Nicola Falco

Nicola Falco
Lawrence Berkeley National Laboratory | LBL · Earth and Environmental Sciences Area

Doctor of Philosophy

About

62
Publications
13,953
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
814
Citations
Citations since 2017
40 Research Items
732 Citations
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200
Introduction
I am a Research Scientist at the Lawrence Berkeley National Laboratory in the Earth and Environmental Sciences Area. My research focuses on the development of methodologies based on image/signal processing, pattern recognition, and machine learning for multi-source data analysis and integration, including remote sensing (hyperspectral, LiDAR, high-res) and geophysical data, with applications in environmental monitoring, climate change, and precision agriculture.
Additional affiliations
October 2016 - February 2020
Lawrence Berkeley National Laboratory
Position
  • PostDoc Position
February 2015 - September 2016
University of Iceland
Position
  • PostDoc Position
Education
November 2011 - February 2015
University of Iceland
Field of study
  • Electrical and Computer Engineering
October 2011 - January 2015
Università degli Studi di Trento
Field of study
  • Information and Communication Technology

Publications

Publications (62)
Article
Full-text available
Landslides are a major natural hazard, threatening communities and infrastructure worldwide. The mitigation of these hazards relies on the understanding of their causes and triggering processes, which depends directly on soil properties, land use, and their changes over time. In this study, we propose a novel framework to estimate the probability o...
Preprint
Landslides are a major natural hazard, threatening communities and infrastructure worldwide. The mitigation of these hazards relies on the understanding of their causes and triggering processes, directly depending on soil properties, land use, and their variations over time. In this study, we propose a new approach combining geophysics and remote s...
Article
Full-text available
Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine lear...
Article
Full-text available
Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, b...
Article
Full-text available
In complex terrain, non‐parallel surfaces receive emitted radiation from adjacent surfaces. Qualitatively, where surface skin temperatures and lower tropospheric temperature and humidity are not uniform, the downwelling longwave radiation (DLR) will be determined not just by radiation from the atmosphere above a given location, but also by adjacent...
Article
Full-text available
In this study, we develop a watershed zonation approach for characterizing watershed organization and functions in a tractable manner by integrating multiple spatial data layers. We hypothesize that (1) a hillslope is an appropriate unit for capturing the watershed-scale heterogeneity of key bedrock-through-canopy properties and for quantifying the...
Article
Full-text available
Landslide inventory mapping (LIM) is an important application in remote sensing for assisting in the relief of landslide geohazards. However, while conducting LIM tasks performing change detection analysis using bi-temporal very high-resolution (VHR) remote sensing images, due to landslide usually occurred in a mountain area, the phenological diffe...
Preprint
Full-text available
Mortality rates during the COVID-19 pandemic have varied by orders of magnitude across communities in the United States. Individual, socioeconomic, and environmental factors have been linked to health outcomes of COVID-19. It is now widely appreciated that the environmental microbiome, composed of microbial communities associated with soil, water,...
Preprint
Full-text available
Background: During a pandemic, estimates of geographic variability in disease burden are important but limited by the availability and quality of data. Methods: We propose a framework for estimating geographic variability in testing effort, total number of infections, and infection fatality ratio (IFR). Because symptomatic people are more likely to...
Preprint
Full-text available
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging th...
Article
Full-text available
Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (Reco). However, accurate estimation of ET and Reco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we develop...
Article
Full-text available
Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combin...
Article
Full-text available
Change vector analysis (CVA) is a simple yet attractive method to detect changes with remote sensing images. Since its first introduction in 1980, CVA has received increased attention by the remote sensing community, leading to the definition of several new methodologies based on the CVA's concept while extending its applicability. In this study, w...
Article
Full-text available
Working within a vineyard in the Pessac Léognan Appellation of Bordeaux, France, this study documents the potential of using simple statistical methods with spatially-resolved and increasingly available electromagnetic induction (EMI) geophysical and normalized difference vegetation index (NDVI) datasets to accurately estimate Bordeaux vineyard soi...
Article
Full-text available
Land cover change detection (LCCD) with remote sensing images is an important application of Earth observation data because it provides insights into environmental health, global warming, and city management. In particular, very-high-resolution (VHR) remote sensing images can capture details of a ground object and offer an opportunity to detect lan...
Article
Full-text available
Background Biogeochemical exports from watersheds are modulated by the activity of microorganisms that function over micron scales. Here, we tested the hypothesis that meander-bound regions share a core microbiome and exhibit patterns of metabolic potential that broadly predict biogeochemical processes in floodplain soils along a river corridor. R...
Preprint
Full-text available
In this study, we develop a watershed zonation approach for characterizing watershed organization and function in a tractable manner by integrating multiple spatial data layers. Recognizing the coupled ecohydrogeological-biogeochemical interactions that occur across bedrock through canopy compartments of a watershed, we hypothesize that (1) suites...
Article
Full-text available
Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned...
Preprint
Full-text available
Soil thickness plays a central role in the interactions between vegetation, soils, and topography where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combine...
Article
Full-text available
Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water st...
Article
Full-text available
In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are hi...
Article
Full-text available
Long-term plot-scale studies have found water limitation to be a key factor driving ecosystem productivity in the Rocky Mountains. Specifically, the intensity of early summer (the ‘foresummer’ period from May to June) drought conditions appears to impose critical controls on peak ecosystem productivity. This study aims to (1) assess the importance...
Article
Full-text available
Point 1: In recent years the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality, and foliar trait distributions. However, we have barely be...
Preprint
Full-text available
Gradual changes in meteorological forcings (such as temperature and precipitation) are reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including water and carbon fluxes. Estimating evapotranspiration (ET) and ecosystem respiration (RECO) is essential for analyzing the effect of climate change on ecosystem behavi...
Preprint
Full-text available
Biogeochemical exports of C, N, S and H 2 from watersheds are modulated by the activity of microorganisms that function over micron scales. This disparity of scales presents a substantial challenge for development of predictive models describing watershed function. Here, we tested the hypothesis that meander-bound regions exhibit patterns of microb...
Article
Full-text available
This study aims to investigate the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain‐hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain‐hillslope study site (750 m x 750 m) in the Upper Colorado River Basin, we developed a new...
