National Commission for the Knowledge and Use of Biodiversity CONABIO

Mexico City, Mexico

National Commission for the Knowledge and Use of Biodiversity CONABIO

Mexico City, Mexico

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Colditz R.R.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Lopez Saldana G.,University of Lisbon | Maeda P.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Espinoza J.A.,National Institute for Statistics and Geography INEGI | And 5 more authors.
Remote Sensing of Environment | Year: 2012

Land cover plays a key role in global to regional monitoring and modeling because it affects and is being affected by climate change and thus became one of the essential variables for climate change studies. National and international organizations require timely and accurate land cover information for reporting and management actions. The North American Land Change Monitoring System (NALCMS) is an international cooperation of organizations and entities of Canada, the United States, and Mexico to map land cover change of North America's changing environment. This paper presents the methodology to derive the land cover map of Mexico for the year 2005 which was integrated in the NALCMS continental map. Based on a time series of 250. m Moderate Resolution Imaging Spectroradiometer (MODIS) data and an extensive sample data base the complexity of the Mexican landscape required a specific approach to reflect land cover heterogeneity. To estimate the proportion of each land cover class for every pixel several decision tree classifications were combined to obtain class membership maps which were finally converted to a discrete map accompanied by a confidence estimate. The map yielded an overall accuracy of 82.5% (Kappa of 0.79) for pixels with at least 50% map confidence (71.3% of the data). An additional assessment with 780 randomly stratified samples and primary and alternative calls in the reference data to account for ambiguity indicated 83.4% overall accuracy (Kappa of 0.80). A high agreement of 83.6% for all pixels and 92.6% for pixels with a map confidence of more than 50% was found for the comparison between the land cover maps of 2005 and 2006. Further wall-to-wall comparisons to related land cover maps resulted in 56.6% agreement with the MODIS land cover product and a congruence of 49.5 with Globcover. © 2012 Elsevier Inc.


Colditz R.R.,National Commission for the Knowledge and Use of Biodiversity CONABIO
MultiTemp 2013 - 7th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: "Our Dynamic Environment", Proceedings | Year: 2013

To eliminate atmospheric disturbances and sensor malfunctions many remote sensing products are offered in form of composites that combine multiple observations over a defined period. However, selecting specific observations result in varying interval lengths which affect and limit time series analysis techniques. This study provides a conceptual view on the possibilities to use or define specific composite days and its impact on time series. Often the starting or final day of the compositing period is selected, alternatively the middle day may be chosen. These time series are compared to a series that considers the actual day of observation. An experimental exercise employs 16-day MODIS VI composites of 1000m spatial resolution for a 1100×500km region in central Mexico. Statistical measures such as temporal cross-correlation, harmonic analysis, and difference in start, center, and end of season were used to characterize the temporal shift in time series. A shift of approximately seven days with a high variability is introduced when using the starting day of the compositing period, which is mitigated with assuming the middle day. However, only time series that take into account the day of observation can be used for correct estimation of temporal characteristics. © 2013 IEEE.


Colditz R.R.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Llamas R.M.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Ressl R.A.,National Commission for the Knowledge and Use of Biodiversity CONABIO
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

The goal of the North American Land Change Monitoring System (NALCMS) is to provide annually updated land cover maps for the North American continent using satellite information and automated data processing. Current activities of the project aim at the development of an automated algorithm to detect areas of change using 250 m MODIS data. This paper shows the methodology developed for Mexico and demonstrates the resulting change map between the years 2005 and 2010. A data-driven algorithm that builds upon the spectral differences of monthly image composites was developed and critical parameters were defined. Results show that only extreme values of difference images indicate change and that change has to be mapped in at least 25% of all features. The total area of change detected between 2005 and 2010 was 702,331 ha (0.36% of the country) which is in line with other change detection studies in Mexico. Accuracy assessment using higher spatial resolution images accounts for the change fraction in the reference data. The overall accuracy of the change/no change mask is approximately 80%. This is similar to decision tree-based change classification that was developed in other studies and applied to Mexico and significantly better than post-classification change detection. The main limitation is the coarse spatial resolution considering the small-patch landscape structure for large portions of the country, which results in a high omission error (50%) but only 20% commission error for change. © 2013 IEEE.


