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13 results on '"Araza, Arnan"'

1. Integrated global assessment of the natural forest carbon potential

Author

Mo, Lidong, Zohner, Constantin M., Reich, Peter B., Liang, Jingjing, de Miguel, Sergio, Nabuurs, Gert-Jan, Renner, Susanne S., van den Hoogen, Johan, Araza, Arnan, Herold, Martin, Mirzagholi, Leila, Ma, Haozhi, Averill, Colin, Phillips, Oliver L., Gamarra, Javier G. P., Hordijk, Iris, Routh, Devin, Abegg, Meinrad, Adou Yao, Yves C., Alberti, Giorgio, Almeyda Zambrano, Angelica M., Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F., Amaral, Iêda, Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A., Baker, Timothy R., Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G., Bastian, Meredith L., Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro H. S., Brandl, Susanne, Brearley, Francis Q., Brienen, Roel, Broadbent, Eben N., Bruelheide, Helge, Bussotti, Filippo, Cazzolla Gatti, Roberto, César, Ricardo G., Cesljar, Goran, Chazdon, Robin L., Chen, Han Y. H., Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D., Coomes, David A., Cornejo Valverde, Fernando, Corral-Rivas, José J., Crim, Philip M., Cumming, Jonathan R., Dayanandan, Selvadurai, de Gasper, André L., Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J., Eyre, Teresa J., Fandohan, Adandé Belarmain, Fayle, Tom M., Feldpausch, Ted R., Ferreira, Leandro V., Finér, Leena, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gianelle, Damiano, Glick, Henry B., Harris, David J., Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L., Hillers, Annika, Honorio Coronado, Eurídice N., Hui, Cang, Ibanez, Thomas, Imai, Nobuo, Jagodziński, Andrzej M., Jaroszewicz, Bogdan, Johannsen, Vivian Kvist, Joly, Carlos A., Jucker, Tommaso, Jung, Ilbin, Karminov, Viktor, Kartawinata, Kuswata, Kearsley, Elizabeth, Kenfack, David, Kennard, Deborah K., Kepfer-Rojas, Sebastian, Keppel, Gunnar, Khan, Mohammed Latif, Killeen, Timothy J., Kim, Hyun Seok, Kitayama, Kanehiro, Köhl, Michael, Korjus, Henn, Kraxner, Florian, Kucher, Dmitry, Laarmann, Diana, Lang, Mait, Lu, Huicui, Lukina, Natalia V., Maitner, Brian S., Malhi, Yadvinder, Marcon, Eric, Marimon, Beatriz Schwantes, Marimon-Junior, Ben Hur, Marshall, Andrew R., Martin, Emanuel H., Meave, Jorge A., Melo-Cruz, Omar, Mendoza, Casimiro, Mendoza-Polo, Irina, Miscicki, Stanislaw, Merow, Cory, Monteagudo Mendoza, Abel, Moreno, Vanessa S., Mukul, Sharif A., Mundhenk, Philip, Nava-Miranda, María Guadalupe, Neill, David, Neldner, Victor J., Nevenic, Radovan V., Ngugi, Michael R., Niklaus, Pascal A., Oleksyn, Jacek, Ontikov, Petr, Ortiz-Malavasi, Edgar, Pan, Yude, Paquette, Alain, Parada-Gutierrez, Alexander, Parfenova, Elena I., Park, Minjee, Parren, Marc, Parthasarathy, Narayanaswamy, Peri, Pablo L., Pfautsch, Sebastian, Picard, Nicolas, Piedade, Maria Teresa F., Piotto, Daniel, Pitman, Nigel C. A., Poulsen, Axel Dalberg, Poulsen, John R., Pretzsch, Hans, Ramirez Arevalo, Freddy, Restrepo-Correa, Zorayda, Rodeghiero, Mirco, Rolim, Samir G., Roopsind, Anand, Rovero, Francesco, Rutishauser, Ervan, Saikia, Purabi, Salas-Eljatib, Christian, Saner, Philippe, Schall, Peter, Schelhaas, Mart-Jan, Schepaschenko, Dmitry, Scherer-Lorenzen, Michael, Schmid, Bernhard, Schöngart, Jochen, Searle, Eric B., Seben, Vladimír, Serra-Diaz, Josep M., Sheil, Douglas, Shvidenko, Anatoly Z., Silva-Espejo, Javier E., Silveira, Marcos, Singh, James, Sist, Plinio, Slik, Ferry, Sonké, Bonaventure, Souza, Alexandre F., Stereńczak, Krzysztof J., Svenning, Jens-Christian, Svoboda, Miroslav, Swanepoel, Ben, Targhetta, Natalia, Tchebakova, Nadja, ter Steege, Hans, Thomas, Raquel, Tikhonova, Elena, Umunay, Peter M., Usoltsev, Vladimir A., Valencia, Renato, Valladares, Fernando, van der Plas, Fons, Van Do, Tran, van Nuland, Michael E., Vasquez, Rodolfo M., Verbeeck, Hans, Viana, Helder, Vibrans, Alexander C., Vieira, Simone, von Gadow, Klaus, Wang, Hua-Feng, Watson, James V., Werner, Gijsbert D. A., Wiser, Susan K., Wittmann, Florian, Woell, Hannsjoerg, Wortel, Verginia, Zagt, Roderik, Zawiła-Niedźwiecki, Tomasz, Zhang, Chunyu, Zhao, Xiuhai, Zhou, Mo, Zhu, Zhi-Xin, Zo-Bi, Irie C., Gann, George D., and Crowther, Thomas W.

