Study of time-lapse processing for dynamic hydrologic conditions

The usefulness of dynamic display techniques in exploiting the repetitive nature of ERTS imagery was investigated. A specially designed Electronic Satellite Image Analysis Console (ESIAC) was developed and employed to process data for seven ERTS principal investigators studying dynamic hydrological conditions for diverse applications. These applications include measurement of snowfield extent and sediment plumes from estuary discharge, Playa Lake inventory, and monitoring of phreatophyte and other vegetation changes. The ESIAC provides facilities for storing registered image sequences in a magnetic video disc memory for subsequent recall, enhancement, and animated display in monochrome or color. The most unique feature of the system is the capability to time lapse the imagery and analytic displays of the imagery. Data products included quantitative measurements of distances and areas, binary thematic maps based on monospectral or multispectral decisions, radiance profiles, and movie loops. Applications of animation for uses other than creating time-lapse sequences are identified. Input to the ESIAC can be either digital or via photographic transparencies

New Remote Sensing Methods for Detecting and Quantifying Forest Disturbance and Regeneration in the Eastern United States

Forest disturbances, such as wildfires, the southern pine beetle, and the hemlock woolly adelgid, affect millions of hectares of forest in North America with significant implications for forest health and management. This dissertation presents new methods to quantify and monitor disturbance through time in the forests of the eastern United States using remotely sensed imagery from the Landsat family of satellites, detect clouds and cloud-shadow in imagery, generate composite images from the clear-sky regions of multiple images acquired at different times, delineate the extents of disturbance events, identify the years in which they occur, and label those events with an agent and severity. These methods operate at a 30x30 m spatial resolution and a yearly temporal resolution. Overall accuracy for cloud and cloud-shadow detection is 98.7% and is significantly better than a leading method. Overall accuracy for designating a specific space and time as disturbed, stable, or regenerating is 85%, and accuracy for labeling disturbance events with a causal agent ranges from 42% to 90%, depending on agent, with overall accuracy, excluding samples marked as `uncertain\u27, of 81%. Due to the high spatial resolution of the imagery and resulting output, these methods are valuable for managers interested in monitoring specific forested areas. Additionally, these methods enable the discovery and quantification of forest dynamics at larger spatial scales in a way other datasets cannot. Applying these methods over the entire extent of the eastern United States highlands reveals significant differences in disturbance frequency by ecoregion, from less than 1% of forested area per year in the Central Appalachians, to over 5% in the Piedmont. Yearly variations from these means are substantial, with disturbance frequency being twice as high as the mean in some years. Additionally, these analyses reveal that some disturbance agents, such as the southern pine beetle, exhibit periodic dynamics. Finally, although these methods are applied here to the problem of forest disturbance in the eastern United States, the core innovations are easily extended to other locations or even to other applications of landscape change, such as vegetation succession, shifting coastlines, or urbanization

Exploring Aerosols near Clouds with High-Spatial-Resolution Aircraft Remote Sensing During SEAC4RS

Since aerosols are important to our climate system, we seek to observe the variability of aerosol properties within cloud systems. When applied to the satelliteborne Moderateresolution Imaging Spectroradiometer (MODIS), the Dark Target retrieval algorithm provides global aerosol optical depth (AOD; at 0.55 m) in cloudfree scenes. Since MODIS' resolution (500m pixels, 3 or 10km product) is too coarse for studying nearcloud aerosol, we ported the Dark Target algorithm to the highresolution (~50m pixels) enhancedMODIS Airborne Simulator (eMAS), which flew on the highaltitude ER2 during the Studies of Emissions, Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys Airborne Science Campaign over the United States in 2013. We find that even with aggressive cloud screening, the ~0.5km eMAS retrievals show enhanced AOD, especially within 6 km of a detected cloud. To determine the cause of the enhanced AOD, we analyze additional eMAS products (cloud retrievals and degradedresolution AOD), coregistered Cloud Physics Lidar profiles, MODIS aerosol retrievals, and groundbased Aerosol Robotic Network observations. We also define spatial metrics to indicate local cloud distributions near each retrieval and then separate into nearcloud and farfromcloud environments. The comparisons show that low cloud masking is robust, and unscreened thin cirrus would have only a small impact on retrieved AOD. Some of the enhancement is consistent with clearcloud transition zone microphysics such as aerosol swelling. However, 3D radiation interaction between clouds and the surrounding clear air appears to be the primary cause of the high AOD near clouds

