THE USE OF C-BAND SYNTHETIC APERTURE RADAR SATELLITE DATA FOR RICE PLANT GROWTH PHASE IDENTIFICATION

Identification of the rice plant growth phase is an important step in estimating the harvest season and predicting rice production. It is undertaken to support the provision of information on national food availability. Indonesia’s high cloud coverage throughout the year means it is not possible to make optimal use of optical remote sensing satellite systems. However, the Synthetic Aperture Radar (SAR) remote sensing satellite system is a promising alternative technology for identifying the rice plant growth phase since it is not influenced by cloud cover and the weather. This study uses multi-temporal C-Band SAR satellite data for the period May–September 2016. VH and VV polarisation were observed to identify the rice plant growth phase of the Ciherang variety, which is commonly planted by farmers in West Java. Development of the rice plant growth phase model was optimized by obtaining samples spatially from a rice paddy block in PT Sang Hyang Seri, Subang, in order to acquire representative radar backscatter values from the SAR data on the age of certain rice plants. The Normalised Difference Polarisation Index (NDPI) and texture features, namely entropy, homogeneity and the Grey-Level Co-occurrence Matrix (GLCM) mean, were included as the samples. The results show that the radar backscatter value (σ0) of VH polarisation without the texture feature, with the entropy texture feature and GLCM mean texture feature respectively exhibit similar trends and demonstrate potential for use in identifying and monitoring the rice plant growth phase. The rice plant growth phase model without texture feature on VH polarisation is revealed as the most suitable model since it has the smallest average error

DINAMIKA GENANGAN PESISIR JAKARTA BERDASARKAN DATA MULTI-TEMPORAL SATELIT MENGGUNAKAN INDEKS AIR DAN POLARISASI RADAR

Combining baseline data of remote sensing systems active and passive has many advantages in monitoring coastal inundation dynamically. It has advanced the surface water information gaps in coastal areas, especially areas covered by clouds and shadows. The main objective of this study was to assess the dynamics of coastal inundation in Jakarta based on multi-temporal data optics of Landsat 8 and Synthetic Aperture Radar (SAR) Sentinel 1A. The method of this research used two water index algorithms. They are Modified Normalized Difference Water Index (MNDWI) and Dynamic Surface Water Extent (DSWE) based on spectral reflectance values and empirical formulas. The other method is using the coefficient backscattering of water from a single polarization of Vertical Verticals (VV) and Vertical Horizontal (VH). The study results show that the use of both satellite data baseline of 8, 9, 15, and 16 days is quite effective, applying inundation dynamics for 8-49 days. Based on the threshold value of MNDWI > 0.123 and the backscattering coefficient of -19dB are quite efficient to extract satellite data information. The empirical algorithms result in the feature of inundation, especially along the coastal dikes, reservoirs, mangrove ecosystems, and built-up lands. Satellite monitoring results show that the peak of inundation occurred on 30 May 2016 and was still visible on 15 June 2016. The combination of remote sensing methods is quite effective and efficient for monitoring inundation dynamically.Kombinasi baseline data pengindraan jauh sistem aktif dan pasif memiliki banyak keuntungan dalam pemantauan dinamika genangan pesisir. Kedua jenis sensor satelit mengatasi kesenjangan informasi genangan, terutama pada area yang ditutupi awan/bayangan. Tujuan utama penelitian ini adalah untuk mengkaji dinamika genangan di wilayah pesisir Jakarta berdasarkan data multi-temporal sensor optik dari Landsat 8 dan Synthetic Aperture Radar (SAR) Sentinel 1A. Metode penelitian ini menggunakan dua algoritma indeks air. Algoritma tersebut yaitu Modified Normalized Difference Water Index (MNDWI) dan Dynamic Surface Water Extent (DSWE) berdasarkan nilai spektral reflektansi dan formula empirik. Metode lainnya adalah menggunakan nilai rata-rata koefisien backscatter air dari analisis polarisasi tunggal Vertikal Vertikal (VV) dan Vertikal Horisontal (VH). Hasil studi menunjukkan bahwa penggunaan kedua tipe data satelit dengan baseline data 8, 9, 15 dan 16 hari cukup efektif memantau dinamika genangan selama 8-49 hari, termasuk area yang tertutup awan dan bayangan. Berdasarkan nilai threshold dari MNDWI >0,123 dan koefisien backscattering air -19dB cukup efisien digunakan untuk mengesktrak informasi data satelit. Algoritma empiris tersebut menghasilkan kenampakan genangan, terutama di sepanjang tanggul pantai, waduk, ekosistem mangrove dan lahan terbangun. Hasil pemantauan satelit menunjukkan bahwa puncak genangan terjadi pada 30 Mei 2016 dan masih terlihat pada 15 Juni 2016. Kombinasi metode pengindraan jauh tersebut cukup efektif dan efisien untuk memantau genangan secara dinamis

