Land cover classification with AVHRR multichannel composites in northern environments

Cihlar, J.; Ly, Hung; Xiao, Qinghan

doi:10.1016/0034-4257(95)00210-3

Cited by 64 publications

(32 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cihlar [6], such as using NOAA data for land cover classification in Canada, the NDVI, channel a (0.58 ~ 0.68 (including m) albedo C1, channel 2 (0.725 ~ 1.0 (including m) albedo C2 and channel 4 m (10.3 ~ 11.3 (including) compares the C4 bright temperature, the results show that the NDVI is the best of the single feature classification performance, followed by C2, C1, C4. Also prove that NDVI used together with other features, can get a higher classification accuracy.…”

Section: A From Noaa Ndvi To Modis Evimentioning

confidence: 99%

“…MODIS land cover/land use change research team in addition to using enhanced vegetation index in classification, also used the channel 1 ~ 7 albedo data, the surface temperature data, land and water, ice and snow marking data, terrain elevation data and texture information of optical channel [7]. Cihlar [6], such as research has shown that the introduction of secondary data to identify water bodies, can avoid the classification of water area is narrow; The authors also emphasize the temperature data in a wide range of research areas (latitude span large area) can play a greater role. Palace [12] using MODIS NDVI data -such as climbing to land cover classification research of northeastern China, after the introduction of the surface temperature data, a year a ripe crops with high coverage grassland, deciduous coniferous forest and deciduous broad-leaved mixed phenomenon in a certain extent solved.…”

Section: B Secondary Datamentioning

confidence: 99%

“…Classification results such as Cihlar [6] using TM AVHRR classification results of inspection: classification of the TM image into the same projection and AVHRR, with minimum distance method and TM pixels re-sampling into the same space with AVHRR classification results. MODIS soil use and land cover change research group adopts STEP (System for Terrestrial Ecosystem Parameterization) System for the training sample collection, test and the comparison between results of classification algorithm.…”

Section: About the Land Cover Classification Systemmentioning

confidence: 99%

See 2 more Smart Citations

Review of Land Cover Classification Based on Remote Sensing Data

Wang¹,

He²,

Xiang³

et al. 2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

View full text Add to dashboard Cite

Abstract-Land use and land cover change is one of the focuses in the study of global change in recent year. Land cover characteristics, at the global and regional scale, are very important for evaluating global environment status and simulating global environment scenario in the future. In order to provide some useful reference for this kind of study in China, the paper introduces the most recent progresses of international researches on land cover classification from four aspects including remote sensing data, classification method, land cover classification system and outcome comparison. The author believes making a classification system not only reflects the Chinese condition but also being compatible with other international systems, building a public and updating land use/cover database, are the most important things to be solved.

show abstract

Section: A From Noaa Ndvi To Modis Evimentioning

confidence: 99%

Section: B Secondary Datamentioning

confidence: 99%

Section: About the Land Cover Classification Systemmentioning

confidence: 99%

See 1 more Smart Citation

Review of Land Cover Classification Based on Remote Sensing Data

Wang¹,

He²,

Xiang³

et al. 2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

View full text Add to dashboard Cite

show abstract

“…An obvious approach is to assign the pixel to the single largest cover type within the pixel (e.g. Cihlar et al 1996, Hansen et al 2000. This can be accomplished with the aid of ne resolution maps where these are available.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Land Cover Mapping of Large Areas from Satellites: Status and Research Priorities

