Dr. Yan Gao Profile

Dr. Yan Gao

at Univ Nacional Autónoma de México

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Publications (2)

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Proceedings Article | 18 November 2014 Paper

Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

Yan Gao, Prashanth Marpu, Luis M. Morales Manila

Proceedings Volume 9263, 92630P (2014) https://doi.org/10.1117/12.2068966

KEYWORDS: Image classification, Image segmentation, Sensors, Accuracy assessment, Image analysis, Remote sensing, Spatial resolution, Vegetation, Image processing algorithms and systems, Satellites

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This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

Proceedings Article | 8 November 2014 Paper

National level biomass database comparison for Mexico in relation to vegetation degradation stages

Jean Francois Mas, Yan Gao, Jaime Paneque-Galvez, Adriana Rodriguez

Proceedings Volume 9260, 92604I (2014) https://doi.org/10.1117/12.2068974

KEYWORDS: Vegetation, MODIS, Data centers, Data modeling, Databases, Carbon, Biological research, Remote sensing, Spatial resolution, Clouds

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Anthropogenic land cover change, e.g. deforestation and forest degradation cause carbon emission. To estimate deforestation and forest degradation, it is important to have reliable data on vegetation and carbon distribution. In Mexico, land cover maps are available at national level in which vegetation is described in four statuses: primary, secondary (“woodland”), secondary (“shrub land”), and secondary (“grass”) according to degradation stages. Data on biomass/carbon distribution are also available including: (1) INFyS: national forest and soil inventory; (2) MODIS WHRC: biomass data by Woodshole Research Center for Pantropical region using MODIS data; (3) PALSAR EHRC: biomass data produced by WHRC for Mexico using PALSAR data; (4) MODIS VCF: Vegetation Continuous Fields percent tree cover layer. The aim of this study is 1) to evaluate if degradation stages and biomass are positively correlated, e.g. better preserved vegetation has more biomass, and 2) to evaluate the spatial patterns of the comparison in 1) using geographically weighted regression (GWR), 3) to assess the correlation among the biomass datasets including VCF data. Results show that 1) in general, the biomass value decreases following the degradation stages and the most degraded stage corresponds to the least biomass value. Cuzick value shows that this trend is significant in most of the cases. However, there is serious overlapping in biomass values in various stages. 2) GWR results show that in some regions the four disturbance stages corresponds better with the difference in biomass values. The regions with higher parameter value show better correlation. 3) The biomass data from PALSAR WHRC show higher Spearman values and thus stronger correlation with the biomass data from INFyS. However, due to that biomass data from INfyS and PALSAR WHRC are not independent; we consider the better correlation is from the rest two biomass datasets.

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