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Forests 2017, 8(10), 392; https://doi.org/10.3390/f8100392

Estimation of Vegetation Cover Using Digital Photography in a Regional Survey of Central Mexico

1
Programa Mexicano del Carbono, Texcoco 56230, Mexico
2
Postgrado en Ciencias Forestales, Colegio de Postgraduados, Texcoco 56230, Mexico
3
Postgrado en Hidrociencias, Colegio de Postgraduados, Texcoco 56230, Mexico
4
Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Chihuahua 33820, Mexico
*
Author to whom correspondence should be addressed.
Received: 9 September 2017 / Revised: 3 October 2017 / Accepted: 9 October 2017 / Published: 15 October 2017
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Abstract

The methods for measuring vegetation cover in Mexican forest surveys are subjective and imprecise. The objectives of this research were to compare the sampling designs used to measure the vegetation cover and estimate the over and understory cover in different land uses, using digital photography. The study was carried out in 754 circular sampling sites in central Mexico. Four spatial sampling designs were evaluated in three spatial distribution patterns of the trees. The sampling designs with photographic captures in diagonal form had lower values of mean absolute error (MAE < 0.12) and less variation in random and grouped patterns. The Carbon and Biomass Sampling Plot (CBSP) design was chosen due to its smaller error in the different spatial tree patterns. The image processing was performed using threshold segmentation techniques and was automated through an application developed in the Python language. The two proposed methods to estimate vegetation cover through digital photographs were robust and replicable in all sampling plots with different land uses and different illumination conditions. The automation of the process avoided human estimation errors and ensured the reproducibility of the results. This method is working for regional surveys and could be used in national surveys due to its functionality. View Full-Text
Keywords: automated classification; sampling design; adaptive threshold; over and understory cover automated classification; sampling design; adaptive threshold; over and understory cover
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Salas-Aguilar, V.; Sánchez-Sánchez, C.; Rojas-García, F.; Paz-Pellat, F.; Valdez-Lazalde, J.R.; Pinedo-Alvarez, C. Estimation of Vegetation Cover Using Digital Photography in a Regional Survey of Central Mexico. Forests 2017, 8, 392.

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