Generation and analysis of the 2005 land cover map for Mexico using 250m MODIS data

Colditz, René R.; Saldaña, Gerardo López; Maeda, Pedro; Espinoza, Jesús Argumedo; Tovar, Carmen Meneses; Hernández, Arturo Victoria; Benítez, Carlos Zermeño; López, Isabel Cruz; Ressl, Rainer

doi:10.1016/j.rse.2012.04.021

Cited by 54 publications

(54 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our findings regarding the more accurate performance using stratification-based classifications may be highly relevant for subnational-to continental-scale classification efforts [46,47], which often suffer from a lack of accurate generalization of training signatures across large areas with non-parametric machine-learning classifiers [35,110,111]. For instance, in one of the preceding works where one of the authors was involved [35], the difficulty of creating an accurate land-cover classification across Eastern Europe was attributed to a lack of spectral signature generalization by a single non-parametric machine-learning classifier (SVM).…”

Section: Discussionmentioning

confidence: 86%

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Löw¹,

Prishchepov

Waldner

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Abstract:Cropland abandonment is globally widespread and has strong repercussions for regional food security and the environment. Statistics suggest that one of the hotspots of abandoned cropland is located in the drylands of the Aral Sea Basin (ASB), which covers parts of post-Soviet Central Asia, Afghanistan and Iran. To date, the exact spatial and temporal extents of abandoned cropland remain unclear, which hampers land-use planning. Abandoned land is a potentially valuable resource for alternative land uses. Here, we mapped the abandoned cropland in the drylands of the ASB with a time series of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016]. To overcome the restricted ability of a single classifier to accurately map land-use classes across large areas and agro-environmental gradients, "stratum-specific" classifiers were calibrated and classification results were fused based on a locally weighted decision fusion approach. Next, the agro-ecological suitability of abandoned cropland areas was evaluated. The stratum-specific classification approach yielded an overall accuracy of 0.879, which was significantly more accurate (p < 0.05) than a "global" classification without stratification, which had an accuracy of 0.811. In 2016, the classification results showed that 13% (1.15 Mha) of the observed irrigated cropland in the ASB was idle (abandoned). Cropland abandonment occurred mostly in the Amudarya and Syrdarya downstream regions and was associated with degraded land and areas prone to water stress. Despite the almost twofold population growth and increasing food demand in the ASB area from 1990 to 2016, abandoned cropland was also located in areas with high suitability for farming. The map of abandoned cropland areas provides a novel basis for assessing the causes leading to abandoned cropland in the ASB. This contributes to assessing the suitability of abandoned cropland for food or bioenergy production, carbon storage, or assessing the environmental trade-offs and social constraints of recultivation.

show abstract

Section: Discussionmentioning

confidence: 86%

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Löw¹,

Prishchepov

Waldner

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Table 2 shows the class aggregation scheme from 18 classes of IDEAM to nine classes of the IGBP legend; class "snow" was aggregated to barren since the area is too small to be considered in the training process. Training sites were only selected in core areas of the reference cartography (i.e., IDEAM map) at least 500 m away from other land cover classes (3 ¢ 3 kernel of which all pixels had the same class as the central pixel [47]). More training data as relative to its expected area from the IDEAM map were sampled for barren, broadleaf forests and secondary vegetation to mitigate confusion with urban and built-up and shrubland [47].…”

Section: Reference Data For Training and Legend Definitionmentioning

confidence: 99%

Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series

2015

View full text Add to dashboard Cite

Abstract:Generating annual land cover maps in the tropics based on optical data is challenging because of the large amount of invalid observations resulting from the presence of clouds and haze or high moisture content in the atmosphere. This study proposes a strategy to build an annual time series from multi-year data to fill data gaps. The approach was tested using the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index and spectral bands as input for land cover classification of Colombia. In a second step, selected ancillary variables, such as elevation, L-band Radar, and precipitation were added to improve overall accuracy. Decision-tree classification was used for assigning eleven land cover classes using the International Geosphere-Biosphere Programme (IGBP) legend. Maps were assessed by their spatial confidence derived from the decision tree approach and conventional accuracy measures using reference data and statistics based on the error matrix. The multi-year data integration approach drastically decreased the area covered by invalid pixels. Overall accuracy of land cover maps significantly increased from 58.36% using only optical time series of 2011 filtered for low quality observations, to 68.79% when using data for 2011¨2 years. Adding elevation to the feature set resulted in 70.50% accuracy.

show abstract

“…The NALCMS provides land-cover information across the different ecosystems in North America, derived with 10-day MODIS composites at 250 m resolution (Latifovic et al 2010). In the case of Mexico, land-cover classification maps embedded in the NALCMS were developed for 2005 and 2010 forming the basis of the comparison in this study; information on the classification approach is provided by Colditz et al (2012). Fifteen land-cover classes were identified using multiple classifications (ensemble classifier) with decision trees and ancillary datasets including slope, aspect, temperature, precipitation, aerial photography, and high spatial resolution satellite images.…”

Section: Remote Sensing Observations Of Land-cover Changementioning

confidence: 99%

“…This was mainly due to a 13 % decrease in the amount of cultivated hectares reported in 2010 in the municipality records of the agricultural database of SAG-ARPA (SAGARPA 2012). Remote sensing observations of land-cover change From the land-cover change analysis derived with MODIS thematic maps, fourteen of the fifteen land-cover classes identified in Mexico by Colditz et al (2012) were found in the YP. Land-cover change from 2005 to 2010 totaled 7,281 ha.…”

Section: Forest Disturbances In the Yucatan Peninsulamentioning

confidence: 99%

Attributing changes in land cover using independent disturbance datasets: a case study of the Yucatan Peninsula, Mexico

et al. 2014

View full text Add to dashboard Cite

Detailed observations of natural and anthropogenic disturbance events that impact forest structure and the distribution of carbon are essential to estimate changes in terrestrial carbon pools and the associated emissions and removals of greenhouse gasses. Recent advances in remote sensing approaches have resulted in annual and decadal estimates of land-cover change derived from observations using broad-scale moderate resolution imaging spectroradiometer (MODIS) 250 m-1 km imagery. These land-use change estimates, however, are often not attributed directly to a cause or activity and are not well validated, especially in tropical areas. Knowledge of the type of disturbance that caused the observed land-cover changes is important, however, for the quantification of the associated impacts on ecosystem carbon stocks and fluxes. In this paper, we provide estimates of the amount of forest land-cover change in a Mexican forested region and propose an approach for attributing the cause of the observed changes to the underlying disturbance driver. To do so, we collate geospatial and remote sensing data from a variety of sources to summarize statistics about the major disturbances within the Yucatan Peninsula, an ''early action'' region for the reduction of emissions from deforestation and degradation, from 2005 to 2010. We combine the datasets to develop rules to estimate the likely disturbances that caused the observed land-cover changes based on their spatially explicit location. Finally, we compare our observed disturbance rates to those detected using classified land-cover data derived from MODIS.

show abstract

Generation and analysis of the 2005 land cover map for Mexico using 250m MODIS data

Cited by 54 publications

References 34 publications

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series

Attributing changes in land cover using independent disturbance datasets: a case study of the Yucatan Peninsula, Mexico

Contact Info

Product

Resources

About