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| ALMEIDA, D. R. A. de; ZAMBRANO, A. M. A.; BROADBENT, E. N.; WENDT, A. L.; FOSTER, P.; WILKINSON, B. E.; SALK, C.; PAPA, D. de A.; STARK, S. C.; VALBUENA, R.; GORGENS, E. B.; SILVA, C. A.; BRANCALION, P. H. S.; FAGAN, M.; MELI, P.; CHAZDON, R.. |
| Drone-based remote sensing is a promising new technology that combines the benefits of ground-based and satellite-derived forest monitoring by collecting fine-scale data over relatively large areas in a cost-effective manner. Here, we explore the potential of the GatorEye drone-lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables? relationship to aboveground biomass (AGB) stocks and species diversity. In the Caribbean lowlands of northeastern Costa Rica, we analyze nine tropical forests stands (seven... |
| Tipo: Artigo de periódico |
Palavras-chave: Drone; GatorEye; Aerial surveys; Regeneração florestal; Biomassa aérea; Bosques lluviosos; Monitoreo; Restauración de bosques; Bosques secundarios; Biomasa aérea; Teledetección; Vehículos aéreos no tripulados; Sarapiquí; Heredia Province; Caribbean lowlands; Northeastern Costa Rica; Floresta Tropical; Reconhecimento Florestal; Floresta Secundaria; Sensoriamento Remoto; Raio Laser; Rain forests; Monitoring; Forest restoration; Secondary forests; Aboveground biomass; Remote sensing; Unmanned aerial vehicles; Lidar. |
| Ano: 2020 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1124130 |
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| PAPA, D. de A.; ALMEIDA, D. R. A. de; SILVA, C. A.; FIGUEIREDO, E. O.; STARK, S. C.; VALBUENA, R.; RODRIGUEZ, L. C. E.; OLIVEIRA, M. V. N. d'. |
| In high biodiversity areas, such as the Amazon, forest inventory is a challenge due to large variations in vegetation structure and inaccessibility. Capturing the full gradient of variability requires the acquisition of a large number of sample plots. Pre-stratified inventory is an efficient strategy that reduces sampling effort and cost. Low-cost remote sensing techniques may significantly expand pre-stratification capacity; however, the simplest option, satellite optical imagery, cannot detect small variations in primary forests. Alternatively, three-dimensional information obtained from airborne laser scanning (ALS, a.k.a. airborne lidar) has been successfully used to estimate structural parameters in tropical forests. Our objective was to assess to... |
| Tipo: Artigo de periódico |
Palavras-chave: Manejo florestal; Field forest inventory; Filed sampling; Amostragem de campo; Características de plantas; Cubierta forestal; Espacios vacíos en el dosel; Índice de área foliar; Análisis de conglomerados; Análisis estadístico; Embrapa Acre; Rio Branco (AC); Acre; Amazônia Ocidental; Western Amazon; Amazonia Occidental; Administração Florestal; Floresta Tropical; Inventário Florestal; Amostragem; População de Planta; Sensoriamento Remoto; Raio Laser; Estrutura Vegetal; Campo Experimental; Análise Estatística; Tropical forests; Forest management; Plant characteristics; Remote sensing; Lidar; Forest canopy; Canopy gaps; Leaf area index; Cluster analysis; Statistical analysis. |
