scholarly journals Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series

2020 ◽  
Vol 12 (3) ◽  
pp. 478 ◽  
Author(s):  
Yuzhu Hao ◽  
Zhenjie Chen ◽  
Qiuhao Huang ◽  
Feixue Li ◽  
Beibei Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Use Change ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Three Dimensions ◽  
Global Changes ◽  
Landsat Images ◽  
Object Level

High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.

scholarly journals Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

2018 ◽  
Vol 7 (10) ◽  
pp. 405 ◽  
Author(s):  
Urška Kanjir ◽  
Nataša Đurić ◽  
Tatjana Veljanovski
Keyword(s):  
Land Use ◽  
Time Series ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Land Use Control ◽  
Sentinel 2

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.

scholarly journals Land Cover Change Assessment of Vaal Harts Irrigation Scheme using Multi-temporal Satellite Sata

2013 ◽  
Vol 39 (4) ◽  
pp. 59-70 ◽  
Author(s):  
Fredrick Ao Otieno ◽  
Olumuyiwa I Ojo ◽  
George M. Ochieng
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Program ◽  
Land Use Land Cover ◽  
Irrigation Scheme ◽  
Multi Temporal

Abstract Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

scholarly journals LAND USE ANALYSIS USING TIME SERIES OF VEGETATION INDEX DERIVED FROM SATELLITE REMOTE SENSING IN BRANTAS RIVER WATERSHED, EAST JAVA, INDONESIA

2017 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Kunihiko Yoshino ◽  
Yudi Setiawan ◽  
Eikichi Shima
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Use Change ◽  
Land Use Planning ◽  
Vegetation Index ◽  
Tree Planting ◽  
River Watershed ◽  
Watershed Land Use ◽  
Modis Evi ◽  
Land Use Maps

In this study, time series datasets of MODIS EVI (Enhanced Vegetation Index) data from 2002 and 2011 in the Brantas River watershed located in eastern Java, Indonesia were analyzed and classified to make ten land use maps for each year, in order to support watershed land use planning which takes into account local land use and trends in land use change. These land use maps with eight types of main land use categories were examined. During the 10 years period, forested area has expanded, while upland, paddy rice field, mixed garden and plantation have decreased. One of the reasons for this land use change is ascribed to tree planting under the joint forest management system by local people and the state forest corporation.

scholarly journals When to Detect Changes in Object-Based Image Analysis: Before or after Classification?

2017 ◽  
Author(s):  
Kaisheng Luo ◽  
Fu-lu Tao ◽  
Juana P. Moiwo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Change Detection ◽  
Coniferous Forest ◽  
Detection Methods ◽  
Landsat Images ◽  
Two Stage ◽  
Smoothing Effects ◽  
Stage Classification ◽  
Elevation Model

This study compared two object-oriented land use change detection methods—detection after classification (DAC) and classification after detection (CAD) —based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.

scholarly journals An Integrated Approach for Detection and Prediction of Greening Situation in a Typical Desert Area in China and Its Human and Climatic Factors Analysis

2020 ◽  
Vol 9 (6) ◽  
pp. 364
Author(s):  
Lei Zhou ◽  
Siyu Wang ◽  
Mingyi Du ◽  
Jianhua Yang ◽  
Yinuo Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Ecological Engineering ◽  
Integrated Approach ◽  
Vegetation Coverage ◽  
Desert Area ◽  
Unused Land

The combined study of vegetation coverage (VC) and land use change provides important scientific guidance for the restoration and protection of arid regions. Taking Hongjian Nur (HJN) Lake in the desert region as a case study, the VC of this area was calculated using a normalized difference vegetation index (NDVI), which is based on a mixed pixel decomposition method. A grey forecasting model (GM) (1, 1) was used to predict future VC. The driving factors of VC and land use change were analyzed. The results indicate that the average VC of the whole watershed showed a gradual increase from 0.29 to 0.49 during 2000–2017. The prediction results of the GM VC showed that the greening trend is projected to continue until 2027. The area of farmland in the watershed increased significantly and its area was mainly converted from unused land, grassland, and forest. The reason for increased VC may be that the combination of the exploitation of unused land and climate change, which is contrary to the country’s sustainable development goals (SDG; goal 15). Therefore, the particularities of the local ecological environment in China’s desert area needs to be considered in the development of ecological engineering projects.

scholarly journals Identificación de perturbaciones en el bosque húmedo tropical colombiano usando series temporales de imágenes satelitales Landsat mediante el algoritmo Landtrendr

2019 ◽  
pp. 25
Author(s):  
L. Hurtado ◽  
I. Lizarazo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Response ◽  
Short Wave ◽  
Forest Vegetation ◽  
Landsat Images ◽  
Spectral Bands