Implementaion of Regional Burnt Area Algorithms for the GBA2000 initiative

Technical Report - Global Burnt Area 200

Viljelykasvien tunnistaminen Sentinel-2 -satelliittikuvien avulla Suomessa

European Union member countries are obligated to control the validity of Common Agricultural Policy subsidy applications. Each member country performs manual inspection for at least 5% of these subsidy applications. This is both expensive and a considerable administrative burden. According to European Union, the crop type identifcation process in Common Agricultural Policy could be carried out using remote sensing or orthophoto imagery for an alternative to physical inspections by competent authorities. Automated crop type identifcation would reduce the costs signifcantly. This master’s thesis addressed the crop identifcation with optical Sentinel-2 satellite imagery in Finland. The aim was to investigate whether it was possible to reliably identify the crop growing in land parcels by using machine learning classifcation methods. This thesis presented an automated approach of identifying crops. Multiple different machine learning classifcation algorithms were trained and tested to find out the most suitable processing method, time period and classifcation algorithm by utilizing the land parcels obtained from the Finnish Agency for Rural Affairs. The developed processing method and most of the tested classifcation algorithms were able to perform relatively well in crop identifcation in cloudy growth period 2017 of Finland. Therefore, the developed method could be applied to different use cases and cloudy weather conditions. The further development and training of the classifcation algorithms could make it possible to utilize this approach in Finland as well as in other EU countries for the Common Agricultural Policy control and possibly in numerous other tasks.Euroopan Unionin jäsenmaiden on noudatettava yhteisen maatalouspolitiikan tarjoamien maataloustukihakemusten valvomista. Jokainen jäsenmaa suorittaa manuaalisen valvonnan vähintään 5% tukihakemuksista. Tämä on sekä kallista, että huomattava hallinnollinen taakka. Euroopan Unionin mukaan viljelykasvin tunnistamisprosessin voisi suorittaa kaukokartoitus- tai ortokuvien avulla paikan päällä tehtävien tarkastuksien sijaan. Automaattinen viljelykasvin tunnistaminen vähentäisi valvonnan kustannuksia huomattavasti. Tämä diplomityö käsitteli viljelykasvien tunnistamista optisten Sentinel-2 satelliittikuvien avulla Suomessa. Tarkoitus oli tutkia, pystyttäisiinkö koneoppimista hyödyntävien luokittelualgortimien avulla tunnistamaan pelloilla kasvavia maatalouskasveja. Tämä diplomityö esitteli automaattisen lähestymistavan viljelykasvin tunnistamiselle. Useaa erilaisia luokittelualgrotimia opetettiin ja testattiin kaikkein sopivimman prosessointimenetelmän, ajankohdan ja luokittelualgoritmin löytämiseksi Suomen oloihin Maaseutuviraston tarjoamien peltolohkojen avulla. Kehitetty prosessointimenetelmä ja suurin osa testatuista luokittelualgoritmeistä suoriutuivat suhteellisen hyvin viljelykasvin tunnistamisesta Suomen vuoden 2017 pilvisellä kasvukaudella. Tämän vuoksi on mahdollista, että kehitetty prosessointimenetelmää voisi hyödyntää myös erilaisissa ilmastoissa ja eri käyttötapauksissa. Jatkokehityksen ja lisäopetuksen avulla luokittelumenetelmät voisivat mahdollistaa tämän lähestymistavan hyödyntämistä yleisen maatalouspolitiikan maataloustukihakemusten valvontaan Suomessa ja myös muissa EU-maissa muiden käyttötapausten lisäksi

Cloud Shadow Detection and Removal from Aerial Photo Mosaics Using Light Detection and Ranging (LIDAR) Reflectance Images

