A Collection of Novel Algorithms for Wetland Classification with SAR and Optical Data

Wetlands are valuable natural resources that provide many benefits to the environment, and thus, mapping wetlands is crucially important. We have developed land cover and wetland classification algorithms that have general applicability to different geographical locations. We also want a high level of classification accuracy (i.e., more than 90%). Over that past 2 years, we have been developing an operational wetland classification approach aimed at a Newfoundland/Labrador province-wide wetland inventory. We have developed and published several algorithms to classify wetlands using multi-source data (i.e., polarimetric SAR and multi-spectral optical imagery), object-based image analysis, and advanced machine-learning tools. The algorithms have been tested and verified on many large pilot sites across the province and provided overall and class-based accuracies of about 90%. The developed methods have general applicability to other Canadian provinces (with field validation data) allowing the creation of a nation-wide wetland inventory system

Radarkaugseire rakendused metsaüleujutuste ja põllumajanduslike rohumaade jälgimiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Käesolev doktoritöö keskendub radarkaugseire rakenduste arendamisele kahes keerukas looduskeskkonnas: üleujutatud metsas ja põllumajanduslikel rohumaadel. Uurimistöö viidi läbi Tartu Observatooriumis, Tartu Ülikoolis, Ventspilsi Kõrgkoolis ja Aalto Ülikoolis. Töö esimene osa käsitleb X-laineala polarimeetrilise radarisignaali käitumist regulaarselt üleujutatavas metsas Soomaa näitel ning teine osa põllumajanduslike rohumaade seisundi ja polarimeetriliste ning interferomeetriliste tehisava-radari parameetrite vahelisi seoseid. 2012 kevadel Soomaa testalal TerraSAR-X andmetega läbi viidud eksperiment näitas, et topelt-peegeldusele tundlik HH-VV polarimeetriline kanal pakub tõesti kontrastsemat tagasihajumisepõhist üleujutatud metsa eristust üleujutamata metsast kui traditsiooniline HH polarimeetriline kanal. HH-VV kanali eelis HH kanali ees on seda suurem, mida madalam on mets ning raagus tingimustes lehtmetsas oli HH-VV kanali eelis HH kanali ees suurem kui okasmetsas. Lisaks on üleujutusele tundlik HH ja VV kanali polarimeetriline faasivahe, mida on soovitatud ka varasemates töödes kasutada täiendava andmeallikana üleujutuste kaardistamisel. Käesolevas doktoritöös mõõdeti polarimeetrilise X-laineala tehisava-radari HH/VV faasivahe suurenemine üleujutuste tõttu erineva kõrgusega okas- ja lehtmetsas. 2013 a vegetatsiooniperioodil korraldati Rannu test-alal välimõõtmistega toetatud eksperiment uurimaks X- ja C-laineala polarimeetrilise ning X-laineala interferomeetrilise tehisava-radari parameetrite undlikkust rohumaade tingimuste muutustele. Ilmnes, et ühepäevase vahega kogutud X-laineala tehisava-radari interferomeetriliste paaride koherentsus korreleerus rohu kõrgusega. Koherentsus oli seda madalam, mida kõrgem oli rohi - leitud seost on võimalik potentsiaalselt rakendada niitmise tuvastamiseks. TerraSAR-X ja RADARSAT-2 polarimeetriliste aegridade analüüsi tulemusel leiti kaks niitmisele tundlikku parameetrit: HH/VV polarimeetriline koherentsus ja polarimeetriline entroopia. Niitmise järel langes HH/VV polarimeetriline koherentsus järsult ning polarimeetriline entroopia tõusis järsult. Rohu tagasikasvamise faasis hakkas HH/VV polarimeetriline koherentsus aeglaselt kasvama ning entroopia aeglaselt kahanema. Täheldatud efekt oli tugevam TerraSARX X-laineala aegridadel kui RADARSAT-2 C-riba tehisava-radari mõõtmistel ning seda selgemini nähtav mida rohkem biomassi niitmise järgselt maha jäi. Leitud HH/VV polarimeetrilise koherentsuse ja polarimeetrilise entroopia käitumine vastas taimkatte osakestepilve radarikiirguse tagasihajumismudelile. Mudeli järgi põhjus- 60 tas eelnimetatud parameetrite iseloomulikku muutust rohukõrte kui dipoolide orientatsiooni ja korrastatuse muut niitmise tõttu, mis on kooskõlas meie välimõõtmiste andmetega.This thesis presents research about the application of radar remote sensing for monitoring of complex natural environments, such as flooded forests and agricultural grasslands. The study was carried out in Tartu Observatory, University of Tartu, Ventspils University College, and Aalto University. The research consists of two distinctive parts devoted to polarimetric analysis of images from a seasonal flooding of wetlands, and to polarimetric and interferometric analysis of a summer-long campaign covering eleven agricultural grasslands. TerraSAR-X data from 2012 were used to assess the use of the double-bounce scattering mechanism for improving the mapping of flooded forest areas. The study confirmed that the HH–VV polarimetric channel that is sensitive to double-bounce scattering provides increased separation between flooded and unflooded forest areas when compared to the conventional HH channel. The increase in separation increases with decreasing forest height, and it is more pronounced for deciduous forests due to the leaf-off conditions during the study. The phase difference information provided by the HH–VV channel may provide additional information for delineating flooded and unflooded forest areas. Time series of X-band (TanDEM-X and COSMO-SkyMed) and C-band (RADARSAT-2) data from 2013 were analyzed in respect to vegetation parameters collected during a field survey. The one-day repeat-pass X-band interferometric coherence was shown to be correlated to the grassland vegetation height. The coherence was also found to be potentially useful for detecting mowing events. The polarimetric analysis of TanDEM-X and RADARSAT-2 data identified two parameters sensitive to mowing events - the HH/VV polarimetric coherence magnitude and the H2α entropy. Mowing of vegetation consistently caused the coherence magnitude to decrease and the entropy to increase. The effect was more pronounced in case of X-band data. Additionally, the effect was stronger with more vegetation left on the ground after mowing. The effect was explained using a vegetation particle scattering model. The changes in polarimetric variables was shown to be caused by the change of orientation and the randomness of the vegetation