Sentinel-1 Satellite Data as a Tool for Monitoring Inundation Areas near Urban Areas in the Mexican Tropical Wet

This work shows advances in the field of water body monitoring with radar images. Particularly, a monitoring procedure is developed to define the extension and frequency of inundation for continental waters of the Grijalva-Usumacinta basin, in the state of Tabasco, Mexico. This is a region located in the Mexican tropical wet and under its meteorological conditions, radar technology can be used to characterize monthly inundation frequency. The identification of water bodies were obtained by processing images at a monthly intervals captured by Sentinel-1A during 2015 having kappa indices and overall accuracy higher than 0.9. The chapter describes the seasonal variability of these water bodies, and at the same time, the relationship with human settlements located in their neighborhood. To do this, a proximity analysis was carried out to emphasize the importance of spatial-temporal studies of superficial water bodies, linked to an urban and a rural area. This information is useful to investigate changes in the ecosystem, as well as risks to human settlements, and as a contribution for a comprehensive management of hydric resources

CONSIDERATIONS ON THE USE OF SENTINEL-1 DATA IN FLOOD MAPPING IN URBAN AREAS: ANKARA (TURKEY) 2018 FLOODS

Flood events frequently occur due to -most probably- climate change on our planet in the recent years. Rapid urbanization also causes imperfections in city planning, such as insufficient considerations of the environmental factors and the lack of proper infrastructure development. Mapping of inundation level following a flood event is thus important in evaluation of flood models and flood hazard and risk analyzes. This task can be harder in urban areas, where the effect of the disaster can be more severe and even cause loss of lives.With the increased temporal and spatial availability of SAR (Synthetic Aperture Radar) data, several flood detection applications appear in the literature although their use in urban areas so far relatively limited. In this study, one flood event occurred in Ankara, Turkey, in May 2018 has been mapped using Sentinel-1 SAR data. The preprocessing of Sentinel-1 data and the mapping procedure have been described in detail and the results have been evaluated and discussed accordingly. The results of this study show that SAR sensors provide fast and accurate data during the flooding using appropriate methods, and due to the nature of the flood events, i.e. heavy cloud coverage, it is currently irreplaceable by optical remote sensing techniques.</p

Automating global landslide detection with heterogeneous ensemble deep-learning classification

With changing climatic conditions, we are already seeing an increase in extreme weather events and their secondary consequences, including landslides. Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. However, these both rely on data from previous landslide events, which is often scarce. Many deep learning (DL) models have recently been applied for landside mapping using medium- to high-resolution satellite images as input. However, they often suffer from sensitivity problems, overfitting, and low mapping accuracy. This study addresses some of these limitations by using a diverse global landslide dataset, using different segmentation models, such as Unet, Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an ensemble model. The ensemble model achieved the highest F1-score (0.69) when combining both Sentinel-1 and Sentinel-2 bands, with the highest average improvement of 6.87 % when the ensemble size was 20. On the other hand, Sentinel-2 bands only performed very well, with an F1 score of 0.61 when the ensemble size is 20 with an improvement of 14.59 % when the ensemble size is 20. This result shows considerable potential in building a robust and reliable monitoring system based on changes in vegetation index dNDVI only.Comment: Author 1 and Author 2 contributed equally to this wor

