Europe, Japan and for parts of China, whereas strong, positive trends were seen in India, parts of China and in the Middle East. The years 2005 and 2007 had the highest occurrence of negative breakpoints, but the trends thereafter in general reversed, and the highest tropospheric NO2 concentrations were observed for the years 2017–2018. This indicates that the anthropogenic contribution to air pollution is still a major issue and that further actions are necessary to reduce this contribution, having a substantial impact on human and environmental health.

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