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Multisensor systems and flood risk management. Application to the Danube Delta using radar and hyperspectral imagery

International audienceAt the beginning of the 21st century, flood risk still represents a major world threat ( 60% of natural disasters are initiated by storms ) and the climate warming might even accentuate this phenomenon in the future. In Europe, despite all the policies in place and the measures taken during the past decades, large floods have occurred almost every year. The news regularly confirms this reality and the serious threat posed by flood risks in Europe. This paper presents an application to the Danube Delta exploiting radar imagery ENVISAT/ASAR and hyperspectral imagery CHRIS/PROBA for mapping flooded and floodable areas during the events of spring 2006. The uses of multisensor systems, such as radar and hyperspectral imagers, contribute to a better comprehension of floods in this wetland, their impacts, and risk management and sustainable development in the delta. In the section Risk management, this paper approaches the methodological aspects related to the characterization of the flood hazard whereas in the section Forecasting we will focus on the knowledge and modeling of the Land cover. The method of kernels, particularly adapted to the highlighting of the special-temporal variations - Support Vector Machine - and the methods based on the principle of the vague logic ( object-oriented classifications ) will be implemented so as to obtain the information plan of the spatial data.En ce début de 21e siècle, le risque d'inondation constitue encore le risque majeur au monde ( avec les tempêtes, les inondations représentent 60% des catastrophes naturelles ) et le réchauffement climatique pourrait encore renforcer ce phénomène à l'avenir. En Europe, malgré toutes les politiques et les mesures prises, au cours des dernières décennies, de grandes inondations ont lieu quasiment chaque année. Les actualités confirment régulièrement la réalité et la prégnance du risque d'inondation en Europe. Cet article présente une application concernant le risque d'inondation durant les événements du printemps 2006 dans le delta du Danube en exploitant des images radar ENVISAT/ASAR et l'imagerie hyperspectrale CHRIS/PROBA en matière d'analyse et de cartographie des zones inondées et de la classe de l'inondable. L'utilisation couplée des techniques spatiales ( radar et hyperspectrale ) pourrait contribuer à une meilleure compréhension des phénomènes liés aux inondations dans le Delta du Danube, ainsi qu'à la gestion de ce risque dans le delta et à son développement durable. Dans la partie Gestion du risque, ce travail aborde des aspects méthodologiques liés à la caractérisation de l'aléa de l'inondation tandis que dans la partie Prévision, la connaissance et la modélisation de l'Occupation du sol seront abordés. Des méthodes des noyaux ( kernels ), adaptées en particulier à la mise en évidence des variations spatio-temporelles - Suport Vector Machine - ainsi que des méthodes basées sur le principe de la logique floue ( classifications orientées objet ) sont mis en place afin d'obtenir le plan d'information des données spatiales

Semi-supervised Convolutional Neural Networks for Flood Mapping using Multi-modal Remote Sensing Data

