I want to analyse the long-term changes in the spatio-temporal distribution of aquatic weeds under the influence of climate variability in water bodies.
The changes in coverage of aquatic weeds was analysed using the supervised maximum likelihood classification of five Landsat images over a 27-year period, from 1992 to 2019 using ENVI remote sensing software. The non-parametric Mann-Kendall trend test and Sen's slope estimate were used to analyze the trend of annual rainfall and temperature. We observed a positive trends of temperature that was statistically significant (p value < 0.05) and positive trend of rainfall that was not statistically significant ( p values > 0.05) (but significant at 0.1 level). Over the time period. Our results showed fluctuations in the distribution of AIAPs in the short-term; however, the coverage of AIAPs showed an increased trend in the study area in the long-term.
I have climate data of the study area (Temperature and rainfall) from 1951 to 2100. I want to analyse those data and predict how would be the aquatic weed distribution in the future under the influence of climate change. Whether the aquatic weeds is likely to increase or decrease under climate variability in the study area.
Can somebody instruct me how to do this analysis?
Predictive modelling under climate change scenario can be accomplished by using MaxEnt software.
Read the attached document for details.
Chlampika, the suggestion made by
Gibji Nimasow is in order. But your study showed significance between
aquatic weed and temperature to be significant at 5 percent; significance between aquatic weed and rainfall to be significant only at 10 percent. This is your output from statistical analysis. It is very likely that temperature was more stable than rainfall over the period studied. If that happens to have been the case, will change of your statistical technique magically change the result ?
Hi Gibji and Harold
In my thesis, I have used maxent in another chapter. This study is based on remote sensing technique. I used climate data of the study period (1992-2019) and analysed how climate variability may affect aquatic plant distribution. My supervisor suggested to get future Projected climate data and examine the potential distribution. Any idea of doing this please?
Champika Kariyawasam Madam, I am suggesting you that exactly. You have to extract the future projected climate data from Bioclim website. The data is available for RCP 4.5, 6, 7, 8.5 which is the representative of carbon dioxide (CO2) emissions peak and decline. For example, the RCP 4.5 means the emissions peak around 2040 then decline. You have to the same occurrence points and 2 different sets of climatic variables for the present as well as future climate. As you are aware of Maxent technique, you know how to add the layers. So, load the occurrence data in appropriate place, then load the current climate data in the section of environmental layers and finally load the projected climate data in the lower section where is an option named Projection layers Directory/File. Just keep in mind that all the layers both present and future data should be in same resolution and dimension else the model may not run. Additionally, there are many published research materials you can go through and complete your work. I am attaching an article where the authors have done predictive modelling of Cinnamommum camphora using Maxent in China. Hope this will help you.
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