Relationship of local incidence angle with satellite radar backscatter for different surface conditions

This paper examines the relationship of C-band radar backscatter from the Advanced Synthetic Aperture\ud Radar on board the ENVISAT satellite with the local angle of incidence, whose influence on the received\ud signal is significant, particularly in the modes of sensor operation that use the full swath of the orbit\ud track. Linear regression is carried out for each pixel throughout a large time series of radar data over\ud the whole of the state of Queensland, Australia, and at Great Salt Lake, Utah, USA. In the first case, the\ud resultant coefficients are analysed for correlation against various parameters, with regolith showing the\ud highest correlation. Class separability analysis shows the potential to use the resultant coefficients as a\ud supplement to absolute threshold values in order to distinguish between classes of vegetation and/or\ud geology, where cloud cover may preclude the use of optical data. It is observed that the separability\ud between water and land is greatly higher using the slope coefficient B than using backscatter 0 , which\ud may be of great benefit in the remote sensing of water where cloud cover is present (from which radar is\ud largely independent). This is especially the case when considering the observed overlapping of backscat-\ud ter values from water, with values from aeolian sand and lacustrine and alluvial sediments, rendering\ud the use of backscatter alone problematic. In order to test the potential use of B to map water extents,\ud the study over the Great Salt Lake compares the classification accuracy of B against that of 0 . It is found\ud that the 0 classification misrepresents desert, salt flat and dry lake basin areas, where the B classifica-\ud tion differentiates these regions accurately. The resultant classification achieves a kappa statistic around\ud 0.9, which shows very high conformance. An accurate and novel method to classify water is therefore\ud demonstrated, which awaits the launch of anticipated improved synthetic aperture radar instruments\ud on satellite missions in the coming few years

A multiscale remote sensing assessment of subtropical indigenous forests along the wild coast, South Africa

The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level

Flood Extent and Volume Estimation using Multi-Temporal Synthetic Aperture Radar.

Ph. D. Thesis.Satellite imagery has the potential to monitor flooding across wide geographical regions. Recent launches have improved the spatial and temporal resolution of available data, with the European Space Agency (ESA) Copernicus programme providing global imagery at no end-user cost. Synthetic Aperture Radar (SAR) is of particular interest due to its ability to map flooding independent of weather conditions. Satellite-derived flood observations have real-world application in flood risk management and validation of hydrodynamic models. This thesis presents a workflow for estimating flood extent, depth and volume utilising ESA Sentinel-1 SAR imagery. Flood extents are extracted using a combination of change detection, variable histogram thresholding and object-based region growing. An innovative technique has been developed for estimating flood shoreline heights by combining the inundation extents with high-resolution terrain data. A grid-based framework is used to derive the water surface from the shoreline heights, from which water depth and volume are calculated. The methodology is applied to numerous catchments across the north of England that suffered from severe flooding throughout the winter of 2015-16. Extensive flooding has been identified throughout the study region, with peak inundation occurring on 29th December 2015. On this date, over 100 km2 of flooding is identified in the Ouse catchment, equating to a water volume of 0.18 km3. The SAR flood extents are validated against satellite optical imagery, achieving a Total Accuracy of 91% and a Critical Success Index of 77%. The derived water surfaces have an average error of 3 cm and an RMSE of 98 cm compared to river stage measurements. The methods developed are robust and globally applicable, shown with an additional study along the Mackenzie River in Australia. The presented methodology, alongside the increased temporal resolution provided by Sentinel-1, highlights the potential for accurate, reliable mapping of flood dynamics using satellite imagery.NERC, (DREAM) CD