Cihlar¹

2000

View full text Add to dashboard Cite

Abstract. Although land cover mapping is one of the earliest applications of remote sensing technology, routine mapping over large areas has only relatively recently come under consideration. This change has resulted from new information requirements as well as from new developments in remote sensing science and technology. In the near future, new data types will become available that will enable marked progress to be made in land cover mapping over large areas at a range of spatial resolutions. This paper is concerned with mapping strategies based on 'coarse' and ' ne' resolution satellite data as well as their combinations. The status of land cover mapping is discussed in relation to requirements, data sources and analysis methodologies-including pixel or scene compositing, radiometric corrections, classi cation and accuracy assessment. The overview sets the stage for identifying research priorities in data pre-processingand classi cation in relation to forthcoming improvements in data sources as well as new requirements for land cover information. Introduction and objectiveLand cover, i.e. the composition and characteristics of land surface elements, is key environmental information. It is important for many scienti c, resource management and policy purposes and for a range of human activities. It is an important determinant of land use and thus of value of land to the society. Land cover varies at a range of spatial scales from local to global, and at temporal frequencies of days to millennia. As the need for environmental planning and management became important, an accompanying call for land cover information emerged in parallel.Land cover mapping is a product of the development of remote sensing, initially through aerial photography (Colwell 1960 ). This is because 'viewing' large areas repeatedly is necessary for acquiring information about land cover. For the same reason, land cover mapping has been perhaps the most widely studied problem employing satellite data, beginning with Landsat 1. However, most of the studies using ' ne' resolution data (i.e. 20-100 m) were methodological in nature, exploring various information extraction techniques and applying these over limited areas. Applications over large areas were hampered by the lack of suitable technology, an absence of a user community with a strong need for such information, a lack of appropriate analysis methodologies, and the cost of data. Thus, at the global level, land cover data sets compiled from ground surveys or various national sources (Mathews 1983, Olson et al. 1983 were, for a number of years, the major source of information.

show abstract

“…For instance, NDVI which is the normalized ratio of red and NIR spectral reflectance (NDVI = (NIR − RED)/(NIR + RED)), is one of the most widely and frequently used VIs in remote sensing research. As such, the existing global NDVI data derived from the NOAA's Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite systems provide routine monitoring of terrestrial ecosystems and vegetation changes [9,10]. NDVI has also been shown to be related to a number of plant physiological and biophysical parameters such as leaf area index (LAI), green vegetation density, biomass, chlorophyll content and photosynthetic activity as well as water content and overall vegetation health [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Vegetation Phenological Cycles in Two Different Semi-Arid Environmental Settings Using a Ground-Based NDVI System: A Potential Approach to Improve Satellite Data Interpretation

et al. 2010

View full text Add to dashboard Cite

Abstract:In semi-arid environmental settings with sparse canopy covers, obtaining remotely sensed information on soil and vegetative growth characteristics at finer spatial and temporal scales than most satellite platforms is crucial for validating and interpreting satellite data sets. In this study, we used a ground-based NDVI system to provide continuous time series analysis of individual shrub species and soil surface characteristics in two different semi-arid environmental settings located in the Great Basin (NV, USA). The NDVI system was a dual channel SKR-1800 radiometer that simultaneously measured incident solar radiation and upward reflectance in two broadband red and near-infrared channels comparable to Landsat-5 TM band 3 and band 4, respectively. The two study sites identified as Spring Valley 1 site (SV1) and Snake Valley 1 site (SNK1) were chosen for having different species composition, soil texture and percent canopy cover. NDVI time-series of greasewood (Sarcobatus vermiculatus) from the SV1 site allowed for clear distinction between the main phenological stages of the entire growing season during the period from January to November, 2007. NDVI time series values were significantly different between sagebrush (Artemisia tridentata) and rabbitbrush (Chrysothamnus viscidiflorus) at SV1 as well as between the two bare soil types at the two sites. Greasewood NDVI from the SNK1 site produced significant correlations with chlorophyll index (r = 0.97), leaf area index (r = 0.98) and leaf xylem water potential (r = 0.93). Whereas OPEN ACCESSRemote Sens. 2010, 2 991 greasewood NDVI from the SV1 site produced lower correlations (r = 0.89, r = 0.73), or non significant correlations (r = 0.32) with the same parameters, respectively. Total percent cover was estimated at 17.5% for SV1 and at 63% for SNK1. Results from this study indicated the potential capabilities of using this ground-based NDVI system to extract spatial and temporal details of soil and vegetation optical properties not possible with satellite derived NDVI.

show abstract

Land cover classification with AVHRR multichannel composites in northern environments

Cited by 64 publications

References 18 publications

Review of Land Cover Classification Based on Remote Sensing Data

Review of Land Cover Classification Based on Remote Sensing Data

Land Cover Mapping of Large Areas from Satellites: Status and Research Priorities

Monitoring Vegetation Phenological Cycles in Two Different Semi-Arid Environmental Settings Using a Ground-Based NDVI System: A Potential Approach to Improve Satellite Data Interpretation

Contact Info

Product

Resources

About