Climatic factors impact vegetation. Our study was to examine and analyze the climate variability and relationship to vegetation in Garamba National Park of the Democratic Republic of the Congo over the past 30 years (...Climatic factors impact vegetation. Our study was to examine and analyze the climate variability and relationship to vegetation in Garamba National Park of the Democratic Republic of the Congo over the past 30 years (1990 to 2020), then to relate the climatic variables. Mann Kendall’s non parametric test, ANOVA, and p-value tests are used to analyze existing trends and relationships between vegetation cover, climatic factors, land surface temperature (LST) and normalized difference in temperature Vegetation index (NDVI), Enhanced vegetation index (EVI) in Garamba national park which is of particular importance for the network of protected areas of the Democratic republic of Congo because its position at the northern limit of the savanna-forest mosaics gives it a unique biodiversity. The southern part of the park is dominated by grassy shrub savannas. The results showed that: 1) In Garamba, the monthly correlation coefficient of Kendall and Pearsan between temperature and precipitation are negative respectively 0.763 and <span style="white-space:nowrap;">−</span>0.876 (p-value < 0.00001). 2) Annually during the three decades in Garamba, the correlation between precipitation and NDVI is significant 0.416 (Kendall) and 0.496 (Pearsan);the same between precipitation and EVI 0.291 (Kendall) and 0.496 (Pearsan) while LST and precipitation are negatively correlated (p-value < 0.00001).展开更多
以石家庄中心城区为研究区,基于Landsat8遥感影像,对比分析线性光谱混合分析法和归一化差值不透水面指数在平原区不透水面信息提取中的精确度,从而确定石家庄中心城区这种平原区不透水面提取的最佳方法,并对石家庄中心城区不透水面空间...以石家庄中心城区为研究区,基于Landsat8遥感影像,对比分析线性光谱混合分析法和归一化差值不透水面指数在平原区不透水面信息提取中的精确度,从而确定石家庄中心城区这种平原区不透水面提取的最佳方法,并对石家庄中心城区不透水面空间分布格局进行分析。结果表明:线性光谱混合分析法提取精度较高,RMSE(root mean square error)为0.246。石家庄中心城区不透水面面积占比为77.81%,其中,桥西区、裕华区、新华区和长安区不透水面面积占比分别为84.47%、80.45%、75.90%、74.96%,空间分布相对分散。本研究可为石家庄城市空间布局及规划建设提供数据支撑。展开更多
文摘Climatic factors impact vegetation. Our study was to examine and analyze the climate variability and relationship to vegetation in Garamba National Park of the Democratic Republic of the Congo over the past 30 years (1990 to 2020), then to relate the climatic variables. Mann Kendall’s non parametric test, ANOVA, and p-value tests are used to analyze existing trends and relationships between vegetation cover, climatic factors, land surface temperature (LST) and normalized difference in temperature Vegetation index (NDVI), Enhanced vegetation index (EVI) in Garamba national park which is of particular importance for the network of protected areas of the Democratic republic of Congo because its position at the northern limit of the savanna-forest mosaics gives it a unique biodiversity. The southern part of the park is dominated by grassy shrub savannas. The results showed that: 1) In Garamba, the monthly correlation coefficient of Kendall and Pearsan between temperature and precipitation are negative respectively 0.763 and <span style="white-space:nowrap;">−</span>0.876 (p-value < 0.00001). 2) Annually during the three decades in Garamba, the correlation between precipitation and NDVI is significant 0.416 (Kendall) and 0.496 (Pearsan);the same between precipitation and EVI 0.291 (Kendall) and 0.496 (Pearsan) while LST and precipitation are negatively correlated (p-value < 0.00001).
文摘以石家庄中心城区为研究区,基于Landsat8遥感影像,对比分析线性光谱混合分析法和归一化差值不透水面指数在平原区不透水面信息提取中的精确度,从而确定石家庄中心城区这种平原区不透水面提取的最佳方法,并对石家庄中心城区不透水面空间分布格局进行分析。结果表明:线性光谱混合分析法提取精度较高,RMSE(root mean square error)为0.246。石家庄中心城区不透水面面积占比为77.81%,其中,桥西区、裕华区、新华区和长安区不透水面面积占比分别为84.47%、80.45%、75.90%、74.96%,空间分布相对分散。本研究可为石家庄城市空间布局及规划建设提供数据支撑。
