Mitigating the effects of omission errors on area and area change estimates

Information on Earth's land surface and change over time has never been easier to obtain, but making informed decisions to manage land well necessitates that this information is accurate and precise. In recent years, due largely to the inevitability of classification errors in remote sensing-based maps and the marked effects of these errors on subsequent area estimates, sample-based area estimates of land cover and land change have increased in importance and use. Area estimation of land cover and change by sampling is often made more efficient by a priori knowledge of the study area to be analyzed (e.g., stratification). Satellite data, obtained free of cost for virtually all of Earth's land surface, provide an excellent source for constructing landscape stratifications in the form of maps. Errors of omission, defined as sample units observed as land change but mapped as a stable class, may introduce considerable uncertainty in parameter estimates obtained from the sample data (e.g., area estimates of land change). The effects of omission errors are exacerbated in situations where the area of intact forest is large relative to the area of forest change, a common situation in countries that seek results-based payments for reductions in deforestation and associated carbon emissions. The presence of omission errors in such situations can preclude the acquisition of statistically valid evidence of a reduction in deforestation, and thus prevent payments. International donors and countries concerned with mitigating the effects of climate change are looking for guidance on how to reduce the effects of omission errors on area estimates of land change. This article presents the underlying reasons for the effects of omission errors on area estimates, case studies highlighting real-world examples of these effects, and proposes potential solutions. Practicable approaches to efficiently splitting large stable strata are presented that may reduce the effects of omission errors and immediately improve the quality of estimates. However, more research is needed before further recommendations can be provided on how to contain, mitigate and potentially eliminate the effects of omissions errors. © 2019 Elsevier Inc.This research was funded by support from the NASA Carbon Monitoring System ( NNX16AP26G ) and USGS/SilvaCarbon to Boston University (PI Pontus Olofsson). M.J. Sanz was supported by the Spanish Government through María de Maeztu excellence accreditation MDM-2017-0714

Mechanism Design in Social Networks

This paper studies an auction design problem for a seller to sell a commodity in a social network, where each individual (the seller or a buyer) can only communicate with her neighbors. The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue. We propose a novel auction mechanism, called information diffusion mechanism (IDM), which incentivizes the buyers to not only truthfully report their valuations on the commodity to the seller, but also further propagate the auction information to all their neighbors. In comparison, the direct extension of the well-known Vickrey-Clarke-Groves (VCG) mechanism in social networks can also incentivize the information diffusion, but it will decrease the seller's revenue or even lead to a deficit sometimes. The formalization of the problem has not yet been addressed in the literature of mechanism design and our solution is very significant in the presence of large-scale online social networks.Comment: In The Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, US, 04-09 Feb 201

A practical measure for determining if diameter (D) and height (H) should be combined into D2H in allometric biomass models

Tree diameter at breast height (D) and tree height (H) are often used as predictors of individual tree biomass. Because D and H are correlated, the combined variable D2H is frequently used in regression models instead of two separate independent variables, to avoid collinearity related issues. The justification for D2H is that aboveground biomass is proportional to the volume of a cylinder of diameter, D, and height, H. However, the D2H predictor constrains the model to produce parameter estimates for D and H that have a fixed ratio, in this case, 2.0. In this paper we investigate the degree to which the D2H predictor reduces prediction accuracy relative to D and H separately and propose a practical measure, Q-ratio, to guide the decision as to whether D and H should or should not be combined into D2H. Using five training biomass datasets and two fitting approaches, weighted nonlinear regression and linear regression following logarithmic transformations, we showed that the D2H predictor becomes less efficient in predicting aboveground biomass as the Q-ratio deviates from 2.0. Because of the model constraint, the D2H-based model performed less well than the separate variable model by as much as 12 per cent with regard to mean absolute percentage residual and as much as 18 per cent with regard to sum of squares of log accuracy ratios. For the analysed datasets, we observed a wide variation in Q-ratios, ranging from 2.5 to 5.1, and a large decrease in efficiency for the combined variable model. Therefore, we recommend using the Q-ratio as a measure to guide the decision as to whether D and H may be combined further into D2H without the adverse effects of loss in biomass prediction accuracy

Near-real time forest change detection using PlanetScope imagery

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. To combat global deforestation, monitoring forest disturbances at sub-annual scales is a key challenge. For this purpose, the new Planetscope nano-satellite constellation is a game changer, with a revisit time of 1 day and a pixel size of 3-m. We present a near-real time forest disturbance alert system based on PlanetScope imagery: the Thresholding Rewards and Penances algorithm (TRP). It produces a new forest change map as soon as a new PlanetScope image is acquired. To calibrate and validate TRP, a reference set was constructed as a complete census of five randomly selected study areas in Tuscany, Italy. We processed 572 PlanetScope images acquired between 1 May 2018 and 5 July 2019. TRP was used to construct forest change maps during the study period for which the final user’s accuracy was 86% and the final producer’s accuracy was 92%. In addition, we estimated the forest change area using an unbiased stratified estimator that can be used with a small sample of reference data. The 95% confidence interval for the sample-based estimate of 56.89 ha included the census-based area estimate of 56.19 ha.s

Quantifying MCPA load pathways at catchment scale using high temporal resolution data

