Monitoring the Impacts of Drought on Land Use/Cover: A Developed Object-based Algorithm for NOAA AVHRR Time Series Data

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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Using long term NOAA-AVHRR NDVI time-series data for desertification monitoring in South Tunisia

2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications ◽

10.1109/ictta.2008.4530034 ◽

2008 ◽

Cited By ~ 1

Author(s):

Amor Elmzoughi ◽

Mehriz Zribi ◽

Riadh Abdelfattah ◽

Ziad Belhadj

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Noaa Avhrr ◽

Ndvi Time Series ◽

Desertification Monitoring ◽

South Tunisia

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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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Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

Remote Sensing ◽

10.3390/rs12223798 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3798

Author(s):

Lei Ma ◽

Michael Schmitt ◽

Xiaoxiang Zhu

Keyword(s):

Time Series ◽

Land Cover ◽

Time Series Data ◽

Land Cover Classification ◽

Series Data ◽

Spectral Bands ◽

Object Based ◽

Spatio Temporal ◽

Optical Satellite Images ◽

The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

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Land use/cover characterizaitoin with MODIS time series data with hybrid classification mothed over Australia for 2001 and 2003

2009 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2009.5417761 ◽

2009 ◽

Cited By ~ 1

Author(s):

Kaishan Song ◽

Mingming Jia ◽

Mohsin Hafeez ◽

Zongming Wang ◽

Dongmei Lu ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Time Series Data ◽

Series Data ◽

Hybrid Classification

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Monitoring of desertification in central Asia and western China using long term NOAA-AVHRR NDVI time-series data

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477) ◽

10.1109/igarss.2003.1294414 ◽

2004 ◽

Author(s):

A.X. Liu ◽

Z.J. Liu ◽

C.Y. Wang ◽

Z. Niu ◽

D.M. Yan

Keyword(s):

Time Series ◽

Central Asia ◽

Time Series Data ◽

Western China ◽

Series Data ◽

Noaa Avhrr ◽

Ndvi Time Series

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Modeling Research on 1982-2000 NDVI Time Series Data of Chinese Different Vegetation Types Based on Autoregressive Moving Average Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.955-959.863 ◽

2014 ◽

Vol 955-959 ◽

pp. 863-868

Author(s):

Rong Yu ◽

Bo Feng Cai ◽

Xiang Qin Su ◽

Ya Zi He ◽

Jing Yang

Keyword(s):

Time Series ◽

Vegetation Index ◽

Time Series Data ◽

Moving Average ◽

Original Data ◽

Series Data ◽

Autoregressive Moving Average ◽

Research Areas ◽

Noaa Avhrr ◽

Index Time Series

Vegetation index time series data modeling is widely used in many research areas, such as analysis of environmental change, estimation of crop yield, and the precision of the traditional vegetation index time series data fitting model is lower. This paper conducts the modeling with introducing the autoregressive moving average time series model, and using NOAA/AVHRR normalized differential vegetation index time series data, to estimate the errors of original data which are between under the situation that the parameters to be estimated are lesser, and on the basis gives the fitted equation to the six kinds of main land covers’ vegetation index time series data of Northeast China region.

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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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