Land-use configuration under traditional agriculture in the Kanto Plain, Japan: a historical GIS analysis

Resumen Este artículo presenta los pasos metodológicos para el análisis histórico del uso del suelo usando sistemas geográficos de información, SIG, y sensores remotos, SR. Así mismo, muestra la aplicación de estas técnicas a un estudio de caso para la producción de herramientas digitales que puedan servir para planear y administrar zonas agrícolas de una forma eficiente y sostenible. En el estudio se describe como fotos aéreas de 1973 a 2001, y una imagen de satélite, fueron usadas para generar mapas básicos, mapas de cambio de uso del suelo, así como de unidades administrativas agrícolas. En este estudio se muestra como los mapas y tablas además de otros análisis generados con los sistemas SIG-SR se convierten en una importante estructura analítica para la toma de decisiones en el sector rural. Palabras clave: Uso del suelo, planificación rural, SIG, sostenibilidad Abstract This paper discusses the use of geographic information systems, GIS, supported by remote sensing, RS, data as an important tool in the day to day decision making process on agriculture areas. The study is used to demonstrate how historical aerial photographs and a satellite image from 1973 to 2001 were used to produce land use, and land use change maps as well as maps of agriculture units for the study area that are later used for planning purposes of agriculture activities. The paper summarizes the methodological steps followed in the GIS analysis and the way that GIS-RS systems can be used in rural areas to plan and to manage day to day activities in agriculture areas under the philosophy of sustainable agriculture. Keywords: Sustainable agriculture, GIS, rural planning

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A RS and GIS analysis on land-use-cover change of Shanghai

IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2011) ◽

10.1049/cp.2011.0886 ◽

2011 ◽

Author(s):

Xie Yanan ◽

Wang Lishu ◽

Wang Bo ◽

Geng Hui

Keyword(s):

Land Use ◽

Gis Analysis ◽

Rs And Gis

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APLIKASI CITRA SATELIT PENGINDERAAN JAUH UNTUK PERCEPATAN IDENTIFIKASI TANAH TERLANTAR

BHUMI Jurnal Agraria dan Pertanahan ◽

10.31292/jb.v4i1.215 ◽

2018 ◽

Vol 4 (1) ◽

Author(s):

Westi Utami ◽

I Gede Kusuma Artika ◽

Aziz Arisanto

Keyword(s):

Land Use ◽

Land Cover ◽

Spatial Planning ◽

Supervised Classification ◽

Google Earth ◽

Land Use Land Cover ◽

Gis Analysis ◽

Maximum Likelihood Algorithm ◽

Abandoned Land ◽

And Control

Abstract: Identification and regulation of abandoned land needs to be intensified, to contribute identification of Objects of Agrarian Reform (TORA). Mapping of potential abandoned land carried out by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN) was considered not optimally implemented if compared between the setting targets with the achievements each year. Utilization of google earth imagery and Geographic Information System (GE and GIS) is expected accelerate mapping of potentialabandoned land. Google earth image was used to interpret land cover as the basis to identify land use. Land cover classification was done using supervised classification with maximum likelihood algorithm. The results showed that google earth image and GIS were able to present existing land use, and able to identifyland that has not been used as the permit rights granted. The result of interpretation and GIS analysis was expected to be used as tool to identify potential abandoned land, as the basis to regulate, accelerate and control abandoned land in Indonesia.Intisari: Identifikasi dan penertiban tanah terlantar perlu dilakukan secara intensif, salah satunya untuk memberikan sumbangan bagi Tanah Obyek Reforma Agraria (TORA). Pemetaan potensi tanah terlantar yang dilakukan Kementerian Agraria dan Tata Ruang/Badan Pertanahan Nasional (ATR/BPN) selama ini dirasa belum optimal apabila dibandingkan antara target yang ditetapkan dengan capaian setiap tahunnya. Pemanfaatan citra google earth dan Sistem Informasi Geografi diharapkan dapat membantu pekerjaanpemetaan potensi dan identifikasi tanah terlantar. Data yang digunakan adalah citra google earth untuk interpretasi tutupan tanah sebagai dasar untuk menentukan penggunaan tanah. Klasifikasi tutupan tanah pada penelitian ini menggunakan klasifikasi terselia (supervised) dengan algoritma maxsimum likelihood. Hasil penelitian menunjukkan bahwa pemanfaatan citra google earth dan SIG mampu menyajikan data penggunaan tanah eksisting terbaru, dan mampu mengidentifikasi tanah-tanah yang tidak dimanfaatkan sesuai arahan dalam izin hak yang diberikan. Hasil interpretasi dan analisis dengan SIG ini diharapkan dapat digunakan sebagai identifikasi obyek potensi tanah terlantar untuk kemudian dijadikan sebagai dasar dalam kegiatan penertiban tanah terlantar sehingga dapat membantu percepatan penertiban tanah terlantar di Indonesia.

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CHANGE DETECTION AND LAND USE / LAND COVER DATABASE UPDATING USING IMAGE SEGMENTATION, GIS ANALYSIS AND VISUAL INTERPRETATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w3-61-2015 ◽

2015 ◽

Vol XL-3/W3 ◽

pp. 61-65 ◽

Cited By ~ 6

Author(s):

J.-F. Mas ◽

R. González

Keyword(s):

Land Use ◽

Image Segmentation ◽

High Resolution ◽

Landsat Imagery ◽

Gis Analysis ◽

Spectral Indices ◽

Visual Interpretation ◽

Class Label ◽

High Resolution Imagery ◽

Spot Image

This article presents a hybrid method that combines image segmentation, GIS analysis, and visual interpretation in order to detect discrepancies between an existing land use/cover map and satellite images, and assess land use/cover changes. It was applied to the elaboration of a multidate land use/cover database of the State of Michoacán, Mexico using SPOT and Landsat imagery. The method was first applied to improve the resolution of an existing 1:250,000 land use/cover map produced through the visual interpretation of 2007 SPOT images. A segmentation of the 2007 SPOT images was carried out to create spectrally homogeneous objects with a minimum area of two hectares. Through an overlay operation with the outdated map, each segment receives the “majority” category from the map. Furthermore, spectral indices of the SPOT image were calculated for each band and each segment; therefore, each segment was characterized from the images (spectral indices) and the map (class label). In order to detect uncertain areas which present discrepancy between spectral response and class label, a multivariate trimming, which consists in truncating a distribution from its least likely values, was applied. The segments that behave like outliers were detected and labeled as “uncertain” and a probable alternative category was determined by means of a digital classification using a decision tree classification algorithm. Then, the segments were visually inspected in the SPOT image and high resolution imagery to assign a final category. The same procedure was applied to update the map to 2014 using Landsat imagery. As a final step, an accuracy assessment was carried out using verification sites selected from a stratified random sampling and visually interpreted using high resolution imagery and ground truth.

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