Published
2023
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4. Past decade above-ground biomass change comparisons from four multi-temporal global maps

Author

Araza, Arnan, Herold, Martin, de Bruin, Sytze, Ciais, Philippe, Gibbs, David A., Harris, Nancy, Santoro, Maurizio, Wigneron, Jean-Pierre, Yang, Hui, Málaga, Natalia, Nesha, Karimon, Rodriguez-Veiga, Pedro, Brovkina, Olga, Brown, Hugh C.A., Chanev, Milen, Dimitrov, Zlatomir, Filchev, Lachezar, Fridman, Jonas, García, Mariano, Gikov, Alexander, Govaere, Leen, Dimitrov, Petar, Moradi, Fardin, Muelbert, Adriane Esquivel, Novotný, Jan, Pugh, Thomas A.M., Schelhaas, Mart-Jan, Schepaschenko, Dmitry, Stereńczak, Krzysztof, and Hein, Lars

Published
2023
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7. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Author

Araza, Arnan, de Bruin, Sytze, Herold, Martin, Quegan, Shaun, Labriere, Nicolas, Rodriguez-Veiga, Pedro, Avitabile, Valerio, Santoro, Maurizio, Mitchard, Edward T.A., Ryan, Casey M., Phillips, Oliver L., Willcock, Simon, Verbeeck, Hans, Carreiras, Joao, Hein, Lars, Schelhaas, Mart-Jan, Pacheco-Pascagaza, Ana Maria, da Conceição Bispo, Polyanna, Laurin, Gaia Vaglio, Vieilledent, Ghislain, Slik, Ferry, Wijaya, Arief, Lewis, Simon L., Morel, Alexandra, Liang, Jingjing, Sukhdeo, Hansrajie, Schepaschenko, Dmitry, Cavlovic, Jura, Gilani, Hammad, and Lucas, Richard

Published
2022
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9. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations.

Author

Santoro, Maurizio, Cartus, Oliver, Carvalhais, Nuno, Rozendaal, Danaë M. A., Avitabile, Valerio, Araza, Arnan, de Bruin, Sytze, Herold, Martin, Quegan, Shaun, Rodríguez-Veiga, Pedro, Balzter, Heiko, Carreiras, João, Schepaschenko, Dmitry, Korets, Mikhail, Shimada, Masanobu, Itoh, Takuya, Moreno Martínez, Álvaro, Cavlovic, Jura, Cazzolla Gatti, Roberto, and da Conceição Bispo, Polyanna

Subjects
FOREST biomass, TROPICAL dry forests, FOREST surveys, SYNTHETIC aperture radar, SPATIAL resolution, REMOTE sensing, TELECOMMUNICATION satellites
Abstract

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mgha-1 , where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg , our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at 10.1594/PANGAEA.894711 (Santoro, 2018). [ABSTRACT FROM AUTHOR]

Published
2021
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10. Towards nationwide mapping of bamboo resources in the Philippines: testing the pixel-based and fractional cover approaches.

Author

Dida, Jan Joseph V., Araza, Arnan B., Eduarte, Gerald T., Umali, Arthur Glenn A., Malabrigo, Pastor L., and Razal, Ramon A.

