Near-real time aboveground carbon emissions in Peru

Csillik, Ovidiu; Asner, Gregory P.

doi:10.1371/journal.pone.0241418

Cited by 13 publications

(7 citation statements)

References 56 publications

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“…Higher resolution imagery, including aerial lidar and commercial SmallSat platforms, can enable early detection of environmental changes caused by gold mining, such as the loss of aboveground carbon (Csillik et al., 2019 ). Near real‐time alert systems for deforestation based on these remote sensing platforms are now feasible, with the potential to aid policies that seek to limit mining incursion in protected areas (Csillik & Asner, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Detecting gold mining impacts on insect biodiversity in a tropical mining frontier with SmallSat imagery

Stoll

Roopsind

Maharaj

et al. 2022

Remote Sens Ecol Conserv

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Gold mining is a major driver of Amazonian forest loss and degradation. As mining activity encroaches on primary forest in remote and inaccessible areas, satellite imagery provides crucial data for monitoring mining‐related deforestation. High‐resolution imagery, in particular, has shown promise for detecting artisanal gold mining at the forest frontier. An important next step will be to establish relationships between satellite‐derived land cover change and biodiversity impacts of gold mining. In this study, we set out to detect artisanal gold mining using high‐resolution imagery and relate mining land cover to insects, a taxonomic group that accounts for the majority of faunal biodiversity in tropical forests. We applied an object‐based image analysis (OBIA) to classify mined areas in an Indigenous territory in Guyana, using PlanetScope imagery with ~3.7 m resolution. We complemented our OBIA with field surveys of insect family presence or absence in field plots (n = 105) that captured a wide range of mining disturbances. Our OBIA was able to identify mined objects with high accuracy (>90% balanced accuracy). Field plots with a higher proportion of OBIA‐derived mine cover had significantly lower insect family richness. The effects of mine cover on individual insect taxa were highly variable. Insect groups that respond strongly to mining disturbance could potentially serve as bioindicators for monitoring ecosystem health during and after gold mining. With the advent of global partnerships that provide universal access to PlanetScope imagery for tropical forest monitoring, our approach represents a low‐cost and rapid way to assess the biodiversity impacts of gold mining in remote landscapes.

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Section: Introductionmentioning

confidence: 99%

Detecting gold mining impacts on insect biodiversity in a tropical mining frontier with SmallSat imagery

Stoll

Roopsind

Maharaj

et al. 2022

Remote Sens Ecol Conserv

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“…The PlanetScope satellite constellation enables near-daily monitoring with multi-spectral imagery at high spatial resolution (3 m) [1]. PlanetScope imagery has been applied to a variety of studies to monitor phenomena that require both high spatial and temporal resolution, for instance, to monitor small water bodies [2][3][4], estimate methane emissions from forested wetlands [5], assess river-ice and water velocity [6], improve crop leaf-area-index estimation with sensor data fusion [7][8][9], and monitor near-real-time aboveground carbon emissions from tropical forests [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…For example, for a given period of interest-Basemap can be processed using different image cadences, e.g., daily, weekly, biweekly-PlanetScope images are ranked based on these metrics such that cloud-free images have higher scores than cloudy images [16,18]. Basemap is designed to monitor changes over time and for analytics-driven use cases, and it has been applied to several research projects, including monitoring of forest biomass [10][11][12], to assess carbon emissions from drainage canals [19], and to monitor coral reef map probabilities [20]. Planet Fusion, on the other hand, is based on the CubeSatenabled spatiotemporal enhancement method [8], and it leverages the high spatial and temporal resolution provided by PlanetScope scenes with rigorously calibrated publicly available multispectral satellites (i.e., Sentinel-2, Landsat, MODIS, and VIIRS) to provide daily and radiometrically consistent and gap-filled surface-reflectance images that are free of clouds and shadows [17].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets

et al. 2021

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Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r2 ≥ 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 ≥ 0.55, and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs.

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“…They can also provide estimates of national AGB over time, and its uncertainty, to assist carbon accounting based on NFI sampling (McRoberts & Tomppo, 2007) and to enhance local AGB estimates (Naesset et al, 2020;Toan et al, 2011). In addition, they can provide baseline AGB values when more frequent estimation of carbon emissions is desirable (Csillik & Asner, 2020).…”

Section: Strengths and Limitations Of The Frameworkmentioning

confidence: 99%

“…Biomass estimation for the tropical moist forests is based on ALOS-2 PALSAR-2 L-band satellite and its usage needs to account for the local biases, especially underestimating AGB values higher than 250 Mg/ha( ). Although we reduced this underestimation by adjusting the AGB map based on ground field data, more research is needed on providing up-to-date high-resolution aboveground carbon estimates (Csillik & Asner, 2020) that could further increase the accuracy of local carbon loss estimation. Radar-based estimation of forest carbon stocks is challenging over mountainous terrain and is less accurate in complex canopies (Gibbs et al, 2007) and future integration of radar and optical satellite data will provide more robust estimates (Csillik & Asner, 2020).…”

Section: Limitations and Future Improvementsmentioning

confidence: 99%

Assessment, uncertainties and applications of global Above-ground biomass maps from earth observation

Araza

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Chapter 2 A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps Chapter 3 Past decade above-ground biomass change comparisons from four multi-temporal global maps Chapter 4 Spatial predictions and uncertainties of forest carbon fluxes for environmental-economic accounting Chapter 5 Rapid remote monitoring reveals spatial and temporal hotspots of carbon loss in Africa's rainforests Chapter 6 Synthesis

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Near-real time aboveground carbon emissions in Peru

Cited by 13 publications

References 56 publications

Detecting gold mining impacts on insect biodiversity in a tropical mining frontier with SmallSat imagery

Detecting gold mining impacts on insect biodiversity in a tropical mining frontier with SmallSat imagery

Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets

Assessment, uncertainties and applications of global Above-ground biomass maps from earth observation

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