Deep Transfer Learning of Satellite Imagery for Land Use and Land Cover Classification

Deep learning has been instrumental in solving difficult problems by automatically learning, from sample data, the rules (algorithms) that map an input to its respective output. Purpose: Perform land use landcover (LULC) classification using the training data of satellite imagery for Moscow region and compare the accuracy attained from different models. Methods: The accuracy attained for LULC classification using deep learning algorithm and satellite imagery data is dependent on both the model and the training dataset used. We have used state-of-the-art deep learning models and transfer learning, together with dataset appropriate for the models. Different methods were applied to fine tuning the models with different parameters and preparing the right dataset for training, including using data augmentation. Results: Four models of deep learning from Residual Network (ResNet) and Visual Geometry Group (VGG) namely: ResNet50, ResNet152, VGG16 and VGG19 has been used with transfer learning. Further training of the models is performed with training data collected from Sentinel-2 for the Moscow region and it is found that ResNet50 has given the highest accuracy for LULC classification for this region. Practical relevance: We have developed code that train the 4 models and make classification of the input image patches into one of the 10 classes (Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, and Sea&Lake)

A bi-directional strategy to detect land use function change using time-series Landsat imagery on Google Earth Engine:A case study of Huangshui River Basin in China

Constructed land, cropland, and ecological land are undergoing intense competition in many rapidly-developing regions. One of the major reasons to cause frequent land use (LU) conversions is the policy dynamics. The detection of such conversions is thus a prerequisite to understanding urban dynamics and how policies shape landscapes. This paper presents a bi-directional strategy to detect the LU change of the Huangshui River Basin of China from 1987 to 2018 using time-series Landsat imagery. We first initialized classification and optimization of remote sensing images using the Random Forest algorithm; We then detected bi-directional spatio-temporal changes based on the distribution probability of land-cover types. Our results reveal complicated dynamics underlying the net increase in urban and built-up land (UB) and the net decrease in cropland. In this area, due to the implementation of ecological compensation projects such as ecological migration and mine restoration, we found that on average 5.52 km2 of UB was converted into ecological land (forest, grassland and shrubland) every year, even though UB has expanded 3.6 times in the last 30 years with multiple conversions for cropland and ecological land. Meanwhile, 60% of lost cropland was converted to shrubland and grassland, and 40% was converted to UB. The accuracy of LU classification increases by 6.03% from 88.17%, and kappa coefficient increases by 2.41% from 85.16, compared to the existing initial results and uni-directional detection method. This study highlights the importance of the use of an effective remote sensing-based strategy for monitoring high-frequency LU changes in watershed areas with complicated human-nature interactions.</p

Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version

Repairing Landsat Satellite Imagery Using Deep Machine Learning Techniques

Satellite Imagery is one of the most widely used sources to analyze geographic features and environments in the world. The data gathered from satellites are used to quantify many vital problems facing our society, such as the impact of natural disasters, shore erosion, rising water levels, and urban growth rates. In this paper, we construct machine learning and deep learning algorithms for repairing anomalies in the Landsat satellite imagery data which arise for various reasons ranging from cloud obstruction to satellite malfunctions. The accuracy of GIS data is crucial to ensuring the models produced from such data are as close to reality as possible. Reducing the inherent bias caused by the obstruction or obfuscation of reflectance values is a simple but effective way to more closely represent the reality of our environment with satellite data. Using clean pixels from previously acquired satellite imagery, we were able to model the bias present in each scene at different times and apply algorithms to fix the inconsistencies. The machine learning model decreased the mean absolute error by an average of 80.1% compared to traditional repair algorithms such as mosaicking

Exploring open-source multispectral satellite remote sensing as a tool to map long-term evolution of salt marsh shorelines

From an ecological and socio-economic perspective, salt marshes are one of the most valuable natural assets on Earth. As external pressures are causing their extensive degradation and loss globally, the ability to monitor salt marshes on a long-term scale and identify drivers of change is essential for their conservation. Remote sensing has been demonstrated to be one of the most adept methods for this purpose and open-source multispectral satellite remote sensing missions have the potential to provide worldwide long-term time-series coverage that is non-cost-prohibitive. This study derives the long-term lateral evolution of four salt marsh patches in the Ria Formosa coastal lagoon (Portugal) using data from the Sentinel-2 and Landsat missions as well as from aerial photography surveys to quantitatively examine the accuracy and associated uncertainty in using open-source multispectral satellite remote sensing for this purpose. The results show that these open-source satellite archives can be a useful tool for tracking long-term salt marsh extent dynamics. During 1976-2020, there was a net loss of salt marsh in the study area, with erosion rates reaching an average of-3.3 m/yr opposite a tidal inlet. The main source of error in the satellite results was the dataset spatial resolution limits, but the specific salt marsh shoreline environment contributed to the relative magnitude of that error. The study notes the influence of eco-geomorphological dynamics on the mapping of sedimentary environments, so far not extensively discussed in scientific literature, highlighting the difference between mapping a morphological process and a sedimentary environment.info:eu-repo/semantics/publishedVersio

A Machine Learning Approach to Sentinel-3 Feature Extraction In The Context Of Harmful Algal Blooms

