Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry

High spatial and temporal resolution satellite remote sensing estimates are the silver bullet for monitoring of coastal marine areas globally. From 2000, when the first commercial satellite platforms appeared, offering high spatial resolution data, the mapping of coastal habitats and the extraction of bathymetric information have been possible at local scales. Since then, several platforms have offered such data, although not at high temporal resolution, making the selection of suitable images challenging, especially in areas with high cloud coverage. PlanetScope CubeSats appear to cover this gap by providing their relevant imagery. The current study is the first that examines the suitability of them for the calculation of the Satellite-derived Bathymetry. The availability of daily data allows the selection of the most qualitatively suitable images within the desired timeframe. The application of an empirical method of spaceborne bathymetry estimation provides promising results, with depth errors that fit to the requirements of the international Hydrographic Organization at the Category Zone of Confidence for the inclusion of these data in navigation maps. While this is a pilot study in a small area, more studies in areas with diverse water types are required for solid conclusions on the requirements and limitations of such approaches in coastal bathymetry estimations

BATHYMETRIC EXTRACTION USING PLANETSCOPE IMAGERY (CASE STUDY: KEMUJAN ISLAND, CENTRAL JAVA)

Bathymetry refers to the depth of the seabed relative to the lowest water level. Depth information is essential for various studies of marine resource activities, for managing port facilities and facilities, supporting dredging operations, and predicting the flow of sediment from rivers into the sea. Bathymetric mapping using remote sensing offers a more flexible, efficient,and cost-effective method and covers a largearea. This study aims to determine the ability of Planet Scope imagery to estimate and map bathymetry and to as certain its accuracy using the Stumpf algorithm on the in-situ depth data. PlanetScope level 3B satellite imagery and tide-corrected survey dataare employed; satellite images are useful in high-precision bathymetry extraction.The bathymetric extraction method used the Stumpf algorithm. The research location was Kemujan Island, Karimunjawa Islands, Central Java. The selection of this region wasbased on its water characteristics, which have a reasonably high variation in depth. Based on the results of the data processing, it was found that the PlanetScope image data were able to estimate depths of up to 20 m. In the bathymetric results, the R2 accuracy value was 0.6952, the average RMSE value was 2.85 m,and the overall accuracy rate was 71.68%

Mind the gap in data poor Natura 2000 sites and how to tackle them using Earth Observation and scientific diving surveys

Charismatic species drives decisions for the conservation of marine areas in the view of the coverage of the Natura 2000 sites in the European Union and other forms of Marine Protected Areas in Europe. However, when used solely, critical seascapes and habitats are systematically ignored and practically it can take decades to fulfill baseline needs on habitats distributions, habitats conservation status and species distributions and biodiversity assessments. Luckily, in the last decade, the use of new technologies in conjunction with scientific diving and budget friendly hydroacoustic tools and applications, has allowed to fill the gap in knowledge in such situations and seascapes. The current work demonstrates the use of Earth Observation and Science Dive to fill the gap of knowledge in a newly established Natura 2000 area in Crete, Greece, East Mediterranean, paving the road for replicable approaches in similar situations

Avaliação do Uso Integrado de Imagens de Nanossatélites e Classificadores baseados em Aprendizado de Máquina para Estudos da Dinâmica Hidrológica na Região da Nhecolândia (Pantanal)

A região da Baixa Nhecolândia é uma das paisagens mais icônicas da Bacia do Pantanal. Sua morfologia única é composta por mais de 10.000 lagoas com águas salino-alcalinas e água doce que coexistem em uma área aproximada de 12.000 km². Essa região está sujeita a alagamentos sazonais que atuam no escoamento superficial, porém, pouco se conhece sobre sua dinâmica de inundação. Avanços recentes na área do geoprocessamento têm ajudado a ampliar nosso conhecimento sobre ambientes lacustres. Este trabalho teve como objetivo avaliar o desempenho de dois classificadores supervisionados baseados em aprendizado de máquina (Support Vector Machine e Random Forest), para a caracterização da dinâmica hidrológica da região da Nhecolândia. Os classificadores foram aplicados em imagens de nanossatélites (PlanetScope) por meio da plataforma de computação em nuvem Google Earth Engine. Os resultados evidenciaram o desempenho satisfatório e semelhante dos dois classificadores

Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

The recent PlanetScope constellation (130+ satellites currently in orbit) has shifted the high spatial resolution imaging into a new era by capturing the Earth’s landmass including inland waters on a daily basis. However, studies on the aquatic-oriented applications of PlanetScope imagery are very sparse, and extensive research is still required to unlock the potentials of this new source of data. As a first fully physics-based investigation, we aim to assess the feasibility of retrieving bathymetric and water quality information from the PlanetScope imagery. The analyses are performed based on Water Color Simulator (WASI) processor in the context of a multitemporal analysis. The WASI-based radiative transfer inversion is adapted to process the PlanetScope imagery dealing with the low spectral resolution and atmospheric artifacts. The bathymetry and total suspended matter (TSM) are mapped in the relatively complex environment of Venice lagoon during two benchmark events: The coronavirus disease 2019 (COVID-19) lockdown and an extreme flood occurred in November 2019. The retrievals of TSM imply a remarkable reduction of the turbidity during the lockdown, due to the COVID-19 pandemic and capture the high values of TSM during the flood condition. The results suggest that sizable atmospheric and sun-glint artifacts should be mitigated through the physics-based inversion using the surface reflectance products of PlanetScope imagery. The physics-based inversion demonstrated high potentials in retrieving both bathymetry and TSM using the PlanetScope imagery

PENILAIAN AKURASI ALGORITMA SATELLITE-DERIVED BATHYMETRY DATA CITRA PLANETSCOPE

Batimetri mengacu pada kedalaman dasar laut relatif terhadap permukaan air terendah. Pemetaan batimetri menggunakan penginderaan jauh menawarkan metode yang lebih fleksibel, efisien, dan hemat biaya serta mencakup area yang luas. Tujuan penelitian ini adalah membandingkan tingkat keakurasian antara algoritma Stumpf dan Lyzenga dalam estimasi batimetri menggunakan data citra satelit PlanetScope. Penelitian ini menggunakan citra satelit PlanetScope level 3B dan data survei terkoreksi pasang surut. Metode ekstraksi batimetri menggunakan algoritma Stumpf dan algoritma Lyzenga. Lokasi penelitian berada di Pulau Kemujan, Kepulauan Karimunjawa, Jawa Tengah. Berdasarkan hasil pengolahan data, kemampuan estimasi menggunakan algoritma Stumpf adalah hingga 17.97 m apabila menggunakan algoritma Lyzenga mampu mengestimasi hingga 33 m. Pada hasil uji statistika didapatkan hasil pada masing-masing algoritma yaitu Lyzenga sebesar 0,81 dan Stumpf 0,76. Perhitungan akurasi dengan Confusion Matrix menunjukkan nilai akurasi keseluruhan algoritma Lyzenga sebesar 83% dan Stumpf sebesar 76%. Dari hasil tersebut, estimasi kedalaman dengan algoritma Lyzenga memberikan hasil yang lebih akurat dibandingkan dengan algoritma Stumpf

