Improved land cover mapping using aerial photographs and satellite images

AbstractManual Land Cover Mapping using aerial photographs provides sufficient level of resolution for detailed vegetation or land cover maps. However, in some cases it is not possible to achieve the desired information over large areas, for example from historical data where the quality and amount of available images is definitely lower than from modern data. The use of automated and semiautomated methods offers the means to identify the vegetation cover using remotely sensed data. In this paper automated methods were tested on aerial photographs and satellite images to extract better and more reliable information about vegetation cover. These testswere performed by using automated analysis of LANDSAT7 images (with and without the surface model of the Shuttle Radar Topography Mission (SRTM)) and two temporally similar aerial photographs. The spectral bands were analyzed with supervised (maximum likelihood) methods. In conclusion, the SRTM and the combination of two temporally similar aerial photographs from earlier years were useful in separating the vegetation cover on a floodplain area. In addition the different date of the vegetation season also gave reliable information about the land cover. High quality information about old and present vegetation on a large area is an essential prerequisites ensuring the conservation of ecosystems

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Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map

Remote Sensing ◽

10.3390/rs12091418 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1418

Author(s):

Runmin Dong ◽

Cong Li ◽

Haohuan Fu ◽

Jie Wang ◽

Weijia Li ◽

...

Keyword(s):

Land Cover ◽

Training Data ◽

Training Dataset ◽

Land Cover Mapping ◽

Remotely Sensed Data ◽

Large Area ◽

National Scale ◽

Substantial Progress ◽

Efficient Learning ◽

Land Cover Maps

Substantial progress has been made in the field of large-area land cover mapping as the spatial resolution of remotely sensed data increases. However, a significant amount of human power is still required to label images for training and testing purposes, especially in high-resolution (e.g., 3-m) land cover mapping. In this research, we propose a solution that can produce 3-m resolution land cover maps on a national scale without human efforts being involved. First, using the public 10-m resolution land cover maps as an imperfect training dataset, we propose a deep learning based approach that can effectively transfer the existing knowledge. Then, we improve the efficiency of our method through a network pruning process for national-scale land cover mapping. Our proposed method can take the state-of-the-art 10-m resolution land cover maps (with an accuracy of 81.24% for China) as the training data, enable a transferred learning process that can produce 3-m resolution land cover maps, and further improve the overall accuracy (OA) to 86.34% for China. We present detailed results obtained over three mega cities in China, to demonstrate the effectiveness of our proposed approach for 3-m resolution large-area land cover mapping.

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Monitoring changes in land cover/use by CORINE methodology using aerial photographs and IKONOS satellite images: a case study for Kemer, Antalya, Turkey

International Journal of Remote Sensing ◽

10.1080/01431160802639723 ◽

2009 ◽

Vol 30 (7) ◽

pp. 1771-1778 ◽

Cited By ~ 6

Author(s):

N. Kemal Sönmez ◽

Işin Onur ◽

Mustafa Sari ◽

Derya Maktav

Keyword(s):

Land Cover ◽

Satellite Images ◽

Aerial Photographs

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Investigating the effects of ensemble classification on remotely sensed data for land cover mapping

2010 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2010.5649044 ◽

2010 ◽

Cited By ~ 2

Author(s):

Bolanle Abe ◽

Anthony Gidudu ◽

Tshilidzi Marwal

Keyword(s):

Land Cover ◽

Remotely Sensed ◽

Ensemble Classification ◽

Land Cover Mapping ◽

Remotely Sensed Data

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Large-Scale Land Cover Mapping of Satellite Images Using Ensemble of Random Forests — IEEE Data Fusion Contest 2020 Track 1

10.1109/igarss39084.2020.9547210 ◽

2020 ◽

Author(s):

Huijun Chen ◽

Wei Liu ◽

Changlin Xiao ◽

Rongjun Qin

Keyword(s):

Land Cover ◽

Data Fusion ◽

Random Forests ◽

Large Scale ◽

Satellite Images ◽

Land Cover Mapping ◽

Large Scale Land

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Measuring and monitoring land cover, land use, and vegetation characteristics

Remote Sensing for Ecology and Conservation ◽

10.1093/oso/9780199219940.003.0011 ◽

2010 ◽

Author(s):

Ned Horning ◽

Julie A. Robinson ◽

Eleanor J. Sterling ◽

Woody Turner ◽

Sacha Spector

Keyword(s):

Land Use ◽

Land Cover ◽

Deciduous Forest ◽

Land Cover Classification ◽

Remotely Sensed ◽

Land Cover Mapping ◽

Remotely Sensed Data ◽

Soil P ◽

Vegetation Characteristics ◽

Classification Project

In terrestrial biomes, ecologists and conservation biologists commonly need to understand vegetation characteristics such as structure, primary productivity, and spatial distribution and extent. Fortunately, there are a number of airborne and satellite sensors capable of providing data from which you can derive this information. We will begin this chapter with a discussion on mapping land cover and land use. This is followed by text on monitoring changes in land cover and concludes with a section on vegetation characteristics and how we can measure these using remotely sensed data. We provide a detailed example to illustrate the process of creating a land cover map from remotely sensed data to make management decisions for a protected area. This section provides an overview of land cover classification using remotely sensed data. We will describe different options for conducting land cover classification, including types of imagery, methods and algorithms, and classification schemes. Land cover mapping is not as difficult as it may appear, but you will need to make several decisions, choices, and compromises regarding image selection and analysis methods. Although it is beyond the scope of this chapter to provide details for all situations, after reading it you will be able to better assess your own needs and requirements. You will also learn the steps to carry out a land cover classification project while gaining an appreciation for the image classification process. That said, if you lack experience with land cover mapping, it always wise to seek appropriate training and, if possible, collaborate with someone who has land cover mapping experience (Section 2.3). Although the terms “land cover” and “land use” are sometimes used interchangeably they are different in important ways. Simply put, land cover is what covers the surface of the Earth and land use describes how people use the land (or water). Examples of land cover classes are: water, snow, grassland, deciduous forest, or bare soil.

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Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES)

Geoscientific Model Development ◽

10.5194/gmd-12-179-2019 ◽

2019 ◽

Vol 12 (1) ◽

pp. 179-193 ◽

Cited By ~ 11

Author(s):

Chantelle Burton ◽

Richard Betts ◽

Manoel Cardoso ◽

Ted R. Feldpausch ◽

Anna Harper ◽

...

Keyword(s):

Land Use ◽

Land Use Change ◽

Land Cover ◽

Vegetation Cover ◽

Land Surface ◽

Vegetation Dynamics ◽

Fire Disturbance ◽

Substantial Contribution ◽

Surface Model ◽

The Impact

Abstract. Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from −66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.

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Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping

Remote Sensing ◽

10.3390/rs11161907 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1907 ◽

Cited By ~ 3

Author(s):

Mohammad Mardani ◽

Hossein Mardani ◽

Lorenzo De Simone ◽

Samuel Varas ◽

Naoki Kita ◽

...

Keyword(s):

Machine Learning ◽

Developing Countries ◽

Open Access ◽

Land Cover ◽

Satellite Images ◽

National Level ◽

Google Earth ◽

Support Vector ◽

Land Cover Mapping ◽

Sustainable Food

In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets.

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