Search

Plant community succession is generally approached with phytosociological methods, but field surveys are time-consuming, expensive, and limited to several of sites. Remote sensing offers an efficient and economical way to analyze vegetation on large extensions and in inaccessible areas. Most studies addressing remote sensing and tree community succession refer to forest physiognomies. We investigated whether structural changes that occur in non-forest physiognomies are identified by multispectral sensor images (OLI-Landsat). Thirteen 0.1-ha plots were set up in Caatinga fragments aging 10-15, 20-25, 30-35, 40-45 and >50 years to calculate the total density of individuals (TD), mean canopy height (H), total basal area (G) and total aboveground biomass (AGB). We performed correlation analyses between these structural descriptors and eight remote sensing variables (reflectance data and spectral indices) obtained from Landsat images at the end of the rainy season and during the dry season. Blue and short-wave infrared reflectances were negatively correlated with mean height, basal area and biomass, regardless of the analyzed scene (coefficients between -0.58 and -0.79). The litter layer (a non-photosynthetic vegetation component) and the soil exposure are important factors influencing the spectral data.

The surging demand for commodity crops has led to rapid and severe agricultural frontier expansion globally and has put producing regions increasingly under pressure. However, knowledge about spatial patterns of agricultural frontier dynamics, their leading spatial determinants, and socio-ecological trade-offs is often lacking, hindering contextualized decision making towards more sustainable food systems. Here, we used inventory data to map frontier dynamics of avocado production, a cash crop of increasing importance in global diets, for Michoacán, Mexico, before and after the implementation of the North American Free Trade Agreement (NAFTA). We compiled a set of environmental, accessibility and social variables and identified the leading determinants of avocado frontier expansion and their interactions using extreme gradient boosting. We predicted potential expansion patterns and assessed their impacts on areas important for biodiversity conservation. Avocado frontiers expanded more than tenfold from 12,909 ha (1974) to 152,493 ha (2011), particularly after NAFTA. Annual precipitation, distance to settlements, and land tenure were key factors explaining avocado expansion. Under favorable climatic and accessibility conditions, most avocado expansion occurred on private lands. Contrary, under suboptimal conditions, most avocado expansion occurred on communal lands. Large areas suitable for further avocado expansion overlapped with priority sites for restoration, highlighting an imminent conflict between conservation and economic revenues. This is the first analysis of avocado frontier dynamics and their spatial determinants across a major production region and our results provide entry points to implement government-based strategies to support small-scale farmers, mostly those on communal lands, while trying to minimize the socio-environmental impacts of avocado production., Supplementary Information: The online version contains supplementary material available at 10.1007/s10113-022-01883-6., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.)

In recent history, Coronavirus Disease 2019 (COVID-19) is one of the worst infectious disease outbreaks affecting humanity. The World Health Organization has defined the outbreak of COVID-19 as a pandemic, and the massive growth of the number of infected cases in a short time has caused enormous pressure on medical systems. Mexico surpassed 3.7 million confirmed infections and 285,000 deaths on October 23, 2021. We analysed the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We computed weekly Moran's I index to assess spatial autocorrelation over time and identify clusters of the disease using the "flexibly shaped spatial scan" approach. Finally, we compared Euclidean, cost, resistance distances and gravitational model to select the best-suited approach to predict inter-municipality contagion. We found that COVID-19 pandemic in Mexico is characterised by clusters evolving in space and time as parallel epidemics. The gravitational distance was the best model to predict newly infected municipalities though the predictive power was relatively low and varied over time. This study helps us understand the spread of the epidemic over the Mexican territory and gives insights to model and predict the epidemic behaviour., Competing Interests: The authors declare that they have no competing interests., (© 2021 Mas and Pérez-Vega.)

Our understanding of how COVID-19 spreads over a territory needs to be improved. For example, the evaluation of disease spatiotemporal distribution and its association with other characteristics can help identify covariates, model the behavior of the epidemic, and provide useful information for decision making. Data were compiled from the National Population Council (CONAPO), Google, the National Institute of Statistics and Geography (INEGI), and the Secretary of Health. The data describe the cases of COVID and characteristics of the population, such as distribution, mobility, and prevalence of chronic diseases such as diabetes, hypertension, and obesity. These data were processed to be compatible and georeferenced to a common geographic framework to facilitate spatial analysis in a geographic information system (GIS)., Competing Interests: I have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Published by Elsevier Inc.)