Article
Full-text available
Hekla volcano is known to have erupted at least 23 times in historical time (last 1100 years); often producing mixed eruptions of tephra and lava. The lava flow volumes from the 20th century have amounted 80% to almost 100% of the entire erupted volume. Therefore, evaluating the extent and volume of individual lava flows is very important when asse...
Article
Full-text available
Lava flows pose a hazard in volcanic environments and reset ecosystem development. A succession of dated lava flows provides the possibility to estimate the direction and rates of ecosystem development and can be used to predict future development. We examine plant succession, soil development and soil carbon (C) accretion on the historical (post 8...
Article
Full-text available
Extreme weather, fires, and land use and climate change are significantly reshaping interactions within watersheds throughout the world. Although hydrological–biogeochemical interactions within watersheds can impact many services valued by society, uncertainty associated with predicting hydrology-driven biogeochemical watershed dynamics remains hig...
Article
Full-text available
The empirical line (EL) calibration method is commonly used for atmospheric correction of remotely sensed spectral images and recovery of surface reflectance. The current EL-based methods are applicable to calibrate only single images. Therefore, the use of the EL calibration is impractical for imaging campaigns, where many (partially overlapped) i...
Chapter
Current and forthcoming sensor technologies and space missions are providing remote sensing scientists and practitioners with an increasing wealth and variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities for th...
Conference Paper
Full-text available
Mt. Hekla is among Iceland's most active volcanoes, erupting at least 23 times since the island was settled in c.871 AD. It is located on the highland margin bordering the Southern lowlands, the largest and most productive farmlands in Iceland. Hekla is a ridge shaped stratovolcano, producing both tephra and lava during eruptions. Due to the mounta...
Conference Paper
Background/Question/Methods Mt. Hekla is among Iceland‘s most active volcanoes, erupting at least 23 times since the island was settled in c.871 AD. It is located on the highland margin bordering the Southern lowlands, the largest and most productive farmlands in Iceland. Hekla is a ridge shaped stratovolcano, producing both tephra and lava during...
Article
Full-text available
Morphological attribute profiles are multilevel decompositions of images obtained with a sequence of transformations performed by connected operators. They have been extensively employed in performing multi-scale and region-based analysis in a large number of applications. One main, still unresolved, issue is the selection of filter parameters able...
Article
Full-text available
Random Forest (RF) is a widely used classifier to show a good performance of hyperspectral data classification. However, such performance could be improved by increasing the diversity that characterizes the ensemble architecture. In this paper, we propose a novel ensemble approach, namely rotation random forest via kernel principal component analys...
Conference Paper
Full-text available
Hekla volcano is one of the most active volcanic systems in Iceland and has erupted ~23 times since the settlement of Iceland in AD 874. Hekla is known for its mixed eruptions producing both explosive tephra deposits and effusive lava flows leaving a volcanically diverse landscape behind. The volcanic activity of Hekla has had a huge impact on the...
Article
Full-text available
In this paper, we propose a new version of ro- tation forest (RoF) method for the pixel-wise classification of hyperspectral image. RoF, which is an ensemble of decision tree classifiers, uses random feature selection and data transformation techniques (i.e. principal component analysis) to improve both the accuracy of base classifiers and the dive...
Article
Full-text available
The analysis of multi-temporal remote-sensing images is one of the main applications in Earth’s observation and monitoring. In this paper, we present a Matlab toolbox for change detection analysis of optical multi-temporal remote-sensing data in which unsupervised approaches, iterative principal component analysis (ITPCA), and iteratively reweighte...
Article
Full-text available
The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. In this paper, a novel methodology that combines spectral and spatial information for supervised hyperspectral image classification is proposed. A feature reduction strategy based on independ...
Conference Paper
Full-text available
This is a selection of results of the North State project, that demonstrate how innovative methods applied to the new Sentinel data streams can be combined with models to monitor carbon and water fluxes for pan-boreal Europe.
Conference Paper
Morphological attribute filters have been widely exploited for characterizing the spatial structures in remote sensing images. They have proven their effectiveness especially when computed in multi-scale architectures, such as for Attribute Profiles. However, the question how to choose a proper set of filter thresholds in order to build a represent...
Conference Paper
Full-text available
The objective of project North State, funded by Framework Program 7 of the European Union, is to develop innovative data fusion methods that exploit the new generation of multi-source data from Sentinels and other satellites in an intelligent, self-learning framework. The remote sensing outputs are interfaced with state-of-the-art carbon and water...
Thesis
Full-text available
Recent advances in sensor technology have led to an increased availability of hyperspectral remote sensing images with high spectral and spatial resolutions. These images are composed by hundreds of contiguous spectral channels, covering a wide spectral range of frequencies, in which each pixel contains a highly detailed representation of the refle...
Conference Paper
Full-text available
This article proposes a feature reduction technique for hyperspec-tral images using Independent Component Analysis (ICA). The proposed technique aims at extracting the best subset of class-informative independent components (ICs) for hyperspectral supervised classification. The selection of the most representative components is assured by the minim...
Article
Full-text available
This paper presents a thorough study on the performances of different independent component analysis (ICA) algorithms for the extraction of class-discriminant information in remote sensing hyperspectral image classification. The study considers the three implementations of ICA that are most widely used in signal processing, namely Infomax, FastICA,...
Conference Paper
The new generation of hyperspectral sensors can provide images with a high spectral and spatial resolution. Recent improvements in mathematical morphology have developed new techniques such as the Attribute Profiles (APs) and the Extended Attribute Profiles (EAPs) that can effectively model the spatial information in remote sensing images. The main...
Article
Full-text available
A new approach to change detection in very high resolution remote sensing images based on morphological attribute profiles (APs) is presented. A multiresolution contextual transformation performed by APs allows the extraction of geometrical features related to the structures within the scene at different scales. The temporal changes are detected by...
Conference Paper
In change detection analysis, the computation of the no-change distribution is affected when changed pixels are in large number in the scene. Because of this, the performance of several techniques are compromised. In this paper we compare two well known automatic change detection techniques (ITPCA and IRMAD) by performing an initial elimination of...
Conference Paper
The analysis of changes occurred in multi-temporal images acquired by the same sensor on the same geographical area at different dates is usually done by performing a comparison of the two images after co-registration. When one considers very high resolution (VHR) remote sensing images, the spatial information of the pixels becomes very important a...

