Landsat 8 Uydu Görüntü Uygulaması – Ürün Fenolojisinin Haritalanması İçin NDVI Zaman Serisi: Afganistan’ın Balkh ve Jawzjan Bölgeleri Örneği

Year 2019, Volume: 5 Issue: 1, 49 - 62, 30.05.2019

Abdul Walid Salik Ersin Karacabey

https://doi.org/10.28979/comufbed.557792

Cited By: 1

Abstract

Bu makalede tarımsal ürünlerin yetiştirme sezonu boyunca nihai verimini
etkileyen fenolojik dönemleri temsil edebilecek Landsat 8 görüntüsünden elde edilen
normalize edilmiş vejetasyon indeksi (NDVI) değişiminin ortaya konulması
hedeflenmiştir. Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumundan
(USGS) elde edilen Landsat 16-gün 30 m çözünürlüklü NDVI zaman serileri verisi
kullanılarak geniş coğrafi alanlar üzerindeki ürünlerin mevsimsel fenolojik
değişimlerini haritalama amacıyla etkin bir metot geliştirilmiştir. Landsat 8
verilerinin işlenmesi için Google Earth Engine (GEE) platformu kullanılmıştır.
Bulut örtüsüne ve bulut gölgelerine sahip alanlar maskelenmiş, verilerle
doldurulmamış ve yansıma değerlerinin zaman serisine çift lojistik filtresi
uyarlanmıştır. NDVI zaman serilerinden elde edilen fenolojik metrikler TIMESAT
yazılımı ile elde edilmiştir. 2015 ve 2016 yılı yetiştirme sezonu için mevsimsel
veriler sağlanmış ve çalışma alanı için fenoloji haritaları oluşturulmuştur.

Keywords

NDVI, ürün fenolojisi, uzaktan algılama

Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan

Abstract

In this article, it was targeted to reveal the
variations of NDVI which may represent the phenological stages of agricultural
crops derived from Landsat 8 imagery from the start to end of growing seasons
which eventually influence the final yields. An effective method was developed
to map seasonal phenological variations of crops over large geographic regions
using 16-day Landsat 30 m resolution NDVI time series data obtained from USGS.
The Google Earth Engine (GEE) platform was used for processing the Landsat 8
data. The areas with cloud cover and cloud shadows were masked out, filled by
no data and smoothing double logistic filter was fitted on the time series of
the reflectance values. Phenological metrics extracted from the NDVI time
series were obtained by the TIMESAT software. Seasonal data were extracted for
growing seasons of the years of 2015 and 2016. The phenology maps were created
for study area.

Keywords

NDVI, crop phenology, remote sensing

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