849 research outputs found

    Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series

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    The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8–10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results

    A Data-driven Approach for Mapping Grasslands at a Regional Scale

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    Ph.D.University of Kansas, Geology 2019The goal of this research was to use a data-driven approach to develop a regional scale grassland mapping protocol with the following objectives. First, identify and characterize the spatial distribution of grassland types and land use across Kansas as well as the static or dynamic nature of grasslands over time using multi-year U.S. Department of Agriculture (USDA) Farm Service Agency (FSA) 578 data. Second, evaluate the spectral separability of four hierarchies of grassland types and land use using FSA 578 data, multi-seasonal Landsat 8 spectral bands, Landsat 8 Normalized Difference Vegetation Index (NDVI) data, and Moderate Resolution Imaging Spectrometer (MODIS) NDVI time series. Third, determine the optimal data combination, and the appropriate thematic resolution, for mapping grassland type by evaluating the modeling performance of the Random Forest (RF) classifier. A county-level analysis of the multi-year FSA 578 data found that the data were not all-inclusive of total grasslands across Kansas, but were sufficient to illustrate regional trends in grassland type, land use, and field size. Eastern Kansas was found to be more diverse in grassland type, more variable in land use, and contained a high number of smaller fields. Conversely, western Kansas consisted of larger fields that were primarily grazed native grasslands and land enrolled in the Conservation Reserve Program (CRP). These results indicate a more complex grassland landscape to map in eastern Kansas, while also providing guidance for training sample distributions for image classification. Jeffries-Matusita (JM) distance statistics were calculated for three-date multispectral Landsat 8, three-date Landsat 8 NDVI, and 23-period, 16-day composite Terra MODIS NDVI time series. The results indicate that combining the three datasets maximized the spectral separability of grassland types across all four grassland-type hierarchies. A comparison of the three datasets showed that multispectral Landsat 8 data had the highest JM distance statistics (which indicates the most separability). JM distance statistics calculated by-band and by-period consistently showed that information from spring and fall was more important than summer for separating grassland types. The results showed lower separability for land-use classes within a grassland type versus between grassland types. The spectral separability of pairwise comparisons incorporating land use between grassland types varied, indicating that land use does affect spectral separability in some instances. On the other hand, JM distance statistics did not substantially drop when more refined grassland types were aggregated to coarser grassland type classes (e.g. Level-1: cool- and warm-season), indicating that land use does not negatively affect the spectral separability of functional grassland types. The results indicate low spectral separability between brome and fescue but moderate to high separability between native and CRP, suggesting the use of a Level-1 or Level-2 thematic classification scheme for the study area. Finally, random forest models were constructed and evaluated using 2015 FSA 578 data and four datasets of remotely sensed data in two adjacent Landsat scenes (path/rows). Models were created for each of the four grassland hierarchies. The results showed that out-of-bag (OOB) error increased with grassland hierarchy complexity (the number of thematic classes) and OOB error was lowest for the combined remotely sensed dataset. Mapping CRP as a separate grassland type resulted in low producer’s accuracy levels, with CRP largely mapped as warm-season grasslands, suggesting the Level-1 classification scheme was appropriate for regional mapping of grassland types. Path/rows 27/33 and 28/33 had OOB overall accuracy levels of 87% and 92%, respectively. User’s and producer’s accuracy levels indicate that cool-season grasslands were mapped more accurately in path/row 27/33 where that class is more dominant than in 28/33. Using test data (withheld verification data) unexpectedly increased overall accuracy levels by 4% and 6% over OOB accuracies, which may have resulted from varying data proportions between OOB and test data, suggesting the need for further evaluation

    Mapping of peanut crops in Queensland, Australia using time-series PROBA-V 100-m normalized difference vegetation index imagery

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    Mapping of peanut crops is essential in supporting peanut production, yield prediction, and commodity forecasting. While ground-based surveys can be used over small areas, the development of remote-sensing technologies could provide rapid and inexpensive crop area estimates with high accuracy over large regions. Some of these recent earth observation satellite systems, such as the Project for On-Board Autonomy Vegetation (PROBA-V), have the advantage of increased spatial and temporal resolution. With a study area located in the South Burnett region, Queensland, Australia, the primary aim of this study was to assess the ability of time-series PROBA-V 100-m normalized difference vegetation index (NDVI) for peanut crop mapping. Two datasets, i.e., PROBA-V NDVI time-series imagery and the corresponding phenological parameters generated from TIMESAT data analysis technique, were classified using maximum likelihood classification, spectral angle mapper, and minimum distance classification algorithms. The results show that among all methods used, the application of MLC in PROBA-V NDVI time series produced very good overall accuracy, i.e., 92.75%, with producer and user accuracy of each class ≥78.79  %  . For all algorithms tested, the mapping of peanut cropping areas produced satisfactory classification results, i.e., 75.95% to 100%. Our study confirmed that the use of finer resolution 100 m of PROBA-V imagery (i.e., relative to MODIS 250-m data) has contributed to the success of mapping peanut and other crops in the study area