Network

Cited By

Projects

Projects (3)
Project
Due to the rapid development of sensor technology, multi-modality remotely sensed datasets (e.g., optical, SAR, and LiDAR) that may differ in imaging mechanism, spatial resolution, and coverage can be achieved. Classification is one of the most important techniques to utilize these multi-modality datasets for land cover/land use and dynamic changes in various applications, e.g., precision agriculture, urban planning, and disaster responses. The utilization of multi-modality datasets has been an active topic in recent years because they can provide complementary information of the same scene, thus boosting the classification performance. The availability of big remote sensing multi-modality data platforms, e.g, ESA’s Copernicus program, Landsat series, and China GaoFen series, is likely to reinforce this trend. However, there still remains unsolved problems with multi-modality datasets, such as spectral/spatial variations, gaps in imaging mechanisms, and sensor-specific features of applications, which should be addressed further. This Special Issue, “Multi-Modality Data Classification: Algorithms and Applications”, will collect original manuscripts that address the above-mentioned challenging of multi-modality data classification, not only in the algorithm domain but also in the application domain. We kindly invite you to contribute to the following (but not exhaustive) topics that fit this Special Issue: multi-modality feature extraction, multi-modality data fusion, deep learning and transfer learning using multi-modality datasets, and classification and change detection of multi-modality datasets for any thematic application (related to urban, agricultural, ecological, and disaster ones) from local to global scales. Dr. Junshi Xia Dr. Nicola Falco Dr. Lionel Bombrun Prof. Jon Atli Benediktsson Guest Editors https://www.mdpi.com/journal/remotesensing/special_issues/Multi_Modality
Archived project
1. Build an open-access benchmark repository the Hekla remote sensing super site. Having a benchmark repository in Iceland is necessary for EMMIRS to test the proficiency of the developed mapping techniques. Furthermore, it ensures that new analysis techniques that are being developed internationally in the coming years will be tested with respect to Icelandic conditions. 2. Develop and implement methods for automation of geological and ecological mapping tuned to Icelandic nature. 3. Develop and implement methods for automated change detection allowing monitoring of geological and ecological changes in Iceland. 4. Investigate the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of cutting-edge modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution
Project
The Watershed Function Scientific Focus Area (SFA) seeks to determine how perturbations to mountainous watersheds (e.g., floods, drought, early snowmelt) impact the downstream delivery of water, nutrients, carbon, and metals over seasonal to decadal timescales through the development and testing of scale-adaptive approaches. For more info, visit http://watershed.lbl.gov/