Phân tích xu hướng phát triển đô thị thành phố Cần Thơ giai đoạn 2004 - 2019

Nghiên cứu được thực hiện nhằm phân tích và đánh giá xu hướng phát triển đô thị thành phố (TP.) Cần Thơ năm 2004 và 2019 từ đó hỗ trợ các địa phương định hướng phát triển vùng đô thị tại các quân/huyện TP. Cần Thơ. Ảnh Landsat được phân loại bằng thuật toán xác suất cực đại (Maximum Likelihood Classification-MCL) và phân tích điểm nóng (Hotspot) theo dõi xu hướng đô thị hóa. Kết quả nghiên cứu cho thấy đô thị tập trung chủ yếu tại 4 quận Cái Răng, Ninh Kiều, Bình Thủy và Thốt Nốt với tổng diện tích năm 2004 là 6.400,2 héc-ta (ha) và năm 2019 là 16.007,0 ha. Tỷ lệ đô thị của TP. Cần Thơ tăng từ 4,45% năm 2004 lên 11,12% năm 2019. Tốc độ đô thị hóa trung bình năm của toàn thành phố là 0,43%, cao nhất là quận Ninh Kiều với 1,52% và thấp nhất là 0,19% ở huyện Cờ Đỏ. Mật độ đô thị quận Ninh Kiều cao nhất toàn thành phố với 45,9% năm 2004 và 65,62% năm 2019. Đô thị hóa phát triển theo hướng (1) dọc theo sông Hậu hình thành một đô thị dạng chuỗi, (2) theo sông Cần Thơ về phía Tây Nam và (3) theo hướng các tuyến quốc lộ chính.

Rabi Groundnut Area Estimation using Synthetic Aperture Radar (SAR) in Thiruvannamalai District of Tamil Nadu

Background: Groundnut, commonly known as peanut, is a significant oil, food and feed legume crop grown in all seasons in Tamil Nadu, including kharif, rabi and summer and it is cultivated both under irrigated and rainfed conditions in all the seasons at Thiruvannamlai district. One of the most important applications of remote sensing in agriculture is a Crop Acreage and Production Estimation (CAPE). The CAPE’s main goal is to estimate crop acreage and production of important crops, so that advanced food production, distribution and supply data were achieved. Methods: Multi-temporal Sentinel 1A SAR IW- GRD data with 20 m spatial resolution and 12 days temporal resolution of Vertical - Horizontal (V-H) polarization were downloaded for the period of 4th October 2020 to 8th January 2021 to have the full coverage during the crop growth period in the study area used for this work. Crop backscattering and multi-temporal features were extracted from MAPscape 5.2 automated pre-processing tool and its classified using supervised maximum likelihood classification for groundnut acreage extraction for Thiruvannamalai district. Result: The rabi groundnut area of Thiruvannamalai district of Tamil Nadu was estimated using SAR Sentinel-1A data as 32298 ha with a higher accuracy percentage of 87.4 and kappa coefficient of 0.75.

Remote Sensing Data Classification Technique: A Review

Remote sensing is the prominent technology to study the ecology of the earth. Classification is a commonly used technique for quantitative analysis of remote sensing image data. It is based on the concept of segmentation of spectral regions into regions that can be associated with a soil cover class of interest for a particular application. As an advanced remote sensing tool, Hyperspectral remote sensing technology has been studied in many applications such as geology, topography, biology, soil science, hydrology, plants and ecosystems, atmospheric science. In this paper, Supervised Decision tree; Minimum distance; Maximum likelihood classification; Parallelepiped; K-nearest neighbor; and Unsupervised K-mean; & ISODATA algorithm are reviewed. This review is helpful to the researchers who are studying this emerging field i.e. HRS.