Subjects
*BAMBOO, *STANDARD deviations
Abstract

In tropical and subtropical countries, the awareness on the importance of bamboos to the environment and economy is increasing and so is the demand for spatial bamboo information. However, mapping bamboos especially those naturally grown has been challenging, as these grasses are often mixed with other land-use and land-cover (LULC). In this study, we used Sentinel 1 and Sentinel 2 remote sensing (RS) images, and their vegetation indices to accurately map the bamboos of Iloilo province in the Philippines using: (1) pixel-based method that mapped bamboos and other LULC at 10 m resolution, and (2) fractional cover method that mapped bamboo cover at 100 m resolution (% ha−1). A random forest model was trained for each method and then validated per hectare basis using a 50:50 training-validation ratio of a stratified random sample. The fractional cover method showed 0.34 higher Nash-Sutcliffe Efficiency (NSE) and 5.10% lower Root Mean Square Error (RMSE) than the pixel-based method. Further validation within upland and lowland sites also favoured the fractional cover method, but the results of the two methods were closer in the upland site (bamboo plantation). Errors at 10 m resolution especially in the lowlands were mostly commission errors, likely because of the spectral similarity and proximity between bamboos and > 14 vegetations. Averaging the RS inputs into 100 m resulted in at most 12% separation of reflectance values among bamboos, forests, and other vegetations. Using the bamboo cover map, a total of 14,795 (± 1,283) ha bamboos and 7.45 (± 4.20) million harvestable culms (poles) were estimated for the whole province, where 54% come from the lowland. We suggest using the fractional cover method for nationwide baselining of bamboo resources. [ABSTRACT FROM AUTHOR]

Published
2021
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11. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations.

Author

Santoro, Maurizio, Cartus, Oliver, Carvalhais, Nuno, Rozendaal, Danaë, Avitabilie, Valerio, Araza, Arnan, Bruin, Sytze de, Herold, Martin, Quegan, Shaun, Veiga, Pedro Rodríguez, Balzter, Heiko, Carreiras, João, Schepaschenko, Dmitry, Korets, Mikhail, Shimada, Masanobu, Itoh, Takuya, Martínez, Álvaro Moreno, Cavlovic, Jura, Gatti, Roberto Cazzolla, and Bispo, Polyanna da Conceição

Subjects
FOREST biomass, TROPICAL dry forests, SYNTHETIC aperture radar, FOREST surveys, REMOTE sensing, SATELLITE-based remote sensing
Abstract

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground forest biomass (dry mass, AGB) with a spatial resolution of 1 ha. Using an extensive database of 110,897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high carbon stock forests with AGB > 250 Mg ha−1 where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in literature (426-571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps, and identify major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the sub-tropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon and socio-economic modelling schemes, and provides a crucial baseline in future carbon stock changes estimates. The dataset is available at: https://doi.pangaea.de/10.1594/PANGAEA.894711 (Santoro, 2018). [ABSTRACT FROM AUTHOR]

Published
2020
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12. Intra-Annual Identification of Local Deforestation Hotspots in the Philippines Using Earth Observation Products.

Author

Araza, Arnan B., Castillo, Gem B., Buduan, Eric D., Hein, Lars, Herold, Martin, Reiche, Johannes, Gou, Yaqing, Villaluz, Maya Gabriela Q., and Razal, Ramon A.

Subjects
FOREST protection, FOREST conservation, FOREST management, FOREST degradation, FOREST products, LANDSLIDES, DEFORESTATION
Abstract

Like many other tropical countries, the Philippines has suffered from decades of deforestation and forest degradation during and even after the logging era. Several open access Earth Observation (EO) products are increasingly being used for deforestation analysis in support of national and international initiatives and policymaking on forest conservation and management. Using a combination of annual forest loss and near-real time forest disturbance products, we provide a comprehensive analysis of the deforestation events in three forest frontiers of the Philippines. A space-time pattern mining approach was used to map quarterly deforestation hotspots at 1 km pixel size (100 hectares), where hotspots are classified according to the spatial and temporal variability of the 2000–2020 deforestation in the study area. Our results revealed that 79–81% of the hotspots overlap with primary forests and 27–29% are inside the state-declared protected areas. The intra-annual analysis of deforestation in 2020 revealed an alarming trend, where most deforestation occurred between the 1st and 2nd quarter (92–94% in hotspot forests; 87–89% in non-hotspot forests), highly overlapping within the slash-and-burn farming season. We also found "new" hotspots (2020) formed mostly from landslide scars and partly from selective logging, the latter is believed to be underestimated. Our study paves the way for rapid and regular assessment of the country's deforestation, useful for the respective environmental institutions who convene several times a year. Moreover, our findings assert the imperative of alternative livelihoods to upland farmers, efficient forest protection activities, and even the mitigation of landslide risks. [ABSTRACT FROM AUTHOR]

Published
2021
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