Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach

Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

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Detecção de formação erosiva (voçoroca) por meio de classificação hierárquica e por árvore de decisão

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702014000400055 ◽

2014 ◽

Vol 20 (4) ◽

pp. 1005-1026 ◽

Cited By ~ 3

Author(s):

Andrea Tedesco ◽

Alzir Felippe Buffara Antunes ◽

Luiz Octávio Oliani

Keyword(s):

Image Analysis ◽

Laser Scanner ◽

Regression Trees ◽

Classification And Regression Trees ◽

Airborne Laser Scanner ◽

Object Based Image Analysis ◽

Airborne Laser ◽

Object Based ◽

Classification And Regression ◽

Evolution Approach

Este estudo teve por objetivo verificar a possibilidade de reconhecimento de feições erosivas do tipo voçoroca utilizando análise orientada a objeto (OBIA - Object-Based Image Analysis). A área de estudo está localizada no município de Uberlândia, em Minas Gerais. Em função do objetivo, definiu-se uma rede semântica hierárquica multinível para representação do conhecimento especialista. Foram usados dados espectrais oriundos de imagem IKONOS e dados de intensidade e altimétricos provenientes de perfilamento com ALS (Airborne Laser Scanner). Os objetos foram gerados por meio de segmentação multirresolução (FNEA-Fractal Net Evolution Approach) aplicada aos dados espectrais e altimétricos. O reconhecimento das feições foi realizado por classificação hierárquica e por árvores de decisão (algoritmo CART-Classification And Regression Trees). A metodologia permitiu a identificação da relevância dos dados de entrada, parâmetros de segmentação e atributos a serem usados para classificação das voçorocas. Os resultados obtidos com a classificação hierárquica e com CART foram bastante similares, os quais evidenciaram a possibilidade do uso do método semiautomatizado a partir dos parâmetros previamente identificados e analisados.

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Land cover analysis using object based image analysis based on Landsat 8 OLI images in the city of Jember

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/747/1/012047 ◽

2021 ◽

Vol 747 (1) ◽

pp. 012047

Author(s):

E I Pangastuti ◽

Y Wijayanto

Keyword(s):

Image Analysis ◽

Land Cover ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Object Based Image Analysis ◽

Object Based ◽

The City

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MAPPING BURNT AREAS USING THE SEMI-AUTOMATIC OBJECT-BASED IMAGE ANALYSIS METHOD

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2020.v17.a3281 ◽

2020 ◽

Vol 17 (1) ◽

pp. 57

Author(s):

Hana Listi Fitriana ◽

Suwarsono Suwarsono ◽

Eko Kusratmoko ◽

Supriatna Supriatna

Keyword(s):

Image Analysis ◽

Geographical Area ◽

Remote Sensing Data ◽

Environmental Damage ◽

Landsat 8 ◽

Fast Method ◽

Object Based Image Analysis ◽

Object Based ◽

Burnt Areas ◽

The Impact

Forest and land fires in Indonesia take place almost every year, particularly in the dry season and in Sumatra and Kalimantan. Such fires damage the ecosystem, and lower the quality of life of the community, especially in health, social and economic terms. To establish the location of forest and land fires, it is necessary to identify and analyse burnt areas. Information on these is necessary to determine the environmental damage caused, the impact on the environment, the carbon emissions produced, and the rehabilitation process needed. Identification methods of burnt land was made both visually and digitally by utilising satellite remote sensing data technology. Such data were chosen because they can identify objects quickly and precisely. Landsat 8 image data have many advantages: they can be easily obtained, the archives are long and they are visible to thermal wavelengths. By using a combination of visible, infrared and thermal channels through the semi-automatic object-based image analysis (OBIA) approach, the study aims to identify burnt areas in the geographical area of Indonesia. The research concludes that the semi-automatic OBIA approach based on the red, infrared and thermal spectral bands is a reliable and fast method for identifying burnt areas in regions of Sumatra and Kalimantan.

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Changed Detection of Landsat 8 Imagery using Object Based Image Analysis with Particle Swarm Optimization

International Journal of Intelligent Enterprise ◽

10.1504/ijie.2020.10020950 ◽

2020 ◽

Vol 7 (1) ◽

pp. 1

Author(s):

Goutam Saha ◽

Amitabha Nath ◽

Amos Bortiew

Keyword(s):

Image Analysis ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Landsat 8 ◽

Swarm Optimization ◽

Object Based Image Analysis ◽

Object Based

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Winter Wheat Mapping Using Landsat 8 Images and Geographic Object-Based Image Analysis

Transactions of the ASABE ◽

10.13031/trans.11933 ◽

2017 ◽

Vol 60 (3) ◽

pp. 625-633

Author(s):

Tengfei Su ◽

Shengwei Zhang

Keyword(s):

Image Analysis ◽

Winter Wheat ◽

Feature Reduction ◽

Classification Model ◽

Feature Analysis ◽

Landsat 8 ◽

Scale Selection ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object

Abstract. Winter wheat is a major food source in many areas, so it is necessary to construct an effective approach for its monitoring based on satellite data. By taking advantage of geographic object-based image analysis (GEOBIA), a winter wheat classification framework was established. Two stages, which included scale selection and feature analysis, were incorporated into the new approach. The scale selection stage was implemented based on an unsupervised method, so human intervention for tuning the scale parameter of image segmentation can be largely saved. The feature analysis stage was performed on the basis of a random forest classification model, and in the experiment this step allowed for feature reduction, which was validated to be beneficial to the classification performance. Keywords: Feature analysis, GEOBIA, Scale selection, Winter wheat.

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