ANALISIS PERBANDINGAN METODE NORMALIZED VEGETATION INDEX DAN MAXIMUM LIKELIHOOD CLASSIFICATION UNTUK ANALISIS RUANG TERBUKA HIJAU (Studi Kasus di Kota Pekalongan, Jawa Tengah)

Ruang Terbuka Hijau (RTH) adalah suatu tempat yang luas dan terbuka yang dimaksudkan untuk penghijauan suatu kota, di mana di dalamnya ditumbuhi pepohonan. Dalam analisis ruang terbuka hijau dapat menggunakan beberapa metode, di antaranya yaitu metode Normalized Difference Vegetation Index (NDVI) dan metode Maximum Likelihood Classification. Tujuan penelitian ini untuk mengetahui perbedaan hasil dari analisis metode NDVI dan Maximum Likelihood Classification yang digunakan untuk mengetahui ruang terbuka hijau di Kota Pekalongan. Metode yang digunakan pada penelitian ini yaitu dengan menggunakan metode NDVI dan metode Maximum Likelihood Classification. Data yang digunakan yaitu Citra Landsat 8 OLI. Pengolahan data menggunakan software Arcgis 10.3. Hasil dari pengolahan berupa peta ruang terbuka hijau dari masing - masing metode. Secara kuantitatif dari hasil perhitungan luas metode NDVI, luas permukiman sebesar 3.016,53 ha, persawahan 609,39 ha, hutan kota 573,3 ha, dan badan air seluas 482,04 ha. Sedangkan untuk metode Maximum Likelihood Classification didapatkan hasil luas permukiman 2.278,26 ha, persawahan 1.141,83 ha, hutan kota 738,18 ha, dan badan air seluas 522,99 ha. Berdasarkan luasan RTH terhadap luas Kota Pekalongan, pada metode NDVI sebesar 25,2%, sedangkan untuk metode Maximum Likelihood Classification sebesar 40,1%. Dari hasil analisis diperoleh perbedaan luasan yang cukup signifikan yaitu pada luasan persawahan dan permukiman. Perbedaan hasil analisis terjadi akibat perbedaan klasifikasi warna citra pada saat pengolahan data.

ANALISIS CITRA SATELIT MENGGUNAKAN METODE SUPERVISED CLASSIFICATION MAXIMUM LIKELIHOOD PADA PEMETAAN TUTUPAN LAHAN DI TAMAN NASIONAL BUKIT BARISAN SELATAN (TNBBS)

Taman nasional merupakan kawasan pelestarian alam yang mempunyai fungsi sebagai perlindungan sistem penyangga kehidupan, pengawetan keanekaragaman jenis tumbuhan dan satwa serta ekosistemnya yang dikelola dengan sistem zonasi, yang terdiri dari zona inti, zona pemanfaatan, dan zona lain sesuai keperluan (Undang-undang No. 5 Tahun 1990). Namun sejauh ini masih sering muncul permasalahan yang terjadi hampir disetiap taman nasional, seperti perburuan satwa liar, illegal loging, perambahan, pencurian kayu/tumbuhan langka dan tata batas kawasan. Salah satu taman nasional yang ada di Indonesia yang tidak luput dari permasalahan adalah Taman Nasional Bukit Barisan Selatan. Penelitian ini dilakukan untuk mengetahui perubahan tutupan lahan yang terjadi di TNBBS. Setelah dilakukan penelitian dengan menggunakan software ArcGIS dengan metode supervised maximum likelihood classification diketahui bahwa dalam kurun tahun 2015 sampai 2018 terjadi peningkatan luas pemukiman sebesar 0,32% dan penurunan luas vegetasi tinggi (kawasan hutan) hingga 7,99%. Lahan terbuka mengalami penurunan luas 0,79%, lahan pertanian mengalami penurunan hingga 1,12%, tutupan lahan berupa rumput/semak meningkat hingga 4,58% dan tutupan lahan vegetasi sedang meningkat hingga 5,03%.

