Backscatter analysis of C-band radar signals using Sentinel-1 multitemporal data (test site near lake Baikal)

The results of the analysis of multitemporal data of the Sentinel-1 radar for the test site near Lake Baikal are presented. The analysis of the seasonal dependences of backscattering from the soil is carried out. The connection between the signal level and the processes of freezing and thawing and temperature values has been established.

Indices Extraction from Multitemporal Remote Sensing Data for Mapping Urban Built-Up

Remote sensing data analysis in the cloudy area is still a challenging process. Fortunately, remote sensing technology is fast growing. As a result, multitemporal data could be used to overcome the problem of the cloudy area. Using multitemporal data is a common approach to address the cloud problem. However, most methods only use two data, one as the main data and the other as complementary of the cloudy area. In this paper, a method to harness multitemporal remote sensing data for automatically extracting some indices is proposed. In this method, the process of extracting the indices is done without having to mask the cloud. Those indices could be further used for many applications such as the classification of urban built-up. Landsat-8 data that is acquired during 2019 are stacked, therefore each pixel at the same position creates a list. From each list, indices are extracted. In this study, NDVI, NDBI, and NDWI are used to mapping built-up areas. Furthermore, extracted indices are divided into four categories by their value (maximum, quantile 75, median, and mean). Those indices are then combined into a simple formula to mapping built-up to see which produces better accuracy. The Pleiades as high-resolution remote sensing data is used to assist supervised classification for assessment. In this study, the combination of mean NDBI, maximum NDVI, and mean NDWI result highest Kappa coefficient of 0.771.

Multitemporal Landsat Imagery with Optimum Band Ratio Techniques for Deciduous Forest Classification

The objective of the study is to evaluate optimum band ratio combinations data set derived from monthly Landsat 8 imageries for forest type classification around the Sirikit dam reservoir using supervised classification with Maximum Likelihood Classifier (MLC). In this study, imageries data acquired from January 2014 to November 2017 were used to create the monthly band ratio data set of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Moisture Indices (NDMI), and Normalized Burn Ratios (NBR) and used to create the monthly multispectral (MS) data set represented as a case of without applying band ratio techniques. In classifying deciduous forest type, four data sets were used to classify two classes of deciduous forests, namely mixed deciduous forest and dry deciduous dipterocarp forest. After the accuracy assessment, the result showed that the overall accuracy and kappa coefficient of all data sets were between 78.33% – 86.21% and between 44.32% – 62.83%, respectively. Herein, the monthly NDVI multitemporal data set provided the highest overall accuracy and kappa coefficient which were better than the monthly MS multitemporal data set about 4% and 8%, respectively. In conclusion, applying monthly multitemporal data of Landsat 8 with band ratio technique, especially NDVI, can increase the accuracy of deciduous forest type classification.

Assessment of static and dynamic geodiversity in the Sesia Val Grande UNESCO Global Geopark: constraints for a sustainable use of related abiotic ecosystem services

<p>Geodiversity includes geological, geomorphological, hydrological and soil elements and processes. By analysing geodiversity we can offer static and dynamic views of abiotic landscapes on the Earth. The current state of geodiversity includes both relict, long-term features recalling the past of our planet earth and active landforms and processes whose monitoring is a key for interpreting relationships between geosphere, biosphere and human activities. If the long term geodiversity mainly represents distribution of litho-structural “static” constrains to environmental changes, recent and active environmental features may act as dynamic “proxies” for interpreting climate change.<br>Aim of this work is to analyse relevant examples of both static and dynamic geodiversity within the territory of the Sesia Val Grande UNESCO Global Geopark (Western Alps, Italy), in order to assess their role as georesources and to highlight possible sustainable use of related abiotic ecosystem services, including geoheritage. Geodiversity assessment has been performed by means of creation of geothematic maps and related factors analysed for better mountain environment understanding and management. <br>Starting with static geodiversity we collected, analysed and interpreted lithological and structural data in order to obtain information on distribution of georesources in the study area and to create a geothematic map on landscape resistance to erosion.<br>Thereafter we focused on two aspects related to dynamic geodiversity and their relationships with dramatic changes of the alpine landscape: glacial evolution and fluvial processes. On one hand, valley scale geomorphological evolution has been reconstructed by means of multitemporal data (e.g.: glacial landforms maps, glacier inventories) on evidences in the Sesia Valley. Obtained information crossed with national landslide inventory allowed to identify areas of strong glacial influence on slope stability (deep-seated gravitational slope deformation and landslides due to slope debutressing). Moreover, recent glacier withdrawal results in new glacier lakes increasing the hydrogeodiversity of the area and representing important potential georesources to be used. Finally, recent alluvial event (October 2020) has been considered for its high impact in reshaping fluvial environment and effects on both infrastructures and popular geosites along the Sesia river.<br>Results of this work are useful for the establishment of a proper Driver-Pressure-State-Impact-Response (DPSIR) framework related to environmental issues due to global change in order to support educational activities and sustainable development of alpine “tourism hubs” included in the Sesia Val Grande UNESCO Global Geopark by the “ArcticHubs” H2020-EU.3.5.1 project.</p>

InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia

A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. The approach assumes the availability of a map of persistent scatterers (PSs) inside the urban settlement and is based on the analysis of the temporal trend of the InSAR coherence and the spatial average of the exponential of the InSAR phase in each PS. Both interferometric products are expected to have high and stable values in the PSs; therefore, anomalous decreases may indicate that floodwater is present in an urban area. The stack of Sentinel-1 data has been divided into two subsets. The first one has been used as a calibration set to identify the PSs and determine, for each PS, reference values of the coherence and the spatial average of the exponential of the interferometric phase under standard non-flooded conditions. The other subset has been used for validation purposes. Flood maps produced by UNOSAT, analyzing very-high-resolution optical images of the floods that occurred in Beletweyne in April–May 2018, October–November 2019, and April–May 2020, have been used as reference data. In particular, the map of the April–May 2018 flood has been used for training purposes together with the subset of Sentinel-1 calibration data, whilst the other two maps have been used to validate the products generated by applying the proposed method. The main product is a binary map of flooded PSs that complements the floodwater map of rural/suburban areas produced by applying a well-consolidated algorithm based on intensity data. In addition, a flood severity map that labels the different districts of Beletweyne, as not, partially, or totally flooded has been generated to consolidate the validation. The results have confirmed the effectiveness of the proposed method.

Oil Spill Detection using Sentinel-1 Multitemporal Data in Offshore Karawang

Oil spill is a serious problem that could lead to economic and ecological losses, both in the short and long term. On July 12, 2019, there occurred an oil leakage around YYA-1 oil platform of Pertamina Hulu Energi Offshore North West Java (PHE ONWJ), located off the northern coast of Karawang, Java Sea. This incident has caused the death of fishes and marine animals, damage to coral reefs, mangroves, and seagrass beds, and several health problems of coastal communities. Therefore, it is necessary to map and monitor oil spills, so that actions can be taken to prevent the spread of oil spills. This study aims to map the distribution of oil spills in Karawang sea using multitemporal Sentinel-1 data from July to September 2019. The detection is carried out using the adaptive thresholding algorithm combined with manual interpretation. The result shows that the oil spills spread around Karawang sea from YYA-1 platform to Sedari Village and there are oil spills spreading from the Central Plant F/S platform. The oil spills tend to shift westward from July to September 2019. This shifting is supposed to be influenced by current and wave factors that were dominant moving westward at that time. Based on data processing, it was found that the oil spill area from July to September was respectively 24.79 km2, 20.05 km2, and 27.12 km2.

POLARIMETRIC SAR DATA FROM SENTINEL-1A APPLIED TO EARLY CROP CLASSIFICATION

This paper aims to map crops in two Brazilian municipalities, Luís Eduardo Magalhães (LEM) and Campo Verde, using dual-polarimetric Sentinel-1A images. The specific objectives were: (1) to evaluate the accuracy gain in the crop classification using Sentinel-1A multitemporal data backscatter coefficients and ratio (σ0VH, σ0VV and, σ0VH/σ0VV, denominate BS group) in comparison to the addition of polarimetric attributes (σ0VH, σ0VV, σ0VH/σ0VV, H, and α, denominate BP group) and; (2) to assess the accuracy gain in the earliest crop classification, creating new scenarios with the addition of the new SAR data together with the previous images for each date and group (BS and BP) during the crop development. For BS and BP groups, 13 e 10 scenarios were analyzed in LEM and Campo Verde, respectively. For the classification process, we used the Random Forest (RF) algorithm. In the LEM site, the best results for BS and BP groups were equivalent (overall accuracy: ∼82%), while for the Campo Verde site, the classification accuracy for the BP group (overall accuracy: ∼80%) was 2% higher than the BS group. The addition of new images during the crop development period increased the earliest crop classification overall accuracy, stabilizing from mid-February in LEM and mid-December in Campo Verde, after 10 and 8 images, respectively. After these periods, the gain in classification accuracy was small with the addition of new images. In general, our results suggest the backscattering coefficients and polarimetric attributes extracted from the Sentinel-1A imagery exhibited a great performance to discriminate croplands.