Marco A. González-Tagle

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k -nearest neighbor strategy in North Central Mexico

As climate change negotiations progress, monitoring biomass and carbon stocks is becoming an important part of the current forest research. Therefore, national governments are interested in developing forest-monitoring strategies using geospatial technology. Among statistical methods for mapping biomass, there is a nonparametric approach called k-nearest neighbor (kNN). We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone. Satellite derived, climatic, and topographic predictor variables were combined with the Mexican National Forest Inventory (NFI) data to accomplish the purpose. Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique. The results indicate that the Most Similar Neighbor (MSN) approach maximizes the correlation between predictor and response variables (r=0.9). Our results are in agreement with those reported in the literature. These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation (REDD+).

Modeling susceptibility to deforestation of remaining ecosystems in North Central Mexico with logistic regression

Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems (shrubland, temperate forest and rainforest) was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993–2007. Logistic regression was used to relate explanatory variables (such as social, investment, forest production, biophysical and proximity factors) with susceptibility to deforestation to construct predictive models with two focuses: general and by biogeographical zone. In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographical zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmosphere. However, after some considerations, encouraging forest environmental services appears to be the best alternative to achieve sustainable forest management.

Forest Cover Mapping in North-Central Mexico: A Comparison of Digital Image Processing Methods

Vegetation type is an environmental attribute that varies across the landscape and over time. Its continuous assessment is important for monitoring land use changes and forest degradation. There are advanced methods that can estimate the fractional cover of vegetation types within each pixel. This paper compares some methods for subpixel mapping of forest cover in the state of San Luis Potosí, Mexico, using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spectral data (MCD43A4). Three methods were tested: (1) Bayesian posterior probability, (2) the Fuzzy k nearest neighbor (FkNN), and (3) linear spectral mixture analysis (LSMA). While the Bayesian approach gave the poorest correlations, FkNN (r = 0.78) and LSMA (r = 0.81) estimations were successfully validated with information obtained from a Landsat image. This paper represents an interesting attempt to compare rarely reported FkNN with traditional approaches such as LSMA and the Bayesian one.

Acclimatation of three co-occurring tree species to water stress and their role as site indicators in mixed pine-oak forests in the Sierra Madre Oriental, Mexico

Water availability and salt excess are limiting factors in Mexican mixed pine-oak forest. In order to characterise the acclimatation of native species to these stresses, leaf water (Ψw) and osmotic potentials (Ψs) of Juniperus flaccida, Pinus pseudostrobus and Quercus canbyi were measured under natural drought and non-drought conditions under two different aspects in the Sierra Madre Oriental. Factorial ANOVA revealed significant differences in Ψw and Ψs between two aspects, species and sampling dates. In general, all species showed high predawn and low midday values that declined progressively with increasing drought and soil–water loss. Seasonal and diurnal fluctuation of Ψw and Ψs were higher for J. flaccida and Q. canbyi than for P. pseudostrobus. Leaf Ψw and Ψs were mainly correlated with soil water content, while Ψs of P. pseudostrobus were hardly correlated with environmental variables. Thus, species have different strategies to withstand drought.P. pseudostrobus was identified as a species with isohydric water status regulation, while J. flaccida and Q. canbyi presented water potential patterns typical for anisohydric species. The type of water status regulation may be a critical factor for plant survival and mortality in the context of climate change. Nevertheless, for precise conclusions about the advantages and disadvantages of each type, further long-term investigations are required.

Construction of aboveground biomass models with remote sensing technology in the intertropical zone in Mexico

Spatially-explicit estimation of aboveground biomass (AGB) plays an important role to generate action policies focused in climate change mitigation, since carbon (C) retained in the biomass is vital for regulating Earth’s temperature. This work estimates AGB using both chlorophyll (red, near infrared) and moisture (middle infrared) based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer (MODIS) and MOD44B vegetation continuous fields (VCF) data. The study area is located in San Luis Potosí, Mexico, a region that comprises a part of the upper limit of the intertropical zone. AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature. Linear and nonlinear (exponential) models were fitted to find their predictive potential when using satellite spectral data as explanatory variables. Highly-significant correlations (p = 0.01) were found between all the explaining variables tested. NDVI62, linked to chlorophyll content and moisture stress, showed the highest correlation. The best model (nonlinear) showed an index of fit (Pseudo — r2) equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables. Validation correlation coefficients were similar for both models: linear (r = 0.87**) and nonlinear (r = 0.86**).