Colditz R.R.,National Commission for the Knowledge and Use of Biodiversity CONABIO
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Many ecological and agricultural studies require accurate information about the phenological state of the vegetation such as the start, peak/plateau, and end of the growing season. This study investigates the impact of the day of observation in image composites on time-series generation and analysis. It employs daily Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and 16-day vegetation index (VI) composites and combines data streams from Terra and Aqua. Results of a time series that take into account the day of observation and time series that assume the starting, ending, or middle day of the compositing period were compared to a reference of daily observations. A temporal shift of approximately 7.5 days with a high variation in error is introduced if the start of the compositing period is assumed. The middle day mitigates the mean error close to zero but cannot fully compensate for temporal delays. Only a time series that takes into account the actual day of observation can be used for the correct estimation of temporal characteristics. In addition, the study addresses that the day of observation for time-series generation from image composites is imperative when combining data of different data streams with phased production cycles such as MODIS VI composites from Terra and Aqua. © 2014 IEEE.


Colditz R.R.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Equihua J.,National Commission for the Knowledge and Use of Biodiversity CONABIO
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2014

Many global, regional and even national products are derived from relatively coarse spatial resolution products (250m-1000m). Although useful, such categorical maps may suffer from underestimating classes that are spatially scattered or mostly make up only small patches with complex shapes, which hardly ever fully cover the area of a coarse resolution pixel. An alternative is the estimation of class memberships; that is the proportion of each class in every pixel. This study compares the results from discrete maps and class memberships from two commonly used approaches: Random Forest Classification (RF-C), from which the class probability was estimated from 1000 iterations, and Cubist as a member of regression approaches. The study shows that class memberships better estimate the area than discrete maps. For class memberships, Cubist shows higher accuracies and lower area estimation differences than RF-C, but the contrary pattern is shown for discrete maps. © 2014 IEEE.


Molgora J.M.E.,National Commission for the Knowledge and Use of Biodiversity CONABIO
ACM International Conference Proceeding Series | Year: 2012

Since 2000 CONABIO has processed and published in near real time geospatial information of occurring wild fires in Mexico and Central America as part of the " Hotspot detection with remote sensing techniques" program. Currently the Fire Alert System receives via direct-broadcast raw data from MODIS (Aqua and Terra platforms) and VIIRS (NPP). This raw data is processed to L1A, L1B, MOD14, MOD13 and MOD11 products (at the moment we only process VIIRS to L1B). MOD14 gives us a Fire-Mask raster that has the coordinates of the thermal anomalies called hotspots (Giglio, 2003). Each hotspot represents a 1 Km squared polygon that, with nearly 0.9 probability, has a uniformly distributed fire. The system automatically publishes information of county level locality name, area slope, ecosystem type and closeness to the nearest "Natural Protected Area". The system also has a WMS service provided by GeoServer that stores thematic map layers of Fire Propagation Index, a NDVI time-series analysis model that measures vegetation anomalies, and Moisture Percentage Map, based on dead vegetation fuel and rainfall duration raster products. Every 24 hrs we process a mean of 6 satellite passes during day and night. Over time, the amount of data has surpass the original capacity of the system. Although the first generation software has been upgraded to accomplish the needs of users, specially fire fighters, researches and decision-making people, the urgent need of a platform capable of forecasting, performing complex analysis and integrating it with next generation data sources has turned the use and administration of the old system an awkward task. A next-generation system is being developed at the moment and the release date is expected by the end of this year. The system lies on a Postgres9.1-Postgis2.0 with raster support engine that decreases dramatically the processing time. The design of a software based on a spatially-enable database has opened a new world of opportunities. This system will be based purely on Open Source Software. Fire Alert System homepage: http://www.conabio.gob.mx/incendios/. © 2012 Author.


Cord A.F.,Helmholtz Center for Environmental Research | Klein D.,German Aerospace Center | Mora F.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Dech S.,German Aerospace Center | Dech S.,University of Würzburg
Ecological Modelling | Year: 2014

