Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

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Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

Remote Sensing ◽

10.3390/rs11243023 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3023 ◽

Cited By ~ 9

Author(s):

Shuai Xie ◽

Liangyun Liu ◽

Xiao Zhang ◽

Jiangning Yang ◽

Xidong Chen ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Vegetation Index ◽

Time Series Data ◽

Land Cover Classification ◽

Google Earth ◽

Thematic Mapper ◽

Series Data ◽

Land Cover Mapping ◽

Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

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Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics

2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) ◽

10.1109/multi-temp.2017.8035199 ◽

2017 ◽

Cited By ~ 1

Author(s):

Caitlin Kontgis ◽

Michael S. Warren ◽

Samuel W. Skillman ◽

Rick Chartrand ◽

Daniela I. Moody

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Agricultural Land ◽

Time Series Data ◽

Series Data ◽

The Tropics

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Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

Geography and Natural Resources ◽

10.1134/s1875372811010021 ◽

2011 ◽

Vol 32 (1) ◽

pp. 9-15 ◽

Cited By ~ 6

Author(s):

Kaishan Song ◽

Zongmin Wang ◽

Qingfeng Liu ◽

Dianwei Liu ◽

V. V. Ermoshin ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

River Basin ◽

Time Series Data ◽

Series Data ◽

Amur River Basin ◽

Land Use Land Cover ◽

Amur River ◽

The Amur River

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A hybrid approach to mapping land-use modification and land-cover transition from MODIS time-series data: A case study from the Bolivian seasonal tropics

Remote Sensing of Environment ◽

10.1016/j.rse.2010.09.007 ◽

2011 ◽

Vol 115 (2) ◽

pp. 353-372 ◽

Cited By ~ 26

Author(s):

Daniel J. Redo ◽

Andrew C. Millington

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Time Series Data ◽

Hybrid Approach ◽

Series Data

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Land use/land cover (LULC) characterizaitoin with MODIS time series data in the Amu River Basin

2009 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2009.5417375 ◽

2009 ◽

Author(s):

Kaishan Song ◽

Zongming Wang ◽

Qqingfeng Liu ◽

Dongmei Lu ◽

Guang Yang ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

River Basin ◽

Time Series Data ◽

Series Data ◽

Land Use Land Cover

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Mapping Annual Land Use and Land Cover Changes in the Yangtze Estuary Region Using an Object-Based Classification Framework and Landsat Time Series Data

Sustainability ◽

10.3390/su12020659 ◽

2020 ◽

Vol 12 (2) ◽

pp. 659

Author(s):

Jinquan Ai ◽

Chao Zhang ◽

Lijuan Chen ◽

Dajun Li

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Time Series Data ◽

Regional Scale ◽

Series Data ◽

Yangtze River Estuary ◽

Classification Framework ◽

Object Based

A system understanding of the patterns, causes, and trends of long-term land use and land cover (LULC) change at the regional scale is essential for policy makers to address the growing challenges of local sustainability and global climate change. However, it still remains a challenge for estuarine and coastal regions due to the lack of appropriate approaches to consistently generate accurate and long-term LULC maps. In this work, an object-based classification framework was designed to mapping annual LULC changes in the Yangtze River estuary region from 1985–2016 using Landsat time series data. Characteristics of the inter-annual changes of LULC was then analyzed. The results showed that the object-based classification framework could accurately produce annual time series of LULC maps with overall accuracies over 86% for all single-year classifications. Results also indicated that the annual LULC maps enabled the clear depiction of the long-term variability of LULC and could be used to monitor the gradual changes that would not be observed using bi-temporal or sparse time series maps. Specifically, the impervious area rapidly increased from 6.42% to 22.55% of the total land area from 1985 to 2016, whereas the cropland area dramatically decreased from 80.61% to 55.44%. In contrast to the area of forest and grassland, which almost tripled, the area of inland water remained consistent from 1985 to 2008 and slightly increased from 2008 to 2016. However, the area of coastal marshes and barren tidal flats varied with large fluctuations.

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Time-Series analysis of MODIS NDVI data along with ancillary data for Land use/Land cover mapping of Uttarakhand

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-1491-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 1491-1500 ◽

Cited By ~ 2

Author(s):

S. K. Patakamuria ◽

S. Agrawal ◽

M. Krishnaveni

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

National Parks ◽

Vegetation Index ◽

Time Series Data ◽

Series Data ◽

Wide Field ◽

Land Use Land Cover ◽

Ancillary Data

Land use and land cover plays an important role in biogeochemical cycles, global climate and seasonal changes. Mapping land use and land cover at various spatial and temporal scales is thus required. Reliable and up to date land use/land cover data is of prime importance for Uttarakhand, which houses twelve national parks and wildlife sanctuaries and also has a vast potential in tourism sector. The research is aimed at mapping the land use/land cover for Uttarakhand state of India using Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The study also incorporated smoothening of time-series plots using filtering techniques, which helped in identifying phenological characteristics of various land cover types. Multi temporal Normalized Difference Vegetation Index (NDVI) data for the year 2010 was used for mapping the Land use/land cover at 250m coarse resolution. A total of 23 images covering a single year were layer stacked and 150 clusters were generated using unsupervised classification (ISODATA) on the yearly composite. To identify different types of land cover classes, the temporal pattern (or) phenological information observed from the MODIS (MOD13Q1) NDVI, elevation data from Shuttle Radar Topography Mission (SRTM), MODIS water mask (MOD44W), Nighttime Lights Time Series data from Defense Meteorological Satellite Program (DMSP) and Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) data were used. Final map product is generated by adopting hybrid classification approach, which resulted in detailed and accurate land use and land cover map.

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