In this stage 1 registered report, we propose an analysis of the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We will compute weekly Moran index to assess spatial autocorrelation over time and identify clusters of the disease using the "flexibly shaped spatial scan" approach. Finally, different distance models will be compared to select the best suited to predict inter-municipality contagion. This study will help us understand the spread of the epidemic over the Mexican territory and give insights to model and predict the epidemic behavior., Competing Interests: The author declares that they have no competing interests., (© 2021 Mas.)

Background: Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates., Results: Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R 2  = 0.44 vs R 2  = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R 2  = 0.26)., Conclusions: This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

It has been suggested that traditional ecological knowledge (TEK) may play a key role in forest conservation. However, empirical studies assessing to what extent TEK is associated with forest conservation compared with other variables are rare. Furthermore, to our knowledge, the spatial overlap of TEK and forest conservation has not been evaluated at fine scales. In this paper, we address both issues through a case study with Tsimane' Amerindians in the Bolivian Amazon. We sampled 624 households across 59 villages to estimate TEK and used remote sensing data to assess forest conservation. We ran statistical and spatial analyses to evaluate whether TEK was associated and spatially overlapped with forest conservation at the village level. We find that Tsimane' TEK is significantly and positively associated with forest conservation although acculturation variables bear stronger and negative associations with forest conservation. We also find a very significant spatial overlap between levels of Tsimane' TEK and forest conservation. We discuss the potential reasons underpinning our results, which provide insights that may be useful for informing policies in the realms of development, conservation, and climate. We posit that the protection of indigenous cultural systems is vital and urgent to create more effective policies in such realms.

Many studies are based on the assumption that an area and its surrounding (buffer) area present similar environmental conditions and can be compared. For example, in order to assess the effectiveness of a protected area, the land use/cover changes are compared inside the park with its surroundings. However, the heterogeneity in spatial variables can bias this assessment: we have shown that most of the protected areas in Mexico present significant environmental differences between their interior and their surroundings. Therefore, a comparison that aims at assessing the effectiveness of conservation strategies, must be cautioned. In this paper, a simple method which allows the generation of a buffer area that presents similar conditions with respect to a set of environmental variables is presented. The method was used in order to assess the effectiveness of the Calakmul Biosphere Reserve, a protected area located in the south-eastern part of Mexico. The annual rate of deforestation inside the protected area, the standard buffer area (based upon distance from the protected area only) and the similar buffer area (taking into account distance along with some environmental variables) were 0.3, 1.3 and 0.6%, respectively. These results showed that the protected area was effective in preventing land clearing, but that the comparison with the standard buffer area gave an over-optimistic vision of its effectiveness.

Unlabelled: One of the major limitations of gamma cameras is their relatively poor energy resolution. The main practical consequence of this is that the detection of both scattered and unscattered photons in the photopeak energy window, affecting image contrast and resolution, makes the data inconsistent with the assumption of scatter-free projection data in reconstruction and attenuation correction algorithms. Here, we proposed a method to improve the effective energy resolution of scintigraphic acquisitions. This method is called photon energy recovery (PER)., Methods: Photon energy recovery is based on a spectral deconvolution analysis and uses iterative recurrent linear regressions. In practice, PER only required splitting the photopeak energy window into several subwindows and did not need list mode acquisitions. The method was fully automated. Photon energy recovery was quantitatively validated on 99mTc planar images using a Monte Carlo simulation and a real phantom and was illustrated by a bone study., Results: The Monte Carlo simulation demonstrated that convergence was reached within relatively few (10-15) iterations. Photon energy recovery led to a considerable quantitative improvement because the mean error between the photopeak energy window image and the true unscattered image was equal to 8.72 s.d. (the mean error between one image and the true image was the mean of the differences between the two images; the difference is expressed as several s.d., where s.d. was the square root of the true value), whereas the mean error between the 140-keV PER image and the true unscattered image was only equal to 2.70. Moreover, the true and PER spectra were highly correlated. The real phantom data pointed out that the counts in the 140-keV PER image calculated from the images acquired "with scatter" were not very different from the true counts given by the "scatter-free" reference image. Planar pelvic bone scintigraphy demonstrated the advantages of PER because contrast increased when only unscattered photons were selected., Conclusion: Photon energy recovery is a stable and automated method that allows recovery of the correct value of the photon energy after a scintigraphic acquisition. Its ability to separate scattered from unscattered events has been quantitatively validated.