Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes

Sano, Edson Eyji; Rizzoli, Paola; Koyama, Christian N.; Adami, Marcos; Shimabukuro, Yosio Edemir; Bayma-Silva, Gustavo; Freitas, Daniel

doi:10.3390/rs13030367

Cited by 13 publications

(10 citation statements)

References 42 publications

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“…Figure 4 shows the dual-pol spectra for 3 spots in the Pantanal wetland of Brazil. A constant Other successful forest applications of ALOS-2 data include forest maps [9].…”

Section: Forest Monitoringmentioning

confidence: 99%

From ALOS-2 to ALOS-4: Japan's pioneering L-band SARs for global vegetation monitoring: state-of-the-art and future perspectives

Koyama,

Shimada,

Ohki

et al. 2023

Sensors, Systems, and Next-Generation Satellites XXVII

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Monitoring of land vegetation is one of the prime objectives for Earth Observation satellite missions. Due to the penetration capabilities of low-frequency radar signals through vegetation canopies, L-band SAR is widely considered as a valuable tool for advanced vegetation monitoring. Based on the physical backscattering properties of the above-ground vegetation strata, polarimetric SAR (PolSAR) imaging can provide detailed information on crucial plant parameters such as amounts of biomass, growth heights, water contents, crop types, etc. 9 years in orbit, ALOS-2 has pioneered as a veritable long-term L-band SAR land observation mission. ALOS-2/PALSAR-2 has acquired unprecedented L-band time-series data with seamless coverage of the entire vegetated land area. Based on the achievements of its predecessor, we discuss the potential of ALOS-4/PALSAR-3 for further breakthroughs in both agriculture and forest monitoring from meter scale to continental scale. Particularly, the cutting-edge capability to observe 200-km swaths in high-resolution Stripmap (SM) mode, achieved by Digital Beam Forming (DBF), will allow overcoming the limitations of ALOS-2’s widely used 50-m resolution ScanSAR modes. Better spatial resolution and image quality paired with higher revisit frequency is expected to improve the reliability of numerous applications ranging from land cover classifications to biomass and yield estimations.

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“…Figure 4 shows the dual-pol spectra for 3 spots in the Pantanal wetland of Brazil. A constant Other successful forest applications of ALOS-2 data include forest maps [9].…”

Section: Forest Monitoringmentioning

confidence: 99%

From ALOS-2 to ALOS-4: Japan's pioneering L-band SARs for global vegetation monitoring: state-of-the-art and future perspectives

Koyama,

Shimada,

Ohki

et al. 2023

Sensors, Systems, and Next-Generation Satellites XXVII

Self Cite

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“…The training set was used to train the RF model and assess the training performance. RF models are commonly used in pixel classification methods, mainly because of their efficiency, simplicity and high performance [38][39][40][41]. The validation set was used for independent validation of the resulting mask.…”

Section: Labeling the Training And Validation Setsmentioning

confidence: 99%

Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

Verhelst

Gou

Herold

et al. 2021

Forests

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Remote Sensing-based global Forest/Non-Forest (FNF) masks have shown large inaccuracies in tropical wetland areas. This limits their applications for deforestation monitoring and alerting in which they are used as a baseline for mapping new deforestation. In radar-based deforestation monitoring, for example, moisture dynamics in unmasked non-forest areas can lead to false detections. We combined a GEDI Forest Height product and Sentinel-1 radar data to improve FNF masks in wetland areas in Gabon using a Random Forest model. The GEDI Forest Height, together with texture metrics derived from Sentinel-1 mean backscatter values, were the most important contributors to the classification. Quantitatively, our mask outperformed existing global FNF masks by increasing the Producer’s Accuracy for the non-forest class by 14%. The GEDI Forest Height product by itself also showed high accuracies but contained Landsat artifacts. Qualitatively, our model was best able to cleanly uncover non-forest areas and mitigate the impact of Landsat artifacts in the GEDI Forest Height product. An advantage of the methodology presented here is that it can be adapted for different application needs by varying the probability threshold of the Random Forest output. This study stresses that, in any application of the suggested methodology, it is important to consider the UA/PA trade-off and the effect it has on the classification. The targeted improvements for wetland forest mapping presented in this paper can help raise the accuracy of tropical deforestation monitoring.

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“…Remote sensing techniques enable the assessment of changes in the landscape from diverse perspectives, therefore they have proved to be successful tools in the study of forest ecosystems: The multispectral analysis is used to detect speci c types of vegetation, their change in distribution according to disturbances or seasonality (Akasheh et (Sano et al, 2021); and photogrammetry (aerial or satellite) is used to model the 3-dimensional structure of the surface (Nolan et al, 2015), the forest canopy height model (Cunliffe et al, 2016;Granholm et al, 2017;Lisein et al, 2013) its changes after a disturbance, or even to estimate the population range (Gallardo-Salazar & Pompa-García, 2020). One or more remote sensing techniques can be chosen according to the study site, the research objectives, and resources, although it is possible to combine them to improve the results in speci c studies.…”

Section: Introductionmentioning

confidence: 99%

Using photogrammetry to assess the recovery of a cypress forest and its impact on water-borne erosion. Case study: Guadalupe Island.

Vera-Ortega,

Hinojosa-Corona,

Luna

et al. 2023

Preprint

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We used photogrammetry to assess and monitor the recovery of the cypress forest on Guadalupe Island, Mexico, an ecosystem impaired by fires and overgrazing. Two drone surveys were conducted over the forest area during the summers of 2016 and 2019 with natural color (RGB) and near infrared (NIR) cameras. It is the first complete 3D reconstruction of the cypress forest on the island. The image process products were the canopy height model (CHM), digital surface model (DSM), and digital terrain model (DTM), which were used to calculate the number, density, and height of the trees. The CHM highly correlates with the forest's structure, R = 0.92, according to field measurements of the trees' heights and geo-position. Results account for ~ 67,340 trees higher than two meters in 2019 and a horizontal expansion of 134 ha from 2016 to 2019. Over 90% of the cypress population were young trees between 2 and 3 m that recovered after an extensive fire in 2008. We developed a novel method to calculate the C cover factor of the Revised Universal Soil Loss Equation (RUSLE) using CHM and DSM obtained by photogrammetry. Modeled erosion rates vary from 0 to 2 Kg m− 2 y− 1 in areas with trees of different ages to values greater than 15 Kg m− 2 y− 1 in areas with steep slopes. According to our results, the derived products from photogrammetry point clouds allow us to describe the vertical structure of the cypress forest with high accuracy and resolution and calculate the cover factor of RUSLE.

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Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes

Cited by 13 publications

References 42 publications

From ALOS-2 to ALOS-4: Japan's pioneering L-band SARs for global vegetation monitoring: state-of-the-art and future perspectives

From ALOS-2 to ALOS-4: Japan's pioneering L-band SARs for global vegetation monitoring: state-of-the-art and future perspectives

Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

Using photogrammetry to assess the recovery of a cypress forest and its impact on water-borne erosion. Case study: Guadalupe Island.

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