The third generation of pan-canadian wetland map at 10 m resolution using multisource earth observation data on cloud computing platform

Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some overestimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy (OA) and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average OA obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an OA increase of 2% or greater and individual ecozone OA results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area overestimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multisource data for large-scale wetland mapping. In addition, the development of a confidence map helps visualize where current results are most and least reliable given the amount of wetland test and train data and the extent of recent landscape disturbance (e.g., fire). The resulting OAs and wetland areal extent reveal the importance of multisource data and adequate test and train data for wetland classification at a countrywide scale

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

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

Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients

Use of Earth Observation data and Google Earth Engine monitoring and early warning of floods and droughts

A total of 24 participants were represented from 8 institutions (WARMA, ZMD, Ndola City Council, IAPRI, Mulungushi University, ZRCS, NRSC and ZAMSTATS). Technical experts introduced with a presentation on the potential application of flood and drought monitoring and early warning using geospatial data, tools and models including earth engine (EE). The various example presented to participants to get an understanding of the EE capabilities and how the training provides an opportunity to utilize the remote sensing products in a timely decision making process. Later the experts delivered a lecture on the basics of remote sensing and GIS to help understand the characteristics of remote sensing sensors and their capabilities in mapping and monitoring environmental issues

Rapid land cover classification using a 36-year time series of multi-source remote sensing data

Long-time series land cover classification information is the basis for scientific research on urban sprawl, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the Random Forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of sample points without land class change determined by counting the sample points of each band of landsat time series from 1986 to 2022 was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of TM and ETM+ sensor data from 2013 to 2022; (iii) the addition of a mining land cover type increase the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and -forest area. Among the land classifications by multi-source remote sensing, the combined variables spectral band + index + topography + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and using sensors under complex terrain conditions. The use of GEE cloud computing platform enabled rapid analysis of remotely sensed data to produce land cover maps with high-accuracy and a long time series

Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

The growing world is more expensive to estimate land use, road length, and forest cover using a plant-scaled ground monitoring system. Satellite imaging contains a significant amount of detailed uncertain information. Combining this with machine learning aids in the organization of these data and the estimation of each variable separately. The resources necessary to deploy Machine learning technologies for Remote sensing images, on the other hand, restrict their reach ability and application. Based on satellite observations which are notably underutilised in impoverished nations, while practical competence to implement SIML might be restricted. Encoded forms of images are shared across tasks, and they will be calculated and sent to an infinite number of researchers who can achieve top-tier SIML performance by training a regression analysis onto the actual data. By separating the duties, the proposed SIML solution, MOSAIKS, shapes SIML approachable and global. A Featurization stage turns remote sensing data into concise vector representations, and a regression step makes it possible to learn the correlations which are specific to its particular task which link the obtained characteristics to the set of uncertain data

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

© 2018 The Author(s) Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer's accuracy of 98.8% (errors of omissions = 1.2%), and user's accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer's accuracy of 80% (errors of omissions = 20%), and user's accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA's Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282