The process of creating aerial photo mosaics can be severely affected by clouds and the shadows they create. In the CZMIL project discussed in this work, the aerial survey aircraft flies below the clouds, but the shadows cast from clouds above the aircraft cause the resultant mosaic image to have sub-optimal results. Large intensity variations, caused both from the cloud shadow within a single image and the juxtaposition of areas of cloud shadow and no cloud shadow during the image stitching process, create an image that may not be as useful to the concerned research scientist. Ideally, we would like to be able to detect such distortions and correct for them, effectively removing the effects of the cloud shadow from the mosaic. In this work, we present a method for identifying areas of cloud shadow within the image mosaic process, using supervised classification methods, and subsequently correcting these areas via several image matching and color correction techniques. Although the available data contained many extreme circumstances, we show that, in general, our decision to use LIDAR reflectance images to correctly classify cloud and not cloud pixels has been very successful, and is the fundamental basis for any color correction used to remove the cloud shadows. We also implement and discuss several color transformation methods which are used to correct the cloud shadow covered pixels, with the goal of producing a mosaic image which is free from cloud shadow effects

Evaluating Rotation-Equivariant Deep Learning Models for On-Orbit Cloud Segmentation

Cloud detection in satellite imagery is key for autonomously taking and downlinking cloud-free images of a target region as well as studying cloud-climate interactions and calibrating microwave radiometers. We propose a C8-equivariant dense U-Net, a rotation-equivariant deep learning model, trained on visible-spectrum, long-wave infrared (LWIR), and short-wave infrared (SWIR) imagery for on-orbit cloud detection. We train this model on the SPARCS1 dataset of Landsat 8 images and compare it to three related deep learning models, two rule-based algorithms, and to the literature. Additionally, we compare a C8-equivariant dense U-Net trained on VIS, LWIR, and SWIR imagery to the same algorithm trained on only VIS and LWIR, on only VIS and SWIR, and on only VIS imagery. We find that augmenting VIS imagery with SWIR imagery is most useful for missions where false positives (non-cloud pixels misidentified as cloud) are extremely costly, and that augmenting with LWIR imagery is most useful for missions where false negatives (cloud pixels misidentified as non-cloud) are extremely costly. We demonstrate also that our C8-equivariant dense U-Net achieves over 97% accuracy (over 99.5% when evaluated with a 2 pixel buffer at the cloud boundaries) on cloud segmentation on the SPARCS dataset, outperforming existing state-of-the-art algorithms as well as human operators, while remaining computationally lightweight enough to be usable on resource-constrained missions such as CubeSats

Continuous change detection and classification of land cover using all available Landsat data

Thesis (Ph.D.)--Boston UniversityLand cover mapping and monitoring has been widely recognized as important for understanding global change and in particular, human contributions. This research emphasizes the use ofthe time domain for mapping land cover and changes in land cover using satellite images. Unlike most prior methods that compare pairs or sets of images for identifying change, this research compares observations with model predictions. Moreover, instead of classifying satellite images directly, it uses coefficients from time series models as inputs for land cover mapping. The methods developed are capable of detecting many kinds of land cover change as they occur and providing land cover maps for any given time at high temporal frequency. One key processing step of the satellite images is the elimination of "noisy" observations due to clouds, cloud shadows, and snow. I developed a new algorithm called Fmask that processes each Landsat scene individually using an object-based method. For a globally distributed set ofreference data, the overall cloud detection accuracy is 96%. A second step further improves cloud detection by using temporal information. The first application ofthe new methods based on time series analysis found change in forests in an area in Georgia and South Carolina. After the difference between observed and predicted reflectance exceeds a threshold three consecutive times a site is identified as forest disturbance. Accuracy assessment reveals that both the producers and users accuracies are higher than 95% in the spatial domain and approximately 94% in the temporal domain. The second application ofthis new approach extends the algorithm to include identification of a wide variety of land cover changes as well as land cover mapping. In this approach, the entire archive of Landsat imagery is analyzed to produce a comprehensive land cover history ofthe Boston region. The results are accurate for detecting change, with producers accuracy of 98% and users accuracies of 86% in the spatial domain and temporal accuracy of 80%. Overall, this research demonstrates the great potential for use of time series analysis of satellite images to monitor land cover change