Multi-temporal Forest Cover Change and Forest Density Trend Detection in a Mediterranean Environment

The loss of forests along with the various types of shrubs in the Mediterranean region is seen as an important driver of climate change and has been repeatedly related with the observed land degradation and desertification in the region. Nevertheless, the extent of woody perennial vegetation cover (WPVC) and its density remain largely unclear. Here, we apply a series of algorithms and methods operationally used in Australia for large-scale WPVC mapping and monitoring and demonstrate their applicability in the Mediterranean region using a Spanish area as the trial site. Five Landsat TM and ETM+ images from various dates spanning 14 years are used to map changes in the extent of WPVC and to identify areas with a declining, stabilising or recovering trend. Results show that the applied methodology, which incorporates (i) preprocessing of the Landsat imagery, (ii) a canonical variate analysis to spectrally discriminate between woody and non-woody land cover types, (iii) a conditional probability network and (iv) spectral indices for mapping woody cover and density trend, is highly successful and well suited for use in Mediterranean environments. A rigorous accuracy assessment is undertaken producing overall accuracies above 97% for both woody and non-woody cover types and all dates. Results also show that in the area of study, the majority of WPVC disturbances were due to forest fires, which represent the region's most frequent natural and anthropogenic disturbance. This raises significant concerns about the future of the area's WPVC. Regeneration compensated to some degree for the high disturbance rates. Copyright © 2015 John Wiley & Sons, Ltd

Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis

Land cover change on the Seward Peninsula: the use of remote sensing to evaluate the potential influences of climate change on historical vegetation dynamics

Thesis (M.S.) University of Alaska Fairbanks, 2000Vegetation on the Seward Peninsula, Alaska, which is characterized by transitions from tundra to boreal forest, may be sensitive to the influences of climate change on disturbance and species composition. To determine the ability to detect decadal-scale structural changes in vegetation, Change Vector Analysis (CVA) techniques were evaluated for Landsat TM imagery of the Seward Peninsula. Scenes were geographically corrected to sub-pixel accuracy and then radiometrically rectified. The CVA results suggest that shrubbiness is increasing on the Seward Peninsula. The CVA detected vegetation change on more than 50% of the burned region on TM imagery for up to nine years following fire. The use of both CVA and unsupervised classification together provided a more powerful interpretation of change than either method alone. This study indicates that CVA may be a valuable tool for the detection of land-cover change in transitional regions between tundra and boreal forest.Abstract -- List of figures -- List of tables -- Acknowledgements -- Introduction -- Methods -- Results -- Radiometric rectification -- Fire disturbance -- Land cover change on the Seward Peninsula -- Potential false change -- Discussion -- CVA vs. unsupervised classification -- Fire disturbance -- Land cover change on the Seward Peninsula -- Challenges and limitations -- Improvements and future directions -- Literature cited

Bi-spectral infrared algorithm for cloud coverage over oceans by the jem-euso mission program

The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrate the vast majority of discrepancies with the MODIS CM. The relatively high accuracy of the algorithm is a relevant result for the JEM-EUSO program. Further work will combine the proposed algorithm with complementary studies in the framework of JEM-EUSO to reinforce the CM above the cloud edges.This research was funded by MADRID GOVERNMENT (Comunidad de Madrid;Spain), PEJ-2018-AI_TIC-11476 and by the SPANISH MINISTRY (MICINN), RTI2018-099825-B-C33. The APC was funded by the MADRID GOVERNMENT, EPUC3M14

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

Automatic methods for crop classification by merging satellite radar (sentinel 1) and optical (sentinel 2) . data and artificial intelligence analysis

Land use and land cover maps can support our understanding of coupled human- environment systems and provide important information for environmental modelling and water resource management. Satellite data are a valuable source for land use and land cover mapping. However, cloud-free or weather independent data are necessary to map cloud-prone regions. Merging radar with optical images would increase the accuracy of the study. Agricultural land cover is characterized by strong variations within relatively short time intervals. These dynamics are challenging for land cover classifications on the one hand, but deliver crucial information that can be used to improve the machine learning classifier’s performance on the other hand. A parcel-based map of the main crop classes of the Netherlands was produced implementing a script on GEE and using Copernicus data. The machine-learning model used is a Random Forest Classifier. This was done by combining time series of radar and multispectral images from Sentinel 1 and Sentinel 2 satellites, respectively. The results show the potential of providing useful information delivered by entirely open source data and uses a cloud computing-based approach. The algorithm combines the two satellites data of one year in a multibands image to feed in the classifier. Standard deviation and several vegetation indexes were added in order to have more variables for each 15-day-median image composite. The process paid particular attention to time variability of mean values of each field. This will provide useful information both for understanding differences among crops and variability over the phenology of the plant. The accuracy assessment demonstrates that several crop types (i.e. corn, tulip) can be better classified with both radar and optical images while others (i.e. sugar beet, barley) have an increased accuracy with only radar. The overall accuracy of RFC with optical and radar is 76% while it is 74% if only radar is used

Multispectral imaging and analysis of the Archimedes Palimpsest

The Archimedes Palimpsest is a manuscript that has been preserved for approximately 1,000 years. Among its pages are some of the few known sources of treatises from the Greek mathematician Archimedes. The writing has been overwritten with prayer text, called the Euchologion, and portions of the faded Archimedes text are difficult to read. This research investigates methods to detect the presence of ink in the Archimedes Palimpsest using state-of-the-art image processing techniques applied to data from X-ray fluorescence (XRF) scans. In an effort to extract more legible text, various methods of imaging have been applied to the Archimedes manuscript. Recent X-ray fluorescence images of the palimpsest suggest the possibility of detecting individual text layers and isolating them from each other. This is encouraging, since many of the pages have also been partially masked by gold-leafed, Byzantine-style artwork, making the Archimedes writing difficult to see with the human eye. The scans measure the X radiation emitted by atoms on the pages that have been excited by other higher energy X rays incident to the parchment. This caused certain elements within the manuscript, such as the iron in the ink, to fluoresce at energies that are specific to the particular material. A total of 2,000 different energy levels, or bands, were recorded. To evaluate the data contained in this large number of bands, a single data set was created that included all bands, referred to as a datacube, which shows the transition of each pixel through the spectrum. Special image processing tools, developed for use in the field of remote sensing to process aerial and satellite data, can be used to detect certain patterns within the datacube. Each tool is then used to segregate the noise from the relevant data in the datacube. The datacube for this thesis research was created from a small portion of one page of the Archimedes Palimpsest, and may inherently be subject to certain noise limitations. This study focuses on two main objectives: Evaluation of X-ray fluorescence data to determine which energy levels contain useful information about the layers of text. Creation of a pseudocolored composite RGB image of a portion of enhanced Archimedes text, similar to previous pseudocolored MSI images. Results from this study show that only a few regions within the datacube contain information relevant to the layers of text. Certain algorithms, such as principal component analysis and minimum noise fraction, showed distinct information about trace elements fluorescing in the ink and parchment. Meaningful data near the spectral line of each trace element was detected after disbanding the datacube into smaller regions. Enough information was obtained as a result to create colorized RGB composite images that enhance the contrast of the Archimedes writing relative to the overwritten text. It is hoped that this research can improve the method for identifying useful bands of information within datacubes. The research may also have created a repeatable method for detecting useful bands of information in similar datacubes. State-of-the-art multispectral imaging applications were specifically applied to detect, extract, and enhance previously illegible writings that are of interest to scholars and museums in particular