| Ano: 2019 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1115167 |
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| SCHEPASCHENKO, D.; CHAVE, J.; PHILLIPS, O. L.; LEWIS, S. L.; DAVIES, S. J.; RÉJOU-MÉCHAIN, M.; SIST, P.; SCIPAL, K.; PERGER, C.; HERAULT, B.; LABRIÈRE, N.; HOFHANSL, F.; AFFUM-BAFFOE, K.; ALEINIKOV, A.; ALONSO, A.; AMANI, C.; ARAUJO-MURAKAMI, A.; ARMSTON, J.; ARROYO, L.; ASCARRUNZ, N.; AZEVEDO, C. P. de; BAKER, T.; BALAZY, R.; BEDEAU, C.; BERRY, N.; BILOUS, A. M.; BILOUS, S. Y.; BISSIENGOU, P.; BLANC, L.; BOBKOVA, K. S.; BRASLAVSKAYA, T.; BRIENEN, R.; BURSLEM, D. F. R. P.; CONDIT, R.; CUNI-SANCHEZ, A.; DANILINA, D.; TORRES, D. del C.; DERROIRE, G.; DESCROIX, L.; SOTTA, E. D.; OLIVEIRA, M. V. N. d'; DRESEL, C.; ERWIN, T.; EVDOKIMENKO, M. D.; FALCK, J.; FELDPAUSCH, T. R.; FOLI, E. G.; FOSTER, R.; FRITZ, S.; GARCIA-ABRIL, A. D.; GORNOV, A.; GORNOVA, M.; GOTHARD-BASSÉBÉ, E.; GOURLET-FLEURY, S.; GUEDES, M. C.; HAMER, K. C.; SUSANTY, F. H.; HIGUCHI, N.; CORONADO, E. N. H.; HUBAU, W.; HUBBELL, S.; ILSTEDT, U.; IVANOV, V. V.; KANASHIRO, M.; KARLSSON, A.; KARMINOV, V. N.; KILLEEN, T.; KOFFI, J. C. K.; KONOVALOVA, M.; KRAXNER, F.; KREJZA, J.; KRISNAWATI, H.; KRIVOBOKOV, L. V.; KUZNETSOV, M. A.; VERHOVETS, S. V.; WEST, T. A. P.; WOELL, H.; WOODS, J. T.; WORTEL, V.; YAMADA, T.; HAJAR, Z. S. N.; ZO-BI, I. C.; RUSCHEL, A. R.; OLIVEIRA, L. C. de; MONTEAGUDO MENDONZA, A.; FREITAS, L. J. M. de; VASQUEZ MARTINEZ, R.; VALENZUELA GAMARRA, L.; TER STEEGE, H.; LAKYDA, I.; LAKYDA, P. I.; LICONA, J. C.; LUCAS, R. M.; LUKINA, N.; LUSSETTI, D.; MALHI, Y.; MANZANERA, J. A.; MARIMON, B.; MARIMON JUNIOR, B. H.; MARTYNENKO, O. V.; MATSALA, M.; MATYASHUK, R. K.; MEMIAGHE, H.; MENDOZA, C.; MOROZIUK, O. V.; MUKHORTOVA, L.; MUSA, S.; NAZIMOVA, D. I.; OKUDA, T.; ONTIKOV, P. V.; OSIPOV, A. F.; PIETSCH, S.; PLAYFAIR, M.; POULSEN, J.; RADCHENKO, V. G.; RODNEY, K.; ROZAK, A. H.; RUTISHAUSER, E.; SEE, L.; SHCHEPASHCHENKO, M.; SHEVCHENKO, N.; SHVIDENKO, A.; SILVEIRA, M.; SINGH, J.; SONKÉ, B.; SOUZA, C. R. de; STERENCZAK, K.; STONOZHENKO, L.; SULLIVAN, M. J. P.; SZATNIEWSKA, J.; TAEDOUMG, H.; TIKHONOVA, E.; TOLEDO, M.; TREFILOVA, O. V.; VALBUENA, R.; VASILIEV, S.; VEDROVA, E. F.; VIDAL, E.; VLADIMIROVA, N. A.; VLEMINCKX, J.; VOS, V. A.; VOZMITEL, F. K.; WANEK, W.. |
| Forest biomass is an essential indicator for monitoring the Earth?s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. |
| Tipo: Artigo de periódico |
Palavras-chave: Biomassa; Sensoriamento Remoto. |
| Ano: 2019 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1113119 |
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