Global land cover classification by remote sensing: present capabilities and future possibilities

Townshend, J. R. G.; Justice, Christopher O.; Li, Wei; Gurney, C.; McManus, Jim

doi:10.1016/0034-4257(91)90016-y

Cited by 388 publications

(160 citation statements)

References 39 publications

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“…Each pixel contains the best observation values within the 8-day period with low view angle and the minimum amounts of clouds, haze or cloud shadow and aerosol loading [46]. Each image contains red (band 1: 620 to 670 nm) and NIR (band 2: 841 to 876 nm), which were used to calculate NDVI (Equation (1)) [47]: CSK is a constellation of four X-band SAR satellites that became fully operational in 2010 under the mandate of the Italian Space Agency (ASI). The CSK Huge Region mode provides standard products with a resolution of 100 meters [48].…”

Section: Modis Surface Reflectance 8-day Compositementioning

confidence: 99%

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

et al. 2014

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Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas.

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Section: Modis Surface Reflectance 8-day Compositementioning

confidence: 99%

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

et al. 2014

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“…Global data archives such as those based on imagery acquired by the Moderate-Resolution Imaging Spectroradiometer (MODIS), Système Probatoire d'Observation de la Terre VEGETATION (SPOT VGT) or the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) are key to understanding ecosystem changes from regional to global scales [4][5][6][7][8]. Without detailed information from global remote sensing systems, global environmental change research would be hampered from understanding Earth as a system [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

et al. 2012

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Detailed information from global remote sensing has greatly advanced our understanding of Earth as a system in general and of agricultural processes in particular. Vegetation monitoring with global remote sensing systems over long time periods is critical to gain a better understanding of processes related to agricultural change over long time periods. This specifically relates to sub-humid to semi-arid ecosystems, where agricultural change in grazing lands can only be detected based on long time series. By integrating data from different sensors it is theoretically possible to construct NDVI time series back to the early 1980s. However, such integration is hampered by uncertainties in the comparability between different sensor products. To be able to rely on vegetation trends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent across products. In this paper we analyzed several indicators of vegetation change for a range of agricultural systems in Inner Mongolia, China, and compared the results across different satellite archives. Specifically, we compared two of the prime NDVI archives-AVHRR OPEN ACCESSRemote Sens. 2012, 4 3365Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT) NDVI. Because a true accuracy assessment of long time series is not possible, we further compared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We found high similarities in interannual trends, and also in trends of the seasonal amplitude and integral between SPOT VGT and MODIS Terra (r > 0.9). However, we observed considerable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOT VGT. We detected similar discrepancies for trends based on phenological parameters, such as amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles. Inconsistencies were partially related to land cover and vegetation density. Different pre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introduced further uncertainties. Our results corroborate findings from other studies that vegetation trends derived from AVHRR GIMMS data not always reflect true vegetation changes. A more thorough understanding of the factors introducing uncertainties in AVHRR GIMMS time series is needed, and we caution against using AVHRR GIMMS data in regional studies without applying regional sensitivity analyses.

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“…These changes are occurring at a range of spatial scales from local to global and at temporal frequencies of days to millennia [2]. Land-cover changes play a significant role in the global carbon cycle, both as a source and a sink [3].…”

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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Global land cover classification by remote sensing: present capabilities and future possibilities

Cited by 388 publications

References 39 publications

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

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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