Gathering predictors of biodiversity change and reconstructing land cover history in Central Apennines using machine learning and remote sensing data

Year 2025, Volume: 2 Issue: 1, 36 - 47, 29.06.2025

Polina Lemenkova

Abstract

The selection of methods for image processing and software functionality is crucial for monitoring Earth's landscapes. This work presents the use of Machine Learning (ML) methods for remote sensing (RS) data processing. The aim is to perform cartographic analysis of land cover changes with a case of central Apennines, Italy. Technically, we present a ML-based classification method using GRASS GIS software integrated with Python library Scikit-Learn. Image processing using ML methods was investigated by employing the algorithms of GRASS GIS. The data are obtained from the United States Geological Survey (USGS) and include a time series of Landsat 8-9 OLI/TIRS satellite images. The operational workflow of image processing includes RS data processing. The images were classified into raster maps with automatically detected categories of land cover types. The approach was implemented by using a set of modules in scripting language of GRASS GIS, including for non-supervised classification used as training dataset of random pixel seeds. The ML classifiers were used to detect changes in land cover types derived from images. The results show different vegetation conditions in spring and autumn periods. Unlike the existing methods of image classification, ML considers the differences among the spectral reflectance of pixels when modelling topology of patches. Other advantages are that ML uses data on texture and spectral features to measure the similarity of neighbouring landscape patches during the process of generating random decision trees. This study demonstrated the benefits of ML for cartography, RS data processing and geoinformatics.

Keywords

Artificial Neural Network , Decision Tree , Geospatial Data , Ecology , Random Forest , GIS , Italy

Makine öğrenimi ve uzaktan algılama verilerini kullanarak Orta Apeninler'deki biyolojik çeşitlilik değişiminin tahmin edicilerini toplamak ve manzara geçmişini yeniden oluşturmak

Abstract

Görüntü işleme ve yazılım işlevselliği için yöntemlerin seçimi, Dünya manzaralarını izlemek için çok önemlidir. Bu çalışma, uzaktan algılama (UA) veri işleme için Makine Öğrenmesi (MÖ) yöntemlerinin kullanımını sunmaktadır. Amaç, İtalya'nın merkezi Apeninler örneği ile arazi örtüsü değişikliklerinin kartografik analizini gerçekleştirmektir. Teknik olarak, Python kütüphanesi Scikit-Learn ile entegre GRASS CBS yazılımını kullanan bir MÖ tabanlı sınıflandırma yöntemi sunuyoruz. MÖ yöntemlerini kullanarak görüntü işleme, GRASS CBS algoritmaları işletim araştırılmıştır. Veriler Amerika Birleşik Devletleri Jeolojik Araştırması (ABDJA)'den elde edilmiştir ve Landsat 8-9 OLI/TIRS uydu görüntülerinin bir zaman serisini içerir. Görüntü işlemenin operasyonel iş akışı, UA veri işlemeyi içerir. Görüntüler, otomatik olarak algılanan arazi örtüsü türü kategorileriyle raster haritalara sınıflandırılmıştır. Yaklaşım, rastgele piksel tohumlarının eğitim veri kümesi olarak kullanılan gözetimsiz sınıflandırma dahil olmak üzere GRASS CBS'in betik dilinde bir dizi modül kullanılarak uygulanmıştır. MÖ sınıflandırıcıları, görüntülerden türetilen arazi örtüsü türlerindeki değişiklikleri tespit etmek için kullanılmıştır. Sonuçlar, ilkbahar ve sonbahar dönemlerinde farklı bitki örtüsü koşullarını göstermektedir. Mevcut görüntü sınıflandırma yöntemlerinden farklı olarak, MÖ, yamaların topolojisini modellerken piksellerin spektral yansıması arasındaki farkları dikkate alır. Diğer avantajlar, ML'nin rastgele karar ağaçları oluşturma süreci sırasında komşu manzara yamalarının benzerliğini ölçmek için doku ve spektral özelliklerle ilgili verileri kullanmasıdır. Bu çalışma, MÖ'nin kartografi, UA veri işleme ve jeoenformatik için faydalarını göstermiştir.

Keywords

İtalya , Yapay Sinir Ağı , Karar Ağacı , Coğrafi Veri , Ekoloji , Rastgele Orman , CBS , İtalya

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