ANALISIS PERUBAHAN KERAPATAN VEGETASI MENGGUNAKAN APLIKASI GOOGLE EARTH ENGINE DENGAN MEMANFAATKAN CITRA SENTINEL-2 LEVEL 2A (Studi Kasus: Kab. Nganjuk, Jawa Timur)

Kabupaten Nganjuk banyak mengalami perubahan lahan pertanian menjadi lahan industri. Mulai tahun 2017 adanya pembangunan Bendungan Semantok, kemudian tahun 2019 adanya pengembangan kawasan industri dan pembangunan seksi jalan tol Solo - Ngawi dan seksi Ngawi – Kertosono. Vegetasi memainkan peran penting yang secara langsung atau tidak langsung dapat mempengaruhi udara. Kerapatan vegetasi menciptakan kenyamanan dan kesejukan penggunaan lahan. Tinggi rendahnya kerapatan vegetasi dapat ditentukan dengan menggunakan indeks vegetasi Normalized Difference Vegetation Index (NDVI), yang merupakan transformasi citra spektral untuk vegetasi. Adanya perubahan kerapatan vegetasi di Kabupaten Nganjuk mengakibatkan dilakukannya penelitian dengan memanfaatkan citra satelit Sentinel-2 Level 2A tahun 2019, 2020 dan 2021 menggunakan Google Earth Engine. Hasil pengolahan citra satelit Sentinel-2 Level 2A tahun 2019, 2020 dan 2021 menunjukkan adanya perubahan kerapatan vegetasi yang mengalami penurunan luas area vegetasi sebesar 435,627 ha

Leaf Identification Using Fourier Descriptors and Other Shape Features

Abstract Leaf identification is a challenging research. So far, many approaches have been proposed. In this paper, an approach that combines Fourier descriptors with other shape features was investigated to identify 100 hundred kinds of leaves. The result shows that the combination of Fourier descriptors and several other shape features can be used to identify leaves with the accuracy rate of 88%. This result indicates that this approach is a promising way for identifying leaves

PERBANDINGAN METODE CROPPING PADA SEBUAH CITRA UNTUK PENGAMBILAN MOTIF TERTENTU PADA KAIN SONGKET SUMATERA BARAT

Pada saat proses pengolahan citra dimana kita hanya membutuhkan bagian tertentu saja dari sebuah citra sesuai kebutuhan yang disebut dengan Region of Interest (ROI), guna mendapatkan itu maka dalam pemrosesan dilakukan sebuah proses cropping. Cropping banyak dilakukan oleh para peneliti terutama yang meneliti pada bidang image processing guna untuk melakukan pengolahan data pada sebuah citra, hasil proses cropping pada sebuah citra biasanya dilakukan untuk memudahkan peneliti fokus pada sesuatu obyek yang diperlukan saja. Pada penelitian ini adalah melakukan perbandingan metode cropping yang sudah ada untuk  mendapatkan suatu motif  yang terdapat pada sebuah citra kain songket Sumatera Barat. Pada penelitian ini menggunakan metode cropping rectangle, square, circle, ellipse, polygon dan diuji dengan menggunakan bahasa pemrograman Matlab. Hasil perbandingan 5 metode cropping untuk pengambilan motif tertentu pada citra songket dengan 5 sampel citra songket yang berbeda, menunjukan bahwa hasil terbaik diperoleh dengan meggunakan metode polygon. Metode polygon dapat menjangkau titik koordinat tertentu pada sebuah citra songket, sehingga hasil cropping lebih baik dan  motif  lain yang  ikut terbawa pada saat proses cropping dapat dikurangi.   Dimension Link : 10.3513

Experiments of Distance Measurements in a Foliage Plant Retrieval System

One of important components in an image retrieval system is selecting a distance measure to compute rank between two objects. In this paper, several distance measures were researched to implement a foliage plant retrieval system. Sixty kinds of foliage plants with various leaf color and shape were used to test the performance of 7 different kinds of distance measures: city block distance, Euclidean distance, Canberra distance, Bray-Curtis distance, x2 statistics, Jensen Shannon divergence and Kullback Leibler divergence. The results show that city block and Euclidean distance measures gave the best performance among the others.Comment: 14 pages, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 5, No. 2, June, 201

Classify the plant species based on lobes, sinuses and margin

This paper proposed a novel approach to cluster the species of plants based on their lobes, sinuses and margin. Firstly, all the boundary points in the clockwise or anticlockwise direction were selected. Then, an estimated centre point for leaf boundary points was used to compute the distance between the leaf boundary points and centre point. Next, the peaks and valleys from the distance found stated above were located where peaks represent lobes and valleys represent the sinuses. The number of peak and valleys is calculated to cluster the plant according to the rule-based method. From the results obtained, the accuracy for the plant clustering is up to 100 percent

A New Leaf Venation Detection Technique for Plant Species Classification

This paper presents a novel approach to classify the leaf shape and to identify plant species using venation detection. The proposed approach consists of five main steps to extract the leaf venation including canny edge detection, remove leaf boundary, extract curve, and produce hue normalization image and image fusion. Moreover, to localize the edge direction efficiently, the lines that extracted from pre-processing, are further divided into smaller segments. Thirty-two leaf images of Malaysian plants are analysed and evaluated with two different datasets, Flavia and Acer. The best accuracy is obtained by 99.3% and 91.06% for Flavia and Acer datasets respectively. Experimental results show the effectiveness of the proposed approach for shape recognition with high accuracy. Keywords: Leaf Venation; plant species; features extraction; features selection; classification