Harmful Algal Blooms (HAB) are typically described as blooms of phytoplankton species that can not only cause harm to the environment but also humans. Some species that form these blooms can release biotoxins, which accumulate in shellfish [1]. When humans consume contaminated shellfish, it can cause adverse health problems [2]–[4]. Due to the associated risk of contamination, shellfisheries are forced to close, sometimes for months, leading to significant economic losses. Although microscopes enable toxic species identification, and bioassays enable biotoxin identification and quantification, these methods are impractical for continuous monitoring since they require recurrent in situ data sampling, followed by laboratory analysis. Chlorophyll a is a pigment common to almost all marine phytoplankton groups. It has a spectral signature that enables it to be detectable by remote satellites that capture water-leaving radiance [5]. Remote sensing can be very useful since it allows us to take synoptic measurements of large sea areas [6]. Several machine learning algorithms have been researched to detect or forecast algal biomass or HAB presence [7]–[10]. However, the application of remotely sensed images to detect and forecast biotoxin concentration seems relatively unexplored. Given this problem, two datasets with Sentinel-3 imagery patches were created, from along the west coastal region of Portugal, which differ in size and the preprocessing applied. We assessed the application of Machine Learning (ML) models to extract informative features from the datasets. The models were evaluated quantitatively and qualitatively. The qualitative analysis demonstrated how the features extracted by the models seem to be consistent with features extracted for downstream tasks in the literature, suggesting the features retain helpful information. However, at this time, further work Is required to determine whether the feature can be helpful in the task of biotoxin concentration forecasting.Um Harmful Algal Bloom (HAB) é tipicamente descrito como sendo a proliferação de espécies de fitoplâncton que podem causar danos não só ao ambiente, mas também aos humanos. Algumas espécies que formam HABs podem libertar biotoxinas, que se acumulam nos moluscos [1]. Quando o ser humano consome moluscos contaminados, pode causar problemas de saúde adversos [2]–[4]. Devido ao risco associado de contaminação, as áreas de exploração de bivalves são forçadas a fechar, por vezes durante meses, levando a perdas económicas significantes. A clorofila a é um pigmento comum a quase todos os grupos de fitoplâncton marinho e tem uma assinatura espectral que lhe permite ser detectável por satélites remotos que captam a radiância que sai da água do mar [5]. A detecção remota pode ser muito útil, uma vez que nos permite fazer medições sinópticas de grandes áreas marítimas [6]. Foram pesquisados vários modelos de aprendizagem automática para detectar ou prever a presença de biomassa algal ou HAB [7]–[10]. No entanto, a utilização de imagens de detecção remota para detectar e prever a concentração de biotoxinas parece relativamente inexplorada. Dado este problema, foram criados dois conjuntos de dados com patches de imagens do satélite Sentinel-3 ao longo da região costeira ocidental de Portugal, que diferem em tamanho e no pré-processamento aplicado. Avaliámos diferentes modelos de aprendizagem automática para extrair características informativas dos conjuntos de dados. Os modelos foram avaliados quantitativa e qualitativamente. A análise qualitativa demonstrou como a informação extraída pelos modelos parecem ser consistentes com a extraída na literatura para informar outros modelos, sugerindo que as características retêm informação útil. Contudo, neste momento, é necessário trabalho futuro para determinar se a informação pode ser útil na tarefa de previsão da concentração de biotoxinas

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

SATELLITE REMOTE SENSING ALONG SANDY COASTLINES: SHORELINE MONITORING, BEACH SLOPES AND MULTI-DECADAL CLIMATE VARIABILITY

The ability to repeatedly observe and quantify the changing position of the shoreline is key to present-day coastal management and future coastal planning. Yet, long-term uninterrupted time-series of coastal change from in situ observations remain absent along much of the world’s coastline. However, Earth Observation satellites have been capturing regular images of the world’s coastlines over the past four decades. In this work, we demonstrate that this archived satellite imagery can capture sub-annual to multi-decadal shoreline variability over large spatial scales and is a valuable resource for coastal scientists and engineers studying open-coast/wave exposed sandy beaches around the world. A new methodology to automatically map the position of the shoreline (CoastSat) on Landsat and Sentinel-2 images is developed and shorelines extracted from 30+ years of imagery are compared to long-term field data at 5 diverse test sites in Europe, Australia, the USA, and New Zealand. The observed horizontal errors are found to be of the order of 10-15 m and generally the satellite-derived shorelines can resolve the observed shoreline variance at timescales of 6 months and longer. To expand the application of satellite-derived shorelines to larger spatial scales and regions with no in situ observational coverage, a remaining limitation is the lack of beach-face slope data to perform the tidal correction. To overcome this challenge, a novel approach is developed to estimate beach-face slopes remotely by combining satellite-derived shorelines and a global tide model. A detailed assessment of this new approach at 8 locations shows strong agreement (R2 = 0.93) with field measurements. The automated beach-face slope estimation technique is then applied at the continental scale to generate a dataset of beach-face slopes for Australia, along more than 13,200 km of sandy coast. Finally, the two methods are combined to generate a multi-decadal dataset of satellite-derived shorelines around the Pacific Rim and investigate the impact of El Niño Southern Oscillation (ENSO) on inter-annual shoreline change at the basin scale. Coherent patterns of beach erosion and accretion controlled by ENSO are identified, with the Eastern Pacific (USA, Mexico, Chile, Peru) experiencing erosion during major El Niño events, while the East Coast of Australia erodes during prolonged La Niña periods

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others