Given the rapid loss of biodiversity worldwide and the resulting impacts on ecosystem functions and services, we more than ever rely on current and spatially continuous assessments of species distributions for biodiversity conservation and sustainable land management. Over the last decade, the usefulness of categorical land cover data to account for the human-induced degradation, transformation and loss of natural habitat in species distribution models (SDMs) has been questioned and the number of studies directly analyzing remotely sensed variables has lately multiplied. While several assumptions support the advantages of remote sensing data, an empirical comparison is still lacking. The objective of this study was to bridge this gap and compare the suitability of an existing categorical land cover classification and of continuous remote sensing variables for modeling the distribution patterns of 30 Mexican tree species. We applied the Maximum Entropy algorithm to predict species distributions based on both data types independently, quantified model performance and analyzed species-land cover relationships in detail. As part of this comparison, we focused on two particular aspects, namely the effects of (1) thematic detail and (2) spatial resolution of the land cover data on model performance. Our analysis revealed that remote sensing data were significantly better model predictors and that the main obstacle of the land cover-based SDMs were their bolder predictions, together with their overall overestimation of suitability. Among the land cover-based models, we found that thematic detail was more important than spatial resolution for SDM performance. However, our results also suggest that the suitability of land cover data differs largely among species and is dependent on their habitat distinctiveness. Our findings have relevant implications for future species distribution modeling studies which aim at complementing their set of topo-climatic predictors by data on land surface characteristics. © 2013 Elsevier B.V.


Colditz R.R.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Ressl R.A.,National Commission for the Knowledge and Use of Biodiversity CONABIO
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

Many remote sensing products that are useful for time series analysis and seasonal monitoring studies are offered in form of composites. A composite combines a number of observations of a defined period and selects or computes one value. This results in observations sampled at varying time intervals that rules out a high number of time series analysis techniques. This study investigates the impact of either using the actual day of observation to generate a time series from composites or assuming the starting or middle day of the compositing period. For this study 16-day MODIS VI composites of 1km spatial resolution from Terra and Aqua were employed. A 1100x500km region in central Mexico served as study site. Statistical measures including temporal cross-correlation and the root mean square error were used for time series analysis. A temporal shift of approximately seven days with a high variability is introduced when using the starting day of the compositing period. The middle day mitigates the mean error close to zero but still shows a high error variability. Only time series that take into account the day of observation and estimate from that samples at equidistant intervals can be used for a correct estimation of temporal characteristics. © 2013 SPIE.


Colditz R.R.,National Commission for the Knowledge and Use of Biodiversity CONABIO
Remote Sensing | Year: 2015

Land cover mapping for large regions often employs satellite images of medium to coarse spatial resolution, which complicates mapping of discrete classes. Class memberships, which estimate the proportion of each class for every pixel, have been suggested as an alternative. This paper compares different strategies of training data allocation for discrete and continuous land cover mapping using classification and regression tree algorithms. In addition to measures of discrete and continuous map accuracy the correct estimation of the area is another important criteria. A subset of the 30 m national land cover dataset of 2006 (NLCD2006) of the United States was used as reference set to classify NADIR BRDF-adjusted surface reflectance time series of MODIS at 900 m spatial resolution. Results show that sampling of heterogeneous pixels and sample allocation according to the expected area of each class is best for classification trees. Regression trees for continuous land cover mapping should be trained with random allocation, and predictions should be normalized with a linear scaling function to correctly estimate the total area. From the tested algorithms random forest classification yields lower errors than boosted trees of C5.0, and Cubist shows higher accuracies than random forest regression. © 2015 by the authors; licensee MDPI, Basel, Switzerland.


Colditz R.R.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Schmidt M.,National Commission for the Knowledge and Use of Biodiversity CONABIO | Conrad C.,University of Würzburg | Hansen M.C.,South Dakota State University | And 2 more authors.
Remote Sensing of Environment | Year: 2011

Regularly updated land cover information at continental or national scales is a requirement for various land management applications as well as biogeochemical and climate modeling exercises. However, monitoring or updating of map products with sufficient spatial detail is currently not widely practiced due to inadequate time-series coverage for most regions of the Earth. Classifications of coarser spatial resolution data can be automatically generated on an annual or finer time scale. However, discrete land cover classifications of such data cannot sufficiently quantify land surface heterogeneity or change. This study presents a methodology for continuous and discrete land cover mapping using moderate spatial resolution time series data sets. The method automatically selects sample data from higher spatial resolution maps and generates multiple decision trees. The leaves of decision trees are interpreted considering the sample distribution of all classes yielding class membership maps, which can be used as estimates for the diversity of classes in a coarse resolution cell. Results are demonstrated for the heterogeneous, small-patch landscape of Germany and the bio-climatically varying landscape of South Africa. Results have overall classification accuracies of 80%. A sensitivity analysis of individual modules of the classification process indicates the importance of appropriately chosen features, sample data balanced among classes, and an appropriate method to combine individual classifications. The comparison of classification results over several years not only indicates the method's consistency, but also its potential to detect land cover changes. © 2011 Elsevier Inc.

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