Dr. Nicola Falco

Research Scientist
Lawrence Berkeley National Laboratory
Earth & Environmental Sciences Area (EESA)                                                              

Nicola Falco portrait
a church in Verona, Italy
satellite in space
Golden Gate Bridge in San Francisco

Research activity

Machine Learning

AI and machine learning techniques applied to remote sensing data for ecosystem characterization (land-cover mapping, multitemporal analysis, multi-variate analysis)

Remote Sensing

Hyperspectral imaging (ground/airborne/satellite) for ecosystem functioning and mapping (linking spectroscopy to plant traits, species composition and biodiversity)

Ecosystem Characterization

Landscape characterization is important to better understand the spatial heterogeneity governing aboveground and belowground processes

Precision agriculture

Investigation of soil-plant interactions through aboveground and belowground characterization (Ecoimaging).

Research

Projects

I am a member of projects utilizing machine learning and remote sensing across different ecosystems and application areas, including mountainous watersheds, agriculture, and terrestrial-aquatic interface.

1

Watershed Function

While watersheds are recognized as Earth's key functional unit for assessing and managing water resources, predicting their behavior remains a significant challenge.

Watershed function scientific focus area logo
2

AR1K - Smart farm research consortium

Our challenge: Improve the livelihood of farmers by optimizing soil amendments with biology and data science and managing the crop cycle to enhance soil fertility.

AR1K logo

Latest news

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Recent

Publications

2022

Landslide Inventory Mapping on VHR Images via Adaptive Region Shape Similarity

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Zhiyong Lv, Fengjun Wang, Weiwei Sun, Zhenzhen You, Nicola Falco, and Jón Atli Benediktsson
IEEE Transactions on Geoscience and Remote Sensing, 60, 1–11.

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 bitemporal very high-resolution (VHR) remote sensing images, due to landslide usually occurred in a mountain area, the phenological difference and outcrop rock may bring pseudochanges to LIM results. In this article, a novel change detection approach based on adaptive region shape similarity (ARSS) is proposed for LIM with VHR remote sensing images to improve detection performance. First, an adaptive region around each pixel is extended to explore the contextual information. Then, direction lines within an adaptive region are defined to describe the shape of the adaptive region. Finally, the pixels located on each direction line are taken into account to build the corresponding histogram. The shape similarity between the pairwise histogram curves is measured by using the discrete Frchet distance (DFD). Once the bitemporal images are processed by using the abovementioned steps, a change magnitude image (CMI) is generated, while a threshold is then used to obtain a final binary change map. The proposed approach is applied to three pairs of landslide site images acquired with aerial plane and one land use change dataset acquired by Quick Bird Satellite. Compared with ten state-of-the-art methods, the proposed approach achieved LIMs and detection results with higher accuracies and better performance.

2022

Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

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Enguehard, Léa, Nicola Falco, Myriam Schmutz, Michelle E. Newcomer, Joshua Ladau, James B. Brown, Laura Bourgeau-Chavez, and Haruko M. Wainwright
Remote Sensing, 14(14), 3285

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 learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations.

2022

Surface Parameters and Bedrock Properties Covary across a Mountainous Watershed: Insights from Machine Learning and Geophysics

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Uhlemann, Sebastian, Baptiste Dafflon, Haruko Murakami Wainwright, Kenneth Hurst Williams, Burke Minsley, Katrina Zamudio, Bradley Carr, Nicola Falco, Craig Ulrich, and Susan Hubbard
Science Advances 8 (12)

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, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/ vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.