Publication history: Accepted - 21 May 2022; Published online - 24 May 2022.Detection of the agricultural acid herbicide MCPA (2-methyl-4-chlorophenoxyacetic acid) in drinking water source catchments is of growing concern, with economic and environmental implications for water utilities and wider ecosystem services. MCPA is poorly adsorbed to soil and highly mobile in water, but hydrological pathway processes are relatively unknown at the catchment scale and limited by coarse resolution data. This understanding is required to target mitigation measures and to provide a framework to monitor their effectiveness. To address this knowledge gap, this study reports findings from river discharge and synchronous MCPA concentration datasets (continuous 7 hour and with additional hourly sampling during storm events) collected over a 7 month herbicide spraying season. The study was undertaken in a surface (source) water catchment (384 km2—of which 154 km2 is agricultural land use) in the cross-border area of Ireland. Combined into loads, and using two pathway separation techniques, the MCPA data were apportioned into event and baseload components and the former was further separated to quantify a quickflow (QF) and other event pathways. Based on the 7 hourly dataset, 85.2 kg (0.22 kg km 2 by catchment area, or 0.55 kg km 2 by agricultural area) of MCPA was exported from the catchment in 7 months. Of this load, 87.7 % was transported via event flow pathways with 72.0 % transported via surface dominated (QF) pathways. Approximately 12 % of the MCPA load was transported via deep baseflows, indicating a persistence in this delayed pathway, and this was the primary pathway condition monitored in a weekly regulatory sampling programme. However, overall, the data indicated a dominant acute, storm dependent process of incidental MCPA loss during the spraying season. Reducing use and/or implementing extensive surface pathway disconnection measures are the mitigation options with greatest potential, the success of which can only be assessed using high temporal resolution monitoring techniques.This work was carried out as part of Source to Tap (IVA5018), a project supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB)

Accommodating heteroscedasticity in allometric biomass models

Allometric models are commonly used to predict forest biomass. These models typically take nonlinear power-law forms that predict individual tree aboveground biomass (AGB) as functions of diameter at breast height (D) and/or tree height (H). Because the residual variance is in most cases heteroscedastic, accommodating the heteroscedasticity (i.e., heterogeneity of variance) becomes necessary when estimating model parameters. We tested several weighting procedures and a logarithmic transformation for nonlinear allometric biomass models. We further evaluated the effectiveness of these procedures with emphasis on how they affected estimates of mean AGB per hectare and their standard errors for large forest areas. Our results revealed that some weighting procedures were more effective for accommodating heteroscedasticity than others and that effectiveness was greater for single predictor models but less for models based on both D and H. Failing to effectively accommodate heteroscedasticity produced small to moderate differences in the estimates of mean AGB per hectare and their standard errors. However, these differences were greater between model forms (models based on D and H versus models based on D only), regardless of the weighting approach. Similar consequences were observed with respect to whether model prediction uncertainty was or was not included when estimating mean AGB per hectare and standard errors. When including model prediction uncertainty, the standard errors of the estimated means increased substantially, by 44-59%. Therefore, to avoid possible negative consequences on large-area biomass estimation, we recommend three steps: (i) testing the effectiveness of a weighting procedure when accommodating heteroscedasticity in allometric biomass models, (ii) incorporating model prediction uncertainty in the total uncertainty estimate and (iii) including H as an additional predictor variable in allometric biomass models

Nutrient status of cattle grazing systems in the western brazilian Amazon.

Low-input cultivated pastures to feed cattle have dominated land use after forest clearing for decades in the western Brazilian Amazon. This study was undertaken to help understand the inherent nutrient supply dynamics underwriting cattle performance on three farms in the state of Acre. We assessed soil chemical and physical properties associated over time with different land uses following forest clearing. This information permitted specifying a conceptual model of nutrient stocks and flows under the observed grazing system, which produced insights about the dynamics of soil nutrient degradation. Above ground forage mass, topsoil nutrient concentrations and soil bulk density were measured. Land covers were Brachiaria spp. grasses, a grass-Pueraria phaseoloides mix, cropland and forest. Most soil nutrient parameters initially decreased after clearing, gradually recovering over time with grass-only pastures; however, 20 yr-old pastures had 20% less forage mass. Most pasture system nutrients on these farms resided in topsoil and roots, where large stocks of mature forage supported soil fertility with recycled nutrients from litter. Estimates of partial topsoil nutrient balances were negative. This suggested that corresponding nutrient stocks and the accumulation of forage mass were probably maintained primarily through the sum of inflows from cattle excreta, the subsoil, soil organic matter, and litter mineralization with scant input of commercial fertilizer. Therefore, herd management to increase animal system productivity via higher stocking rates on vegetatively younger forage requires monitoring of nutrient stocks and flows and fertilization that assures replenishment of the nutrients extracted. Otherwise, rapid depletion of soil nutrient stocks will lead to system degradation and failure

The Importance of Consistent Global Forest Aboveground Biomass Product Validation

Several upcoming satellite missions have core science requirements to produce data for accurate forest aboveground biomass mapping. Largely because of these mission datasets, the number of available biomass products is expected to greatly increase over the coming decade. Despite the recognized importance of biomass mapping for a wide range of science, policy and management applications, there remains no community accepted standard for satellite-based biomass map validation. The Committee on Earth Observing Satellites (CEOS) is developing a protocol to fill this need in advance of the next generation of biomass-relevant satellites, and this paper presents a review of biomass validation practices from a CEOS perspective. We outline the wide range of anticipated user requirements for product accuracy assessment and provide recommendations for the validation of biomass products. These recommendations include the collection of new, high-quality in situ data and the use of airborne lidar biomass maps as tools toward transparent multi-resolution validation. Adoption of community-vetted validation standards and practices will facilitate the uptake of the next generation of biomass products