A GEOGRAPHIC SEGMENTATION APPROACH FOR SATELLITE-DERIVED BATHYMETRY

Safety of navigation depends on our knowledge of seabed and its features, and, as such, any improvements in deriving bathymetry for nautical chart updating are of major importance. Satellite-Derived Bathymetry (SDB) is an alternative to traditional surveys using ship and airborne sensors, particularly for mapping remote and shallow areas, due to its reduced cost and the absence of navigational risks in very shallow and unsurveyed areas. However, the accuracy of SDB can be judged as relatively low for nautical charting purposes and, therefore, is mostly used for reconnaissance or/and for filling gaps in remote or very shallow areas. One of the reasons may be that the conventional approaches assume that bottom type and water clarity are constant and negligible within the entire image, and consequently, a single (global) and linear model is performed to retrieve bathymetric information. To address the spatial heterogeneity within a scene and with the aim to increase the accuracy and coverage of estimated depths, this work investigates the segmentation of the scene, both horizontally and vertically, into smaller spatial units, and accounts for water column parameters in the SDB equation. In practice, the main idea of the segmentation is to divide the image scene into small spatial units and then calibrate the model within each segment. The individual models use the same algorithm but varying model parameters from place to place. Also, to account for water column and sea bottom variations, an extended Dierssen model is applied. The performance of the methods is evaluated in two study areas in the Dry Tortugas, Florida, and St. Thomas East and Reserve, U.S. Virgin Islands. Overall, the results indicate that the accuracy of bathymetry may be improved when the area is divided into smaller spatial units, particularly with a vertical (by depth) segmentation of the scene. In detail, compared to the conventional global and linear approach, the accuracy in both study areas is increased by over 40% with segmenting the area and calibrating the water parameters within each spatial unit. Furthermore, as it is demonstrated with the two study areas, besides the improvements in the depth accuracy, the SDB coverage is increased with the extraction of bathymetry beyond the depth considered as the effective optical depth of the conventional global and linear approach. However, further work is recommended to investigate and verify the accuracy improvement demonstrated by the vertical segmentation and particularly that of the smallest utilized depth range of 1m. since questions are raised about the discontinuity of the models and their quantized depth predictions, and more precisely whether this is due to overfitting rather than an actual improvement in accuracy. Lastly, the results demonstrate that considering the water column and sea bottom heterogeneity for solving the global SDB model increases the accuracy of bathymetry estimates. Nonetheless, when the area is segmented into small spatial units, adding the water column contribution as a parameter to the equation did not produce a significant contribution

Using a new generation of remote sensing to monitor Peru’s mountain glaciers

Remote sensing technologies are integral to monitoring mountain glaciers in a warming world. Tropical glaciers, of which around 70% are located in Peru, are particularly at risk as a result of climate warming. Satellite missions and field-based platforms have transformed understanding of the processes driving mountain glacier dynamics and the associated emergence of hazards (e.g. avalanches, floods, landslides), yet are seldom specialised to overcome the unique challenges of acquiring data in mountainous environments. A ‘new generation’ of remote sensing, marked by open access to powerful cloud computing and large datasets, high resolution satellite missions, and low-cost science-grade field sensors, looks to revolutionise the way we monitor the mountain cryosphere. In this thesis, three novel remote sensing techniques and their applicability towards monitoring the glaciers of the Peruvian Cordillera Vilcanota are examined. Using novel processing chains and image archives generated by the ASTER satellite, the first mass balance estimate of the Cordillera Vilcanota is calculated (-0.48 ± 0.07 m w.e. yr-1) and ELA change of up to 32.8 m per decade in the neighbouring Cordillera Vilcabamba is quantified. The performance of new satellite altimetry missions, Sentinel-3 and ICESat-2, are assessed, with the tracking mode of Sentinel-3 being a key limitation of the potential for its use over mountain environments. Although currently limited in its ability to extract widespread mass balance measurements over mountain glaciers, other applications for ICESat-2 in long-term monitoring of mountain glaciers include quantifying surface elevation change, identifying large accumulation events, and monitoring lake bathymetry. Finally, a novel low-cost method of performing timelapse photogrammetry using Raspberry Pi camera sensors is created and compared to 3D models generated by a UAV. Mean difference between the Raspberry Pi and UAV sensors is 0.31 ± 0.74 m, giving promise to the use of these sensors for long-term monitoring of recession and short-term warning of hazards at glacier calving fronts. Together, this ‘new generation’ of remote sensing looks to provide new glaciological insights and opportunities for regular monitoring of data-scarce mountainous regions. The techniques discussed in this thesis could benefit communities and societal programmes in rapidly deglaciating environments, including across the Cordillera Vilcanota