Radarsat-2 Backscattering for the Modeling of Biophysical Parameters of Regenerating Mangrove Forests

Cougo, Michele Ferreira; Souza-Filho, Pedro Walfir M.; Silva, Arnaldo Q.; Fernandes, Marcus Emanuel Barroncas; Santos, João Roberto dos; Abreu, Maria R. S.; Nascimento, Wilson R.; Simard, Marc

doi:10.3390/rs71215873

Cited by 39 publications

(24 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Satellite remote-sensing, as an important data source, has been widely used to estimate plant AGB at the landscape scale. There are two main kinds satellite remote-sensing data used to estimate AGB, namely optical imagery [14-16] and synthetic aperture radar data [17][18][19], providing different spatial resolutions, such as SPOT (20 m) and MODIS (1 km), and Sentinel 1A (10 m) and ALOS-2 PALSAR2 (6 m). However, crop monitoring applications at the farm scale are highly demanding with respect to temporal frequency (1-3 days revisit), while spatial resolution requirements are driven by the minimum management unit that can be in the range of 1-10 m [20,21].…”

mentioning

confidence: 99%

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

et al. 2019

View full text Add to dashboard Cite

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

show abstract

mentioning

confidence: 99%

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In mangroves specifically, canopy height, D 130, and above‐ground biomass were predicted using remote sensing, which could represent great opportunities to estimate biomass in a non‐destructive way (Fatoyinbo and Simard , Cougo et al. ).…”

Section: Discussionmentioning

confidence: 99%

Inter‐ and intraspecific variation in mangrove carbon fraction and wood specific gravity in Gazi Bay, Kenya

et al. 2018

View full text Add to dashboard Cite

Abstract. The tropical mangrove ecosystem harbors great potential for carbon offsetting schemes because of their exceptionally high carbon sequestration potential. These cannot only generate an income for local communities by financially compensating for the non-exploitation of protected or replanted stands, but also simultaneously reduce emissions due to forest degradation and deforestation, thereby helping to counteract the global threat on these forests. As carbon is directly monetized through offsetting carbon emissions, accurate species-specific estimation of carbon content in trees is essential and reduces the propagation of errors generated by accounting uncertainties. Accordingly, this study assessed variation in both carbon fraction and wood specific gravity among and within the 10 mangrove species occurring in Gazi Bay, Kenya. Significant interspecific differences were found with values ranging from 45.8% (Avicennia marina) to 49.8% (Ceriops tagal) for carbon fraction and from 0.58 (Sonneratia alba) to 0.93 (Pemphis acidula) for wood specific gravity. The influence of environmental factors (soil salinity, stand density, and elevation a.s.l. as a proxy for multiple interrelated conditions) was investigated, but only elevation a.s.l. appeared to have a species-specific though moderate influence only on carbon fraction. Significant differences in carbon fraction and wood specific gravity were found between stem, aerial roots, and branches of Rhizophora mucronata. In contrast, no significant differences in carbon fraction were found among different stem tissues of A. marina, C. tagal and R. mucronata, the Bay's most abundant species. These results provide insight in carbon content variation, confirming the importance of considering species-specific or even sitespecific values of carbon fraction and wood specific gravity, thereby rendering future carbon accounting more accurate.

show abstract

“…Florestas de mangue em regiões equatoriais apresentam elevada produtividade, decorrente principalmente das condições meteorológicas, como altas taxas de precipitação pluviométrica, radiação solar e tempe-ratura. A produção de serapilheira destaca-se como importante componente dessa produtividade do manguezal (KAMRUZZAMAN et al, 2019;RIBEIRO et al, 2019;COUGO et al, 2015).…”

Section: Introductionunclassified

Influência De Sistemas Precipitantes Sobre a Produção De Serapilheira Em Manguezal Da Costa Amazônica

Souza

Vitorino

Vasconcelos

et al. 2019

Rev. Bras. Ciênc. Ambient. (Online)

View full text Add to dashboard Cite

Manguezais são ecossistemas com significativa relevância ambiental e socioeconômica,e o conhecimento da relação ecológica desse ecossistema com a atmosfera é defundamental importância para sua preservação e para o uso sustentável dos recursosnaturais. Diante disso, este estudo tem como objetivos analisar os sistemas meteorológicosprecipitantes e sua variação pluviométrica sazonal sobre a costa amazônica, bem comoexaminar sua influência na variabilidade da produção de serapilheira de floresta demangue. Delimitaram-se três parcelas amostrais com 12 coletores de serapilheira de 1 m2no manguezal do Sítio Experimental de Cuiarana. Classificaram-se as coletas realizadasmensalmente em folha, lenhoso, material reprodutivo e miscelânea. Utilizaram-sedados de precipitação pluvial com base na Torre Micrometeorológica e na técnicaCPC MORPHing Technique (CMORPH). Utilizaram-se as análises two-way e one-wayde variância (ANOVA) (p < 0,05) e análise de regressão linear (p < 0,05). Os resultadosindicam que a variação temporal da precipitação é modulada por diferentes sistemasmeteorológicos. A produção total anual de serapilheira foi de 9,4 ± 0,06 Mg ha-1 ano-1,da qual 67% compunha-se de fração folha. As análises da fração folha total e folha daespécie L. racemosa apresentaram correlação significativa com a precipitação (r2 = 0,36e r2 = 0,72; p < 0,05), respectivamente. Diante dos resultados, conclui-se que a produçãode serapilheira do manguezal possui direta relação com os sistemas precipitantes.

show abstract

Radarsat-2 Backscattering for the Modeling of Biophysical Parameters of Regenerating Mangrove Forests

Cited by 39 publications

References 39 publications

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

Inter‐ and intraspecific variation in mangrove carbon fraction and wood specific gravity in Gazi Bay, Kenya

Influência De Sistemas Precipitantes Sobre a Produção De Serapilheira Em Manguezal Da Costa Amazônica

Contact Info

Product

Resources

About