Determining aboveground biomass of the forest successional chronosequence in a test-site of Brazilian Amazon through X- and L-band data analysis

2014 ◽  
Author(s):  
João R. Santos ◽  
Camila V. d. J. Silva ◽  
Lênio S. Galvão ◽  
Robert Treuhaft ◽  
José C. Mura ◽  
...  
2016 ◽  
Vol 30 (11) ◽  
pp. 1639-1660 ◽  
Author(s):  
Marcos Longo ◽  
Michael Keller ◽  
Maiza N. dos-Santos ◽  
Veronika Leitold ◽  
Ekena R. Pinagé ◽  
...  

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Graciela Tejada ◽  
Eric Bastos Görgens ◽  
Fernando Del Bon Espírito-Santo ◽  
Roberta Zecchini Cantinho ◽  
Jean Pierre Ometto

2018 ◽  
Vol 24 (2) ◽  
pp. 250-269 ◽  
Author(s):  
João Arthur Pompeu Pavanelli ◽  
João Roberto dos Santos ◽  
Lênio Soares Galvão ◽  
Maristela Xaud ◽  
Haron Abrahim Magalhães Xaud

Abstract: In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah.


2016 ◽  
Vol 8 (6) ◽  
pp. 522 ◽  
Author(s):  
Yifan Yu ◽  
Sassan Saatchi
Keyword(s):  

Author(s):  
Henrique Luis Godinho Cassol ◽  
Luiz E. de O. C. Aragao ◽  
Elisabete Caria Moraes ◽  
Joao Manuel de Brito Carreiras ◽  
Yosio Edemir Shimabukuro

2020 ◽  
Vol 12 (12) ◽  
pp. 2048
Author(s):  
Charlie Marshak ◽  
Marc Simard ◽  
Laura Duncanson ◽  
Carlos Alberto Silva ◽  
Michael Denbina ◽  
...  

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.


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