When floods hit populated areas, quick detection of flooded areas is crucial for initial response by local government, residents, and volunteers. Space-borne polarimetric synthetic aperture radar (PolSAR) is an authoritative data sources for flood mapping since it can be acquired immediately after a disaster even at night time or cloudy weather. Conventionally, a lot of domain-specific heuristic knowledge has been applied for PolSAR flood mapping, but their performance still suffers from confusing pixels caused by irregular reflections of radar waves. Optical images are another data source that can be used to detect flooded areas due to their high spectral correlation with the open water surface. However, they are often affected by day, night, or severe weather conditions (i.e., cloud). This paper presents a convolution neural network (CNN) based multimodal approach utilizing the advantages of both PolSAR and optical images for flood mapping. First, reference training data is retrieved from optical images by manual annotation. Since clouds may appear in the optical image, only areas with a clear view of flooded or non-flooded are annotated. Then, a semisupervised polarimetric-features-aided CNN is utilized for flood mapping using PolSAR data. The proposed model not only can handle the issue of learning with incomplete ground truth but also can leverage a large portion of unlabelled pixels for learning. Moreover, our model takes the advantages of expert knowledge on scattering interpretation to incorporate polarimetric-features as the input. Experiments results are given for the flood event that occurred in Sendai, Japan, on 12th March 2011. The experiments show that our framework can map flooded area with high accuracy (F1 = 96:12) and outperform conventional flood mapping methods

Detection of temporarily flooded vegetation using time series of dual polarised C-band synthetic aperture radar data

The intense research of the last decades in the field of flood monitoring has shown that microwave sensors provide valuable information about the spatial and temporal flood extent. The new generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally high-resolution detection of the earth's surface and its environmental changes. This opens up new possibilities for accurate and rapid flood monitoring that can support operational applications. Due to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of new algorithms, which on the one hand enable precise and computationally efficient flood detection and on the other hand can process a large amounts of data. In order to capture the entire extent of the flood area, it is essential to detect temporary flooded vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to extract information from under the vegetation cover. Due to multiple backscattering of the SAR signal between the water surface and the vegetation, the flooded vegetation areas are mostly characterized by increased backscatter values. Using this information in combination with a continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based patterns for temporary flooded vegetation can be identified. This combination of information provides the foundation for the time series approach presented here. This work provides a comprehensive overview of the relevant sensor and environmental parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their benefits, limitations, methodological trends and potential research needs for this area are identified and assessed. The focus of the work lies in the development of a SAR and time series-based approach for the improved extraction of flooded areas by the supplementation of TFV and on the provision of a precise and rapid method for the detection of the entire flood extent. The approach developed in this thesis allows for the precise extraction of large-scale flood areas using dual-polarized C-band time series data and additional information such as topography and urban areas. The time series features include the characteristic variations (decrease and/or increase of backscatter values) on the flood date for the flood-related classes compared to the whole time series. These features are generated individually for each available polarization (VV, VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was performed by Z-transform for each image element, taking into account the backscatter values on the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image elements. The time series features constitute the foundation for the hierarchical threshold method for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time series data for the individual flood-related classes was analyzed and evaluated. The results showed that the dual-polarized time series features are particularly relevant for the derivation of TFV. However, this may differ depending on the vegetation type and other environmental conditions. The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods show the effectiveness of the method presented here in terms of classification accuracy. Theiv supplementary integration of temporary flooded vegetation areas and the use of additional information resulted in a significant improvement in the detection of the entire flood extent. It could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood extent in each of study areas. The transferability of the approach due to the application of a single time series feature regarding the derivation of open water areas could be confirmed for all study areas. Considering the seasonal component by using time series data, the seasonal variability of the backscatter signal for vegetation can be detected. This allows for an improved differentiation between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter signal can be assigned to changes in the environmental conditions, since on the one hand a time series of the same image element is considered and on the other hand the sensor parameters do not change due to the same acquisition geometry. Overall, the proposed time series approach allows for a considerable improvement in the derivation of the entire flood extent by supplementing the TOW areas with the TFV areas

Wetland Monitoring and Mapping Using Synthetic Aperture Radar

Wetlands are critical for ensuring healthy aquatic systems, preventing soil erosion, and securing groundwater reservoirs. Also, they provide habitat for many animal and plant species. Thus, the continuous monitoring and mapping of wetlands is necessary for observing effects of climate change and ensuring a healthy environment. Synthetic Aperture Radar (SAR) remote sensing satellites are active remote sensing instruments essential for monitoring wetlands, given the possibility to bypass the cloud-sensitive optical instruments and obtain satellite imagery day and night. Therefore, the purpose of this chapter is to provide an overview of the basic concepts of SAR remote sensing technology and its applications for wetland monitoring and mapping. Emphasis is given to SAR systems with full and compact polarimetric SAR capabilities. Brief discussions on the latest state-of-the-art wetland applications using SAR imagery are presented. Also, we summarize the current trends in wetland monitoring and mapping using SAR imagery. This chapter provides a good introduction to interested readers with limited background in SAR technology and its possible wetland applications