    Mapping Wheat Growing Areas of Turkey by Integrating Multi-Temporal NDVI Data and Official Crop Statistics

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    Wheat is the most widely cultivated crop in the world providing critical food source of most countries. It exceeds most of the grain crops in acreage and production because of its ability to grow in wide range of climatic and geographic conditions. Timely and reliable information on wheat acreages is essential for government services in order to formulate their policies for planning of agricultural production and monitoring their food supply. Traditionally, agricultural statistics is considered as the main source of such information. Unfortunately, existing statistical data of wheat acreages of Turkey, mostly dependent on farmers’ declarations, does not provide spatial information of where this crop specifically is grown. Satellite remote sensing technology can enable the acquisition of such information indirectly with the use of ancillary data of crop statistics. This study aims to determine wheat cultivation areas of Turkey as percentage per unit area in a crop map by integrating time series satellite NDVI imagery with the official crop statistics through regression analysis. The regression results indicated that satellite data explained 95.8% of the variability in official wheat crop statistics and actual wheat cropping areas were significantly related to NDVI-based wheat classes. Validation of the produced wheat map showed that there was good agreement between actual wheat fractions and estimated NDVI-based wheat fractions explaining approximately 69% (Adj. R2) of the total variability between them. This study suggests use of the methodology employed here to governing bodies that need to identify and to map current wheat cropping areas

    The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

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    Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information SystemsImaging and sensing technologies are constantly evolving so that, now, the latest generations of satellites commonly provide with Earth’s surface snapshots at very short sampling periods (i.e. daily images). It is unquestionable that this tendency towards continuous time observation will broaden up the scope of remotely sensed activities. Inevitable also, such increasing amount of information will prompt methodological approaches that combine digital image processing techniques with time series analysis for the characterization of land cover distribution and monitoring of its dynamics on a frequent basis. Nonetheless, quantitative analyses that convey the proficiency of three-dimensional satellite images data sets (i.e. spatial, spectral and temporal) for the automatic mapping of land cover and land cover time evolution have not been thoroughly explored. In this dissertation, we investigate the usefulness of multispectral time series sets of medium spatial resolution satellite images for the regular land cover characterization at the national scale. This study is carried out on the territory of Continental Portugal and exploits satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and MEdium Resolution Imaging Spectrometer (MERIS). In detail, we first focus on the analysis of the contribution of multitemporal information from multispectral satellite images for the automatic land cover classes’ discrimination. The outcomes show that multispectral information contributes more significantly than multitemporal information for the automatic classification of land cover types. In the sequence, we review some of the most important steps that constitute a standard protocol for the automatic land cover mapping from satellite images. Moreover, we delineate a methodological approach for the production and assessment of land cover maps from multitemporal satellite images that guides us in the production of a land cover map with high thematic accuracy for the study area. Finally, we develop a nonlinear harmonic model for fitting multispectral reflectances and vegetation indices time series from satellite images for numerous land cover classes. The simplified multitemporal information retrieved with the model proves adequate to describe the main land cover classes’ characteristics and to predict the time evolution of land cover classes’individuals

    Advanced Processing of Multispectral Satellite Data for Detecting and Learning Knowledge-based Features of Planetary Surface Anomalies

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    abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes. This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

    Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery

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    Burnt area mapping in humid tropical insular Southeast Asia using medium resolution (250-500m) satellite imagery is characterized by persisting cloud cover, wide range of land cover types, vast amount of wetland areas and highly varying fire regimes. The objective of this study was to deepen understanding of three major aspects affecting the implementation and limits of medium resolution burnt area mapping in insular Southeast Asia: 1) fire-induced spectral changes, 2) most suitable multitemporal compositing methods and 3) burn scars patterns and size distribution. The results revealed a high variation in fire-induced spectral changes depending on the pre-fire greenness of burnt area. It was concluded that this variation needs to be taken into account in change detection based burnt area mapping algorithms in order to maximize the potential of medium resolution satellite data. Minimum near infrared (MODIS band 2, 0.86μm) compositing method was found to be the most suitable for burnt area mapping purposes using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In general, medium resolution burnt area mapping was found to be usable in the wetlands of insular Southeast Asia, whereas in other areas the usability was seriously jeopardized by the small size of burn scars. The suitability of medium resolution data for burnt area mapping in wetlands is important since recently Southeast Asian wetlands have become a major point of interest in many fields of science due to yearly occurring wild fires that not only degrade these unique ecosystems but also create regional haze problem and release globally significant amounts of carbon into the atmosphere due to burning peat. Finally, super-resolution MODIS images were tested but the test failed to improve the detection of small scars. Therefore, super-resolution technique was not considered to be applicable to regional level burnt area mapping in insular Southeast Asia.Laaja valikoima erilaisia maankäyttöluokkia, pilvisyys ja kosteikkoalueiden suuri määrä luovat erityispiirteet paloalueiden kartoitukselle Kaakkois-Aasian saariston kostean troppisissa olosuhteissa keskiresoluutioisilla (250m-500m) satelliittikuva-aineistoilla. Tämän tutkimuksen tavoitteena oli syventää ymmärrystä keskiresoluutioisen paloaluekartoituksen toteutukseen ja rajoituksiin Kaakkois-Aasian saaristossa vaikuttavista tekijöistä. Tutkimuksen tulokset paljastivat suurta vaihtelua tulipalojen aiheuttamissa heijastussäteilyn muutoksissa riippuen palaneen alueen vehreydestä ennen tulipaloa. Johtopäätöksenä todettiin että keskiresoluutioisten satelliittikuvien koko potentiaalin hyödyntämiseksi paloalueiden kartoituksessa tämä vaihtelu tulisi ottaa huomioon paloalueiden havainnointialgoritmeissa jotka perustuvat heijastussäteilyn muutosten seurantaan. Tähän ajatukseen perustuvaa paloalueiden kartoitusta myös kokeiltiin aineistoilla jotka oli tutkimuksissa todettu parhaiten tarkoitukseen sopiviksi. Paloalueiden muoto- ja kokojakauman analyysiin sekä käytännön testeihin perustuen keskiresoluutioinen paloalueiden kartoitus todettiin käyttökelpoiseksi Kaakkois-Aasian saariston kosteikkoalueilla. Muilla alueilla sen sijaan paloalueiden pieni koko uhkasi vakavasti sen käyttökelpoisuutta. Keskiresoluutioisten satelliittikuva-aineistojen käyttökelpoisuus paloalueiden kartoitukseen kosteikkoalueilla on kuitenkin merkittävää sillä viime aikoina Kaakkois-Aasian kosteikkoalueet ovat monilla tieteenaloilla nousseet kiinnostuksen kohteeksi vuosittain esiintyvien tulipalojen takia. Vuosittaiset tulipalot eivät ainoastaan heikennä näitä ainutlaatuisia ekosysteemejä vaan lähinnä palavan turpeen johdosta myös aiheuttavat pahoja alueellisia savusumuongelmia ja vapauttavat maailmanlaajuisesti merkittäviä määriä hiilidioksidia ilmakehään. Tämän tutkimuksen tulokset osaltaan luovat pohjaa yhä tarkempien alueellisten paloalueiden kartoitusmenetelmien kehittämiselle. Näillä menetelmillä kerättävä tieto paloalueiden laajuudesta ja sijainneista antaa muiden alojen tutkijoille yhä paremmat mahdollisuudet arvioida Kaakkois-Aasian saariston kosteikkoalueiden tulipalojen paikallisia, alueellisia ja maailmanlaajuisia vaikutuksia

    Assessment of k-Nearest Neighbor and Random Forest classifiers for mapping forest fire areas in central Portugal using Landsat-8, Sentinel-2, and Terra Imagery

