1crop monitoring
2change detection
3LULC
4NDVI
5a trend analysis of rainfall and temp in terms of agriculture context
6SOIL moisture
please suggest me in this what can we add????? I need your suggestions
Thank you
Hi Bushra
More suggestions meaning more work. For what you already mentioned, this requires a long time research. Can you be more specific on what you exaclty want to do here?
Hi Bushra. I agree with Ahmed. You need to be more specific on what you need. Anyway, if you go to Google Scholar and search using keywords like "climate change agriculture remote sensing" you'd find so many relevant papers. Have you tried it yet?
Eric
Hi Bushra
I think in crop monitoring you should include leaf water potential, canopy temperature. I also agree with Ahmed and Salas.
Best regards,
Attila
Dear all,
Thank you so much for your precious word,my point is i want to do three things
1.change detection of agriculture (you just tell me how many years data should i take for i have 35 years data of temp and pricip and area and agriculture production data in my area?so how should i do for this regression modelling or what
2.soil moisture and leaf area index and NDVI can i do this in this?
3.i need one crop model like info crop or ceres crop model to get productivity but i m unable to do APSIM model i have but not getting idea how to proceed.
yes sir i read many papers many people did change detection based on rainfall and temperature and agriculture production yearly
some people did NDVI and show how NDVI index changing.
i need you all to tell me all
Thank you very much
You have got a lot of information. You may look at the paper. You will get some useful information about climate, agriculture and GIS.
Modeling of agricultural drought risk pattern using Markov chain and GIS in the western part of Bangladesh
Hi Bushra;
You can estimate the agriculture productivity by using Landsat images based on normalized difference vegetation Index (NDVI) and scatter plot classification methods (SCP). The productivity estimation can be implemented on Landsat-8 (Operational Land Imager OLI) images. The OLI bands can enlarged by employing the discrete wavelet transform DWT method. The histograms of the enlarged higher resolution bands are pushed to match the histogram of its corresponding original multi spectral bands by utilizing the histogram-specification method. The productivity areas can be estimated by utilizing the NDVI and SCP methods from the original, wave enlarged, and enlarged-histogram-specified bands. lastly, you can compared the estimated areas with the real cultivated areas to ensure the validation of method.
regards
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