Landsat sensor performance: history and current status

Markham, Brian L.; Storey, James C.; Williams, Darrel L.; Irons, James R.

doi:10.1109/tgrs.2004.840720

Cited by 281 publications

(148 citation statements)

References 13 publications

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“…This may explain the SH class in 2011 (Figure 3), in which two SH events were not detected and four NC events are classified as SH (and one TH). This poor performance mostly depends on the different radiometry of each scene, but can also be related to a degradation of the quality of the data recorded by the TM sensor [48], in accordance with the lower accuracy obtained also in 2009 and 2010. In addition, potential incomplete harvesting (e.g., single harvest jobs spread over two or more years) could have also affected the accuracy of the classification.…”

Section: Discussionsupporting

confidence: 60%

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

2015

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Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of OPEN ACCESS Remote Sens. 2015, 7 7810 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition.

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Section: Discussionsupporting

confidence: 60%

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

2015

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“…To examine this qualitatively, and to provide confidence in the local accuracy of the classification, classified MODIS active fire detections were compared with higher spatial resolution 30 m Landsat images. Landsat 5 images were used as they do not have the scan line corrector failure that reduced the amount of useable Landsat 7 image data acquired after May 2003 by 22% (Markham et al 2004). …”

Section: Classification Accuracy Assessmentmentioning

confidence: 99%

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Roy

Kumar

2016

International Journal of Digital Earth

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The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km 2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user's and producer's classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature. ARTICLE HISTORY

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“…The region also suffers from low Landsat acquisition frequency for the early Landsat sensors because of lack of ground station coverage in central Africa and limited onboard image storage capacity, reducing temporal coverage for the application of land change methods [30][31][32]. Furthermore, the Landsat ETM+ SLC-off acquisitions suffer from wedge shape gaps reducing 22% of the usable data after 2003 [28,33,34].…”

Section: Introductionmentioning

confidence: 99%

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet

Vodacek

2015

Remote Sensing

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Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing the Earth Science community. One such region is the Lake Kivu region in Central Africa. We developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988-2001 and 2001-2011 period was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. These multi-temporal datasets will be a valuable baseline for land use managers in the region OPEN ACCESSRemote Sens. 2015, 7 6684 interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts.

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Landsat sensor performance: history and current status

Cited by 281 publications

References 13 publications

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

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