Uzaktan algılama verileri temel alınarak verimlilik tahminininoluşturulması
Generation of yield estimation based on remote sensing data
Yazar: NILUFAR KARIMLI
Danışman: DR. ÖĞR. ÜYESİ MAHMUT OĞUZ SELBESOĞLU
İstanbul Teknik Üniversitesi / Bilişim Enstitüsü / İletişim Sistemleri Ana Bilim Dalı / Uydu Haberleşmesi ve Uzaktan Algılama Bilim Dalı
Derece: Yüksek Lisans, 2023
Konu: Jeodezi ve Fotogrametri = Geodesy and Photogrammetry

Özet:
Artan insan nüfusunun yeterli gıda ile temin edilmesine ilişkin kaygılar, dikkatleri Gıda Güvenliği alanına çekmektedir. Tarımsal verilerin geleceğe odaklı analiz edilmesi ve işlenmesi, bu alandaki planlama potansiyelini geliştirmekle birlikte gerekli önlemlerin önceden alınmasını da sağlamaktadır. Ancak, bu bölgelerin genişliği ve sayısı göz önüne alındığında, saha araştırması pahalı ve zaman alıcı bir prosedür olmaktadır. Uzaktan Algılama ve optik sensörlerin ortaya çıkmasıyla, çeşitli verileri uzaktan, hızlı ve düşük maliyetli bir şekilde elde etmek mümkün hale gelmiştir.
Bu tez çalışması, Gıda Güvenliği alanında Uzaktan Algılama veri uygulamasının sınırlamalarını ve kapasitesini araştırmıştır. Sonuç olarak, Mamatkulov yaklaşımı ve MEDALUS modeli kullanılarak, Sentinel 1 ve Sentinel 2 verilerinden kışlık buğdayda oldukça doğru Verim Tahmini sonuçları (%98,03) hiçbir maliyet olmadan ve yüksek kullanılabilirlikle elde edilmiştir.
Bu yöntem, regresyon modellerinin oluşturulmasını veya herhangi bir saha çalışmasını beklemeye gerek kalmadan, yeni oluşturulmuş veya önceki yılların verimliliği hakkında bilgi sahibi olmadığımız ekin alanlarının verimliliği hakkında tahminlerde bulunmayı mümkün kılabilir. Çıkan sonuca bakıldığında bu konuda daha kapsamlı analizler yapılabilir.

Abstract:
Concerns about the expanding human population's adequate supply of food draw attention to the field of Food Security. Various studies indicates that the total crop production need to be doubled for procurement of the fast-growing population of the Earth. When we add the decreasing water reserves and the productivity capacity of the soils in parallel here, the need to develop sustainable agricultural approaches arises.
The modern type of agriculture called precision agriculture, which includes the creation of appropriate plans by calculating factors such as retrospective data analysis, climate, water consumption, fertilizer and pesticide spraying requirements, comes to our aid in the above sense. Analysis of empirical-statistical and physical data are the main elements of precision agriculture. The processing of these data can be carried out with both classical and modern technology-based methods.
Future-focused analysis and processing of agricultural data not only improve planning capabilities in this field but also enables the required precautions to be taken beforehand. However, given the breadth and number of these regions, field research would be an expensive and time-consuming endeavour.
At this very moment, advances in space technology come to our aid. Remote sensing satellites, located in lower and middle orbits and intended for ground surveillance, give new meanings to measurable reality by bringing various filters and applying calculations. These images help to analyze include soil, water, vegetation, etc. values. Therefore, it increases the diversity of transportation and data in every sense. With the advent of remote sensing and optical sensors, it is now possible to acquire diverse data remotely, quickly, and in the long term - inexpensively.
xxii
Our thesis study investigated the limitations and capabilities of remote sensing data application in the field of Food Security. For Food Security applications, it is very crucial to monitor plant status and productivity measures, water reserves and changes in soil quality in real time and to create future plans based on statistical models.
Remote sensing provides data on almost all of these. Moreover, it is possible with today's technology to include remote sensing data in productivity calculation methods and to develop new productivity calculation models based on remote sensing data.
Our study explores the applicability and modifiability limits of a complex productivity model calculated only based on remote sensing data, without laboratory knowledge and space in the efficiency calculation.
Mamatkulov's mixed approach, which is based on various methods, including the classical empirical-statistical model in productivity calculation, the Monteith model, which is the plant growth efficiency calculation method, the water consumption balance model, was modified with the MEDALUS model, which is based on the geometric mean value calculation in soil quality calculation, and overall calculations were carried out.
Optical data on vegetation was used for vegetation and water consumption values, and optical and radar images of bare soil area were used for soil quality values. As the study area, the Privolnoye district of Jalilabad province of the Republic of Azerbaijan was determined, and SUPER ELITE brand winter hard wheat was determined as the study applied harvest type due to the long flocculation process.
Open-source Sentinel 1 and Sentinel 2 data were used for calculations, and an elevation model was produced from radar images. SNAP ESA and QGIS open- source remote sensing and GIS applications are used for calculations.
The model we developed as a result of the calculations achieved low RMSE (0.01985) and high accuracy (98.0326%) on winter wheat obtained from the Sentinel 1 and Sentinel 2 data without any cost and with high availability.
A non-linear positive correlation was found between NDVI and SQI. Thus, it was concluded that if the SQI in the soil shows high trends before planting, the NDVI and thus the LAI values will also be high.
The Mamatlukov model took into account the effect of water consumption and vegetation by NDVI calculation and the effect of SQ based on MEDALUS method on crop yield. This approach has been applied according to many research sources, respectively, with parameters that can only be calculated from satellite images. Results were checked with statistically greater than previous crop yield and minimal RMSE confirmed.
This method can make it possible to make predictions about the productivity of newly created crop fields or for which we do not have information about the productivity of previous years, without the need to wait for building regression models or any field studies. Considering the outcome, wide-range and larger analyses on this topic can be carried through.
Modern agriculture can benefit greatly from the application of GIS and UA technologies to monitor crop development, identify areas affected by different causes, assess soil quality status, predict productivity and visualize the true topography of crop fields, among other spatial analyses. In the future, this study may be modified and scaled up to further improve the results.