5 results on '"Ocón, Jonathan Pando"'
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2. Global tropical dry forest extent and cover: A comparative study of bioclimatic definitions using two climatic data sets.
- Author
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Ocón, Jonathan Pando, Ibanez, Thomas, Franklin, Janet, Pau, Stephanie, Keppel, Gunnar, Rivas-Torres, Gonzalo, Shin, Michael Edward, and Gillespie, Thomas Welch
- Subjects
Humans ,Trees ,Ecosystem ,Tropical Climate ,Agriculture ,Africa ,Forests ,General Science & Technology - Abstract
There is a debate concerning the definition and extent of tropical dry forest biome and vegetation type at a global spatial scale. We identify the potential extent of the tropical dry forest biome based on bioclimatic definitions and climatic data sets to improve global estimates of distribution, cover, and change. We compared four bioclimatic definitions of the tropical dry forest biome-Murphy and Lugo, Food and Agriculture Organization (FAO), DryFlor, aridity index-using two climatic data sets: WorldClim and Climatologies at High-resolution for the Earth's Land Surface Areas (CHELSA). We then compared each of the eight unique combinations of bioclimatic definitions and climatic data sets using 540 field plots identified as tropical dry forest from a literature search and evaluated the accuracy of World Wildlife Fund tropical and subtropical dry broadleaf forest ecoregions. We used the definition and climate data that most closely matched field data to calculate forest cover in 2000 and change from 2001 to 2020. Globally, there was low agreement (< 58%) between bioclimatic definitions and WWF ecoregions and only 40% of field plots fell within these ecoregions. FAO using CHELSA had the highest agreement with field plots (81%) and was not correlated with the biome extent. Using the FAO definition with CHELSA climatic data set, we estimate 4,931,414 km2 of closed canopy (≥ 40% forest cover) tropical dry forest in 2000 and 4,369,695 km2 in 2020 with a gross loss of 561,719 km2 (11.4%) from 2001 to 2020. Tropical dry forest biome extent varies significantly based on bioclimatic definition used, with nearly half of all tropical dry forest vegetation missed when using ecoregion boundaries alone, especially in Africa. Using site-specific field validation, we find that the FAO definition using CHELSA provides an accurate, standard, and repeatable way to assess tropical dry forest cover and change at a global scale.
- Published
- 2021
3. Pruning our expectations: The implications and applications of earth observation in developing urban forestry tools in Los Angeles County
- Author
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Ocón, Jonathan Pando
- Subjects
Remote sensing ,Urban forestry ,Urban planning ,LiDAR ,Machine Learning ,Multispectral ,Science Applications ,Socio-ecological Systems ,Species Classification - Abstract
This dissertation, largely funded by the County of Los Angeles to optimize its urban forest planning and management, conducts a critical examination of the implications, applications, and limitations of remote sensing technology in applied science. Leveraging technologies such as Light Detection and Ranging (LiDAR) and high-resolution optical data, remote sensing has offered unparalleled insights into the Earth's surface and atmosphere. Specific applications, such as tree crown segmentation and creation of precise 3D environmental models, have proven invaluable in fields including ecology and urban planning. However, this technology is not without its challenges. An over-reliance on remote sensing data, absent corroborative ground-truthing, can lead to flawed conclusions. Furthermore, using low-resolution imagery for policy decisions concerning fine-scale, on-the-ground issues could create discrepancies between the data and actual conditions, undermining the effectiveness of interventions. In my first and second chapter of this dissertation, I demonstrate that remote sensing technology has become indispensable, particularly for monitoring individual species over time for urban forest management. By identifying and delineating individual tree crowns, this technology enables accurate estimation of species composition and condition. This detailed information facilitates species health monitoring and early disease detection, thereby aiding effective management interventions. However, the effectiveness of remote sensing in urban forestry necessitates continuous data update and integration of ground-based observations for validation. In my third chapter, I transition from the practical applications of remote sensing in urban forestry, and lean on previous research experience, including my collaboration with Los Angeles County, to delve into the social dimensions of this technology. The concept of "socializing the pixel" provides a critical framework for understanding how remote sensing operates within and is influenced by sociopolitical contexts. This perspective recognizes that remote sensing is not a purely objective tool but is embedded within social structures and power dynamics. Stakeholder engagement is thus crucial to consider diverse perspectives in data interpretation and application, enhancing the technology's effectiveness and local relevance. Conversely, power imbalances between remote sensing experts and non-experts, and denial of local knowledge can lead to biased results or misinterpretation of data. Hence, returning to the concept of "socializing the pixel" is critical to ensure effective and equitable use of remote sensing technology.
- Published
- 2023
4. A Near Four-Decade Time Series Shows the Hawaiian Islands Have Been Browning Since the 1980s
- Author
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Madson, Austin, primary, Dimson, Monica, additional, Fortini, Lucas Berio, additional, Kawelo, Kapua, additional, Ticktin, Tamara, additional, Keir, Matt, additional, Dong, Chunyu, additional, Ma, Zhimin, additional, Beilman, David W., additional, Kay, Kelly, additional, Ocón, Jonathan Pando, additional, Gallerani, Erica, additional, Pau, Stephanie, additional, and Gillespie, Thomas W., additional
- Published
- 2022
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5. A Global Analysis of Tropical Dry Forest Extent and Cover Based on Climatic Definitions
- Author
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Ocón, Jonathan Pando and Ocón, Jonathan Pando
- Abstract
Tropical dry forests have been estimated to comprise 42% of all tropical forested biomes and are believed to be one of the world's most endangered ecosystems. There is a growing interest in identifying forest extent and forest change in tropical dry forest regions, especially to identify dry forest that deserve a high conservation priority at a global spatial scale. There is currently a debate concerning the classification and extent of tropical dry forest at the global scale. We identify the extent of tropical dry forest regions based on commonly used climatic definitions and datasets to improve global estimates of tropical dry forest extent. We compare climatic definitions of tropical dry forest (Murphy and Lugo, FAO, Dryflor, Aridity Index) using Worldclim, CHELSA, and Global Aridity and PET climatic datasets (1 km) and compare results to the World Wildlife Fund's Terrestrial Ecoregions (Tropical and Subtropical Dry Broadleaf Forest), as well as 579 field plots identified as tropical dry forest. Understanding the best method to estimate global tropical dry forest extent gives both researchers and policy makers a vital tool to begin protecting this critically endangered and valuable resource. We identify methods that most accurately predicted tropical dry forest extent. The global extents of tropical dry forest regions varied significantly with the Aridity Index predicting the largest extent, Murphy and Lugo and FAO predicting similar extents, and DryFlor predicting the smallest extent regardless of climatic dataset used. Globally, there was low agreement between climatic definitions and WWF Ecoregions. FAO and the Aridity Index climate definitions had the highest agreement with WWF Tropical and Subtropical Dry Broadleaf Forest Ecoregions (57%) while FAO (76%) and Murphy and Lugo (69%) definitions had the highest agreement with field plots. However, extents and accuracy varied significantly by regions, biodiversity hotspots, and island archipelagos. Tropical dry forest region extent varies significantly based on climatic definition but not climatic datasets at a global spatial scale. Nearly half of all tropical dry forests will be missed when only analyzing WWF Ecoregion boundaries and climatic definitions will be needed to estimate dry forest cover and change. There was high heterogeneity among climatic definitions at a regional and local spatial scale suggesting that climate definition can only provide a first order hypothesis about the distribution of dry forests and data on phenology, forest structure, and composition are still needed to compare local tropical dry forest extent.
- Published
- 2020
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