Predicting the Seasonal NDVI Change by GIS Geostatistical Analyst and Study on Driver Factors of NDVI Change in Hainan Island, China

Abstract

As Hainan Island belonged to tropical monsoon influenced region, vegetation coverage was high. It is accessible to acquire the vegetation index information from remote sensing images, but predicting the average vegetation index in spatial distributing trend is not available. Under the condition that the average vegetation index values of observed stations in different seasons were known, it was possible to qualify the vegetation index values in study area and predict the NDVI (Normal Different Vegetation Index) change trend. In order to learn the variance trend of NDVI and the relationships between NDVI and temperature, precipitation, and land cover in Hainan Island, in this paper, the average seasonal NDVI values of 18 representative stations in Hainan Island were derived by a standard 10-day composite NDVI generated from MODIS imagery. ArcGIS Geostatistical Analyst was applied to predict the seasonal NDVI change trend by the Kriging method in Hainan Island. The correlation of temperature, precipitation, and land cover with NDVI change was analyzed by correlation analysis method. The results showed that the Kriging method of ARCGIS Geostatistical Analyst was a good way to predict the NDVI change trend. Temperature has the primary influence on NDVI, followed by precipitation and land-cover in Hainan Island.

Vegetation monitoring in the South Aral Sea region by remote sensing and GIS

“…This, in turn, means that changes in vegetation dynamics are related to changes in the environment [10,[17][18][19][20]. Results of the use of NDVI indices in determining the area covered by vegetation found that the use of this index in quantitative indicators was effective [13]. NDVI indicators have been used to differentiate between green and non-green plants in the region.…”

“…This made it necessary to learn the variance trend of NDVI in a region. We could qualify the NDVI value and predict the NDVI change trend if we knew the average NDVI value of each station during each of the distinct seasons [13]. Photosynthetically active irradiation of NDVI has been strongly correlated with vegetation biomass, green cover, and leaf area index.…”

The Relationship between Emotional Perception and High-Density Built Environment Based on Social Media Data: Evidence from Spatial Analyses in Wuhan

“…Issues such as weak scale perception, limited data samples, and singular analysis approaches have arisen. However, GIS can facilitate comprehensive and precise quantitative analysis of high-density built environments through techniques such as big data processing and visualization [30], spatial morphology assessment [31,32], ecological environment monitoring [33], spatial element identification [34], and spatial statistical research [35]. In terms of emotional perception, traditional research methods typically fall into two categories: first, by constructing subjective indicators such as cognition and employing methods like questionnaire surveys to obtain respondents' emotional perceptions [36] and second, by collecting textual information such as travelogues and interviews from travel websites and inferring people's emotions towards cities or places through text analysis [37].…”

Pictograma: Elaboração Em Linguagem R

“…As respostas espectrais da vegetação são modificadas com base na densidade da folha e na estrutura do dossel. As diferenças relativas nas características espectrais vermelho (RED) e infravermelho próximo (NIR) formam a base de vários índices de vegetação, que são projetados para avaliar a condição da vegetação (LIU et al, 2016).…”