Intrinsic color correction for stereo matching

Ran, Qing; Zhao, Wenjing; Feng, Jiashi

doi:10.1016/j.cag.2019.05.008

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…However, the advancement of remote sensing in recent years has greatly aided in the accurate and comprehensive monitoring of vegetation dynamics at multiple scales due to its high spatial resolution, continuous time series, and advantages in easily accessing vegetation when compared to previous traditional methods [10,11]. However, how to accurately extract detailed information about vegetation change from a large number of time series records, including where the change occurred, the process (pattern) of the change, the reason for the change, and the duration of the change, is a prerequisite for studying the driving mechanism of vegetation change [12][13][14]. Furthermore, multiple approaches, such as linear regression analysis [6], Sen and Mann-Kendall models [15], and standard deviation analysis [16,17], have been widely used in previous research to estimate the dynamics of vegetation cover at a large scale.…”

Section: Introductionmentioning

confidence: 99%

How Did the Southwest China Mountains Turn Green from 2000 to 2020? A Case Study in Chongqing

Cheng,

Qian,

Liu

et al. 2023

Forests

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Vegetation, as a comprehensive ecological indicator of environmental change, plays an essential role in terrestrial ecosystems. Dramatic climate change and increasing anthropogenic disturbances have led to a profound change in vegetation cover. This study employed Moderate Resolution Imaging Spectroradiometer (MODIS) and other relevant data in Southwest China mountains, using Chongqing as a case study, to explore the variation in Normalized Difference Vegetation Index (NDVI) and its spatial differentiation mechanisms, quantify the relative contributions of climate change and human activities to these variations, and assess the effects of ecological policies, including afforestation, mountain closure forestry and natural forest conservation, and agricultural improvement. Results showed that the spatial distribution of annual NDVI, on average, varied 0.109 to 0.929, with the rate varying from −0.033 to 0.017 yr−1. NDVI was improved in 83.4% areas of Chongqing, and human activities contributed 69.1% of revegetation, whereas climate contributed 30.9%. On the other hand, human activities and climate change contributed 76.3% and 23.7% to vegetation degradation in 17.6% areas, respectively. Moreover, the explanatory power of anthropogenic factors on spatial differentiation of NDVI were stronger than that of climatic factors, and land use type was the dominant factor. The cumulative afforestation area, effective irrigation rate, fertilizer application, and total agricultural machinery power of the whole city was extremely significantly correlated with NDVI (p < 0.01), and the vegetation cover fluctuation in the region affected by natural forest protection and mountain closure forestry project was much more stable than that in non-protected areas, which to a certain extent reflected the effective implementation of local ecological protection, agricultural improvement, and other related policies.

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