Change analysis in land use land cover due to surface mining in Jharia coalfield through Landsat time series 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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Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Remote Sensing ◽

10.3390/rs12172735 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2735 ◽

Cited By ~ 16

Author(s):

Carlos M. Souza ◽

Julia Z. Shimbo ◽

Marcos R. Rosa ◽

Leandro L. Parente ◽

Ane A. Alencar ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Time Series Data ◽

Google Earth ◽

Series Data ◽

Data Series ◽

Forest Conversion ◽

Pixel Resolution ◽

Historical Land Use

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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Land Cover Change Detection Using Object-Based Classification Technique: A Case Study Along The Kosi River, Bihar

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

10.5194/isprsarchives-xl-8-839-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 839-843 ◽

Cited By ~ 2

Author(s):

M. Modi ◽

R. Kumar ◽

G. Ravi Shankar ◽

T.R. Martha

Keyword(s):

Land Use ◽

Land Cover ◽

Time Series Data ◽

Feature Space ◽

Series Data ◽

Land Use Land Cover ◽

Shape Context ◽

Object Property ◽

Object Based ◽

Remote Sensing Techniques

Land use/land cover (LULC) is dynamic in nature and can affect the ability of land to sustain human activities. The Indo-Gangetic plains of north Bihar in eastern India are prone to floods, which have a significant impact on land use / land cover, particularly agricultural lands and settlement areas. Satellite remote sensing techniques allow generating reliable and near-realtime information of LULC and have the potential to monitor these changes due to periodic flood. Automated methods such as object-based techniques have better potential to highlight changes through time series data analysis in comparison to pixel-based methods, since the former provides an opportunity to apply shape, context criteria in addition to spectral criteria to accurately characterise the changes. In this study, part of Kosi river flood plains in Supaul district, Bihar has been analysed to identify changes due to a flooding event in 2008. Object samples were collected from the post-flood image for a nearest neighbourhood (NN) classification in an object-based environment. Collection of sample were partially supported by the existing 2004–05 database. The feature space optimisation procedure was adopted to calculate an optimum feature combination (i.e. object property) that can provide highest classification accuracy. In the study, for classification of post-flood image, best class separation was obtained by using distance of 0.533 for 28 parameters out of 34. Results show that the Kosi flood has resulted in formation of sandy riverine areas.

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Land Use Land Cover Change and its Pathways in Sidin VDC, Panchthar District, Nepal

Geographical Journal of Nepal ◽

10.3126/gjn.v11i0.19550 ◽

2018 ◽

Vol 11 ◽

pp. 77-94 ◽

Cited By ~ 1

Author(s):

Prem Sagar Chapagain ◽

Mohan Kumar Rai ◽

Basanta Paudel

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Agricultural Land ◽

Time Series Data ◽

Household Survey ◽

Human Interaction ◽

Series Data ◽

Land Use Land Cover ◽

Important Indicator

Land use/land cover situation is an important indicator of human interaction with environment. It reflects both environmental situation and the livelihood strategies of the people in space over time. This paper has attempted to study the land use/ land cover change of Sidin VDC, in the Koshi River basin in Nepal, based on maps and Remote sensing imageries (RS) data and household survey using structured questionnaires, focus group discussion and key informant interview. The study has focused on analysis the trend and pathways of land use change by dividing the study area into three elevation zones – upper, middle and lower. The time series data analysis from 1994-2004-2014 show major changes in forest and agricultural land. The dominant pathways of change is from forest to agriculture and forest to shrub during 1994-2004 and agriculture to forest during 2004-2014. The development of community forest, labor migration and labor shortage are found the major causes of land use change.The Geographical Journal of NepalVol. 11: 77-94, 2018

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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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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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A spatiotemporal ensemble machine learning framework for generating land use / land cover time-series maps for Europe (2000 – 2019) based on LUCAS, CORINE and GLAD Landsat

10.21203/rs.3.rs-561383/v1 ◽

2021 ◽

Author(s):

Martijn Witjes ◽

Leandro Parente ◽

Chris J. van Diemen ◽

Tomislav Hengl ◽

Martin Landa ◽

...

Keyword(s):

Machine Learning ◽

Land Use ◽

Time Series ◽

Land Cover ◽

Data Science ◽

Cross Validation ◽

Series Data ◽

Land Use Land Cover ◽

Cover Time ◽

Learning Framework

Abstract A seamless spatiotemporal machine learning framework for automated prediction, uncertainty assessment, and analysis of land use / land cover (LULC) dynamics is presented. The framework includes: (1) harmonization and preprocessing of high-resolution spatial and spatiotemporal covariate datasets (GLAD Landsat, NPP/VIIRS) including 5 million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and uncertainty per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model was fitted by combining random forest, gradient boosted trees, and artificial neural network, with logistic regressor as meta-learner. The results show that the most important covariates for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with 62%, 70%, and 87% accuracy when predicting 33 (level-3), 14 (level-2), and 5 classes (level-1); with artificial surface classes such as 'airports' and 'railroads' showing the lowest match with validation points. The spatiotemporal model outperforms spatial models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest gradual deforestation trends in large parts of Sweden, the Alps, and Scotland. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict land cover for years prior to 2000 and beyond 2020. The generated land cover time-series data stack (ODSE-LULC), including the training points, is publicly available via the Open Data Science (ODS)-Europe Viewer.

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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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