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    Forest fires threaten the population’s health, biomass, and biodiversity, intensifying the desertification processes and causing temporary damage to conservation areas. Remote sensing has been used to detect, map, and monitor areas that are affected by forest fires due to the fact that the different areas burned by a fire have similar spectral characteristics. This study analyzes the performance of the k-Nearest Neighbor (kNN) and Random Forest (RF) classifiers for the classification of an area that is affected by fires in central Portugal. For that, image data from Landsat-8, Sentinel-2, and Terra satellites and the peculiarities of each of these platforms with the support of Jeffries–Matusita (JM) separability statistics were analyzed. The event under study was a 93.40 km2 fire that occurred on 20 July 2019 and was located in the districts of Santarém and Castelo Branco. The results showed that the problems of spectral mixing, registration date, and those associated with the spatial resolution of the sensors were the main factors that led to commission errors with variation between 1% and 15.7% and omission errors between 8.8% and 20%. The classifiers, which performed well, were assessed using the receiver operating characteristic (ROC) curve method, generating maps that were compared based on the areas under the curves (AUC). All of the AUC were greater than 0.88 and the Overall Accuracy (OA) ranged from 89 to 93%. The classification methods that were based on the kNN and RF algorithms showed satisfactory results.Research was supported by PAIUJA-2019/2020 and CEACTEMA from University of Jaen (Spain), and RNM-282 research group from the Junta de Andalucia (Spain). Special thanks to the four anonymous reviewers for their insightful comments

    Assessment of Time-Series MODIS Data for Cropland Mapping in the U.S. Central Great Plains

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    The goal of this study was to further investigate the potential of MODIS NDVI 250-m data for crop spectral characterization, discrimination, and mapping in the Great Plains of the USA using various exploratory approaches. GIS operations, and reference data refinement using clustering and visual assessment of each crop's NDVI cluster profiles in Nebraska, demonstrated that it is possible to devise an alternative reference data set and refinement plan that redresses the unexpected loss of training and validation data. A pixel-level analysis of the time-series MODIS 250-m NDVI for 1,288 field sites representing each of the eight cover types under investigation across Nebraska found that each crop type had a distinctive MODIS 250-m NDVI profile corresponding to the crop calendar. A visual and statistical comparison of the average NDVI profiles showed that the crop types were separable at different times of the growing season based on their phenology-driven spectral-temporal differences. In Kansas, an initial investigation revealed that there was near-complete agreement between the winter wheat crop profiles but that there were some minor differences in the crop profiles for alfalfa and summer crops between 2001 and 2005. However, the profiles of summer crops - corn, grain sorghum, and soybeans - displayed a shift to the right by at least 1 composite date, indicative of possible late crop planting and emergence. Alfalfa and summer crops, seem to suggest that time series NDVI response curves for crops over a growing period for one year of valid ground reference data may not be used to map crops for a different year without taking into account the climatic and/or environmental conditions of each year

    Global burned area mapping from Sentinel-3 Synergy and VIIRS active fires

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    After more than two decades of successful provision of global burned area data the MODIS mission is near to its end. Therefore, using alternative images to generate moderate resolution burned area maps becomes critical to guarantee temporal continuity of these products. This paper presents the development of a hybrid algorithm based on Copernicus Sentinel-3 (S3) Synergy (SYN) data and Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fires for global detection of burned areas. Using the synergistic and co-located measurements of OLCI and SLSTR instruments on board S3A and S3B, the SYN product offers global, near-daily surface reflectance data at 300 m for both sensors. Our algorithm relied on SYN shortwave infrared (SWIR) bands to compute a multi-temporal separability index that enhanced the burn signal. Active fires from the VIIRS sensor were used to generate spatio-temporal clusters for determining local detection thresholds. Active fires were filtered from those thresholds to obtain the seeds from which a contextual growing was applied to extract burned patches. The algorithm was processed globally for 2019 data to generate a new burned area product, named FireCCIS310. Based on a stratified random sampling, error estimates showed an important reduction of omission errors versus other global burned area products while keeping the commission errors at a similar level (Oe = 41.2% ± 3.0%, Ce = 19.2% ± 1.7%). The new FireCCIS310 dataset included 4.99 million km2 for the year 2019, which implied around 1 million more than the precursor FireCCI51 product, based on MODIS 250 m reflectance values. Temporal reporting accuracy was improved as well, detecting 53% of the burned pixels within a 0–1 day difference. Besides, the new product was much less affected by the border effects than FireCCI51, as a result of an improved active fire filtering process. The FireCCIS310 product is accessible through the CCI Open Data Portal (https://climate.esa.int/es/odp/#/dashboard, last accessed on July 2022).This research has been supported by the ESA Climate Change Initiative - Fire ECV (contract no. 4000126706/19/I-NB), and the Spanish Ministry of Science, Innovation, and Universities through a FPU doctoral fellowship (FPU17/02438)
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