<p>Time series analysis of satellite images for detection of deforestation and forest disturbances at specific dates has been a subject of research over the last few years. There are many limitations to identify the exact date of deforestation due mainly to the large volume of data and the criteria required for its correct characterization. A further limitation in the analysis of multispectral time series is the identification of true deforestation considering that forest vegetation may undergo different changes over time. This study analyzes deforestation in a zone within the Colombian Amazon using the Normalized Difference Vegetation Index (NDVI) based on semestral median mosaics generated from Landsat images collected from 2000 to 2017. Several samples representing trends of change over the time series were extracted and classified according to their degree of change and persistence in the series, using four categories: (i) deforestation, (ii) degradation, (iii) forest plantation, and (iv) regeneration. Specific deforestation samples were analyzed in the same way using the soil-adjusted vegetation index (SAVI) to reduce the effect of spectral response variations due to soil reflectance changes. It is concluded that the two indices used, together with the near infrared (NIR) and short-wave infrared (SWIR 1) spectral bands, allow to extract values and intervals where the change produced by deforestation on forest vegetation is identified with acceptable accuracy. The analysis of time series using the Landtrendr algorithm confirmed a reliable change detection in each of the forest disturbance categories.</p>

scholarly journals SPATIOTEMPORAL MULTI-SATELLITE BIOPHYSICAL DATA ANALYSIS OF THE EFFECT OF URBANIZATION ON LAND SURFACE AND AIR TEMPERATURE IN BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 47-54
Author(s):  
A. Baloloy ◽  
R. R. Sta. Ana ◽  
J. A. Cruz ◽  
A. C. Blanco ◽  
N. V. Lubrica ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Use Changes ◽  
High Rate ◽  
Landsat 8 ◽  
Landsat Images

Abstract. Urbanization can be observed through the occurrence of land-use changes as more land is being transformed and developed for urban use. One of the Philippine cities with high rate of urbanization is Baguio City, known for having a subtropical highland climate. To understand the spatiotemporal relationship between urbanization and temperature, this study aims to analyze the correlation of urban extent with land surface and air temperature in Baguio City using satellite-based built-up extents, land surface temperature (LST) maps, and weather station-recorded air temperature data. Built-up extent layers were derived from three satellite images: Landsat, RapidEye and PlanetScope. Land-use land cover (LULC) maps were generated from Landsat images using biophysical indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI); while RapidEye and PlanetScope built-up extent maps were generated by applying the visible green-based built-up index (VgNIR-BI). Mean LST values from 1988 to 2018 during the dry and wet seasons were calculated from the Landsat-retrieved surface temperature layers. The result of the study shows that the increase in the built-up extent significantly intensified the LST during the dry season which was observed in all satellite data-derived built-up maps: RapidEye+PlanetScope (2012–2018; r = 0.88), Landsat 8 (2012–2018; r = 0.63) and Landsat 5,7,8 (1988–2018; r = 0.61). The main LST hotspots were detected inside the Central Business District where it expanded gradually from year 1998 (43 ha) to 2011 (83 ha), but have increased extensively within the years 2014 to 2019 (305 ha). On average, 98.5% of the hotspots detected from 1995 to 2019 are within the equivalent built-up area.

scholarly journals Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, North and West of Africa

Proceedings ◽  
2019 ◽  
Vol 39 (1) ◽  
pp. 3
Author(s):  
Malak Henchiri ◽  
Wilson Kalisa ◽  
Zhang Sha ◽  
Jiahua Zhang
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Land Resources ◽  
Detection Analysis ◽  
Spatio Temporal ◽  
Very High

Land use planners require a time series land resources information and changing pattern for future management. Therefore, it is essential to assess changes in land cover. This study was to quantify the spatio-temporal dynamics of land use change over North and West Africa between 1985 and 2015 using the Normalized Difference Vegetation Index (NDVI) from the Very High Resolution Radiometer (AVHRR). The total investigated area was determined by 17,328,557.16 km2. The class of Urban and Built-up, Barren or sparsely vegetated, Savannas and Deciduous Broadleaf Forests increases considerably during the last three decades. In contrast, the class of Open Shrublands, Woody Savannas and water decrease notably during the three decades. The class of croplands decreases from 1985 to 1995 and increased from 1995 to 2015. The class of grasslands recorded a first increase from 1985 to 1995, and then decreased from 1995 to 2015. The class of permanent wetlands first decrease from 1985 to 1995, then increase from 1995 to 2005, followed by a decreasing trend during the last decade. The class of evergreen broadleaf forests decreased in the first two decades, from 1985 to 2005, and increased over the last decade.

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