Updated inventory of glacier ice in New Zealand based on 2016 satellite imagery

Copyright © The Author(s), 2020. Published by Cambridge University Press. The only complete inventory of New Zealand glaciers was based on aerial photography starting in 1978. While there have been partial updates using 2002 and 2009 satellite data, most glaciers are still represented by the 1978 outlines in contemporary global glacier databases. The objective of this project is to establish an updated glacier inventory for New Zealand. We have used Landsat 8 OLI satellite imagery from February and March 2016 for delineating clean glaciers using a semi-Automatic band ratio method and debris-covered glaciers using a maximum likelihood classification. The outlines have been checked against Sentinel-2 MSI data, which have a higher resolution. Manual post processing was necessary due to misclassifications (e.g. lakes, clouds), mapping in shadowed areas, and combining the clean and debris-covered parts into single glaciers. New Zealand glaciers cover an area of 794 ± 34 km2 in 2016 with a debris-covered area of 10%. Of the 2918 glaciers, seven glaciers are >10 km2 while 71% is <0.1 km2. The debris cover on those largest glaciers is >40%. Only 15 glaciers are located on the North Island. For a selection of glaciers, we were able to calculate the area reduction between the 1978 and 2016 inventories.

Updated inventory of glacier ice in New Zealand based on 2016 satellite imagery

Copyright © The Author(s), 2020. Published by Cambridge University Press. The only complete inventory of New Zealand glaciers was based on aerial photography starting in 1978. While there have been partial updates using 2002 and 2009 satellite data, most glaciers are still represented by the 1978 outlines in contemporary global glacier databases. The objective of this project is to establish an updated glacier inventory for New Zealand. We have used Landsat 8 OLI satellite imagery from February and March 2016 for delineating clean glaciers using a semi-Automatic band ratio method and debris-covered glaciers using a maximum likelihood classification. The outlines have been checked against Sentinel-2 MSI data, which have a higher resolution. Manual post processing was necessary due to misclassifications (e.g. lakes, clouds), mapping in shadowed areas, and combining the clean and debris-covered parts into single glaciers. New Zealand glaciers cover an area of 794 ± 34 km2 in 2016 with a debris-covered area of 10%. Of the 2918 glaciers, seven glaciers are >10 km2 while 71% is <0.1 km2. The debris cover on those largest glaciers is >40%. Only 15 glaciers are located on the North Island. For a selection of glaciers, we were able to calculate the area reduction between the 1978 and 2016 inventories.

COMPARAÇÃO DE MÉTODOS DE CLASSIFICAÇÃO PARA O MAPEAMENTO DA COBERTURA DA TERRA NO SETOR NORTE DA ILHA DO MARANHÃO

A zona costeira se estende das planícies costeiras até a borda externa das plataformas continentais. A GEOBIA (Geographic Object-Based Image Analysis) é um método eficaz na análise de imagens de alta resolução, incluindo fotointerpretação e classificação de características da paisagem. O método de classificação supervisionada consiste em um conhecimento prévio de algumas áreas a serem estudadas, o que permite amostras de confiáveis. Assim, o presente estudo tem como objetivo principal comparar o método de classificação supervisionada pixel a pixel com o método de GEOBIA, buscando o mapeamento da cobertura da terra no setor Norte da Ilha do Maranhão. O algoritmo classificador utilizado foi o Maximum Likelihood Classification. O método de Classificação Supervisionada apresentou um valor de Kappa de 0,8469, Exatidão Global de 0,8950 e Variância Kappa de 0,0009295. Para a GEOBIA apresentou um valor de Kappa de 0,9410, Exatidão Global de 0,9600 e Variância Kappa de 0,0004075. O sensoriamento remoto se mostra extremamente eficiente em pesquisas relacionadas ao uso e ocupação da terra. Os resultados do método da Classificação Supervisionada, apesar de suas limitações, mostraram resultados satisfatórios quando comparados com os resultados do método da GEOBIA, o que torna interessante sua aplicação para iniciativas de mapeamento em diversos municípios brasileiros.