scholarly journals TREND ANALYSIS OF SOIL SALINITY IN DIFFERENT LAND COVER TYPES USING LANDSAT TIME SERIES DATA (CASE STUDY BAKHTEGAN SALT LAKE)

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 251-257 ◽  
Author(s):  
M. M. Taghadosi ◽  
M. Hasanlou
Keyword(s):  
Land Cover ◽  
Trend Analysis ◽  
Soil Salinity ◽  
Salt Lake ◽  
Bare Soil ◽  
Series Data ◽  
Soil Productivity ◽  
Land Cover Types ◽  
Salinity Changes ◽  
Index Maps

Soil salinity is one of the main causes of desertification and land degradation which has negative impacts on soil fertility and crop productivity. Monitoring salt affected areas and assessing land cover changes, which caused by salinization, can be an effective approach to rehabilitate saline soils and prevent further salinization of agricultural fields. Using potential of satellite imagery taken over time along with remote sensing techniques, makes it possible to determine salinity changes at regional scales. This study deals with monitoring salinity changes and trend of the expansion in different land cover types of Bakhtegan Salt Lake district during the last two decades using multi-temporal Landsat images. For this purpose, per-pixel trend analysis of soil salinity during years 2000 to 2016 was performed and slope index maps of the best salinity indicators were generated for each pixel in the scene. The results of this study revealed that vegetation indices (GDVI and EVI) and also salinity indices (SI-1 and SI-3) have great potential to assess soil salinity trends in vegetation and bare soil lands respectively due to more sensitivity to salt features over years of study. In addition, images of May had the best performance to highlight changes in pixels among different months of the year. A comparative analysis of different slope index maps shows that more than 76% of vegetated areas have experienced negative trends during 17 years, of which about 34% are moderately and highly saline. This percent is increased to 92% for bare soil lands and 29% of salt affected soils had severe salinization. It can be concluded that the areas, which are close to the lake, are more affected by salinity and salts from the lake were brought into the soil which will lead to loss of soil productivity ultimately.

Detection of soil salinity changes and mapping land cover types based upon remotely sensed data

2011 ◽  
Vol 6 (3) ◽  
pp. 913-919 ◽  
Author(s):  
Hamid Reza Matinfar ◽  
Sayed Kazem Alavi Panah ◽  
Farhad Zand ◽  
Kamal Khodaei
Keyword(s):  
Land Cover ◽  
Soil Salinity ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Land Cover Types ◽  
Salinity Changes

scholarly journals Eight-Day Daytime Land Surface Temperature Pattern over Peninsular Malaysia

2019 ◽  
Vol 8 (4) ◽  
pp. 11949-11955
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Peninsular Malaysia ◽  
Series Data ◽  
Temperature Pattern ◽  
Land Cover Types ◽  
North East ◽  
South West Monsoon

The monitoring of land surface temperature (LST) can assist scientists to better understand the possible effect of climate change on land cover types and in conducting appropriate analysis. The earth's annual temperature has moved up and down a few degrees Celsius, including Malaysia, over the past few million years. The location of Malaysia near the equator line and experiences tropical monsoon season were important to take into consideration to the LST changes. In this paper, the eight-day LST time series data for 14-year period (Jan 2003 – Dec 2016) was downloaded from Moderate Resolution Imaging Spectroradiometer (MODIS) website with two types of satellites which are Terra and Aqua. This study focused on two gridded areas in Peninsular Malaysia; the first one which is exposed to North East monsoon, namely super region 21 and another one to South West monsoon, which is super region 26. These two areas are selected due to they being the largest land area covered within the grid relative to other grids. The objective of this study is to compare the eight- day daytime LST pattern between two monsoons with different land cover types for both satellites. Analysis such as cubic spline function with the annual periodic boundary condition and weighted least square (WLS) regression were performed to extract annual seasonal trend for the areas covered. The results of LST showed most of the gridded areas experience similar seasonal pattern for each year but different pattern were discovered when both monsoons were considered. Moreover, the LST trends also changed according to the land cover types over the study period

scholarly journals Measurements of nitric oxide and ammonia soil fluxes from a wet savanna ecosystem site in West Africa during the DACCIWA field campaign

2019 ◽  
Vol 19 (4) ◽  
pp. 2299-2325 ◽  
Author(s):  
Federica Pacifico ◽  
Claire Delon ◽  
Corinne Jambert ◽  
Pierre Durand ◽  
Eleanor Morris ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Land Cover ◽  
West Africa ◽  
Regional Scale ◽  
Bare Soil ◽  
Rural Site ◽  
Field Campaign ◽  
Land Cover Types ◽  
No Fluxes ◽  
No Emissions

Abstract. Biogenic fluxes from soil at a local and regional scale are crucial to study air pollution and climate. Here we present field measurements of soil fluxes of nitric oxide (NO) and ammonia (NH3) observed over four different land cover types, i.e. bare soil, grassland, maize field, and forest, at an inland rural site in Benin, West Africa, during the DACCIWA field campaign in June and July 2016. At the regional scale, urbanization and a massive growth in population in West Africa have been causing a strong increase in anthropogenic emissions. Anthropogenic pollutants are transported inland and northward from the megacities located on the coast, where the reaction with biogenic emissions may lead to enhanced ozone production outside urban areas, as well as secondary organic aerosol formation, with detrimental effects on humans, animals, natural vegetation, and crops. We observe NO fluxes up to 48.05 ngN m−2 s−1. NO fluxes averaged over all land cover types are 4.79±5.59 ngN m−2 s−1, and maximum soil emissions of NO are recorded over bare soil. NH3 is dominated by deposition for all land cover types. NH3 fluxes range between −6.59 and 4.96 ngN m−2 s−1. NH3 fluxes averaged over all land cover types are -0.91±1.27 ngN m−2 s−1, and maximum NH3 deposition is measured over bare soil. The observations show high spatial variability even for the same soil type, same day, and same meteorological conditions. We compare point daytime average measurements of NO emissions recorded during the field campaign with those simulated by GEOS-Chem (Goddard Earth Observing System Chemistry Model) for the same site and find good agreement. In an attempt to quantify NO emissions at the regional and national scale, we also provide a tentative estimate of total NO emissions for the entire country of Benin for the month of July using two distinct methods: upscaling point measurements and using the GEOS-Chem model. The two methods give similar results: 1.17±0.6 and 1.44 GgN month−1, respectively. Total NH3 deposition estimated by upscaling point measurements for the month of July is 0.21 GgN month−1.

scholarly journals Surface Tradeoffs and Elevational Shifts at the Largest Italian Glacier: A Thirty-Years Time Series of Remotely-Sensed Images

2021 ◽  
Vol 13 (1) ◽  
pp. 134
Author(s):  
Nicola Alessi ◽  
Camilla Wellstein ◽  
Duccio Rocchini ◽  
Gabriele Midolo ◽  
Klaus Oeggl ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Biodiversity Loss ◽  
Remotely Sensed ◽  
Alpine Grassland ◽  
Series Data ◽  
Alpine Grasslands ◽  
Landsat Images ◽  
Land Cover Types

Biodiversity loss occurring in mountain ecosystems calls for integrative approaches to improve monitoring processes in the face of human-induced changes. With a combination of vegetation and remotely-sensed time series data, we quantitatively identify the responses of land-cover types and their associated vegetation between 1987 and 2016. Fuzzy clustering of 11 Landsat images was used to identify main land-cover types. Vegetation belts corresponding to such land-cover types were identified by using species indicator analysis performed on 80 vegetation plots. A post-classification evaluation of trends, magnitude, and elevational shifts was done using fuzzy membership values as a proxy of the occupied surfaces by land-cover types. Our findings show that forests and scrublands expanded upward as much as the glacier retreated, i.e., by 24% and 23% since 1987, respectively. While lower alpine grassland shifted upward, the upper alpine grassland lost 10% of its originally occupied surface showing no elevational shift. Moreover, an increase of suitable sites for the expansion of the subnival vegetation belt has been observed, due to the increasing availability of new ice-free areas. The consistent findings suggest a general expansion of forest and scrubland to the detriment of alpine grasslands, which in turn are shifting upwards or declining in area. In conclusion, alpine grasslands need urgent and appropriate monitoring processes ranging from the species to the landscape level that integrates remotely-sensed and field data.

scholarly journals Analysis of vegetation and land cover dynamics in north-western Morocco during the last decade using MODIS NDVI time series data

2011 ◽  
Vol 8 (11) ◽  
pp. 3359-3373 ◽  
Author(s):  
C. Höpfner ◽  
D. Scherer
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Land Cover Types ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Western Morocco ◽  
North Western

Abstract. Vegetation phenology as well as the current variability and dynamics of vegetation and land cover, including its climatic and human drivers, are examined in a region in north-western Morocco that is nearly 22 700 km2 big. A gapless time series of Normalized Differenced Vegetation Index (NDVI) composite raster data from 29 September 2000 to 29 September 2009 is utilised. The data have a spatial resolution of 250 m and were acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The presented approach allows to compose and to analyse yearly land cover maps in a widely unknown region with scarce validated ground truth data by deriving phenological parameters. Results show that the high temporal resolution of 16 d is sufficient for (a) determining local land cover better than global land cover classifications of Plant Functional Types (PFT) and Global Land Cover 2000 (GLC2000) and (b) for drawing conclusions on vegetation dynamics and its drivers. Areas of stably classified land cover types (i.e. areas that did not change their land cover type) show climatically driven inter- and intra-annual variability with indicated influence of droughts. The presented approach to determine human-driven influence on vegetation dynamics caused by agriculture results in a more than ten times larger area compared with stably classified areas. Change detection based on yearly land cover maps shows a gain of high-productive vegetation (cropland) of about 259.3 km2. Statistically significant inter-annual trends in vegetation dynamics during the last decade could however not be discovered. A sequence of correlations was respectively carried out to extract the most important periods of rainfall responsible for the production of green biomass and for the extent of land cover types. Results show that mean daily precipitation from 1 October to 15 December has high correlation results (max. r2=0.85) on an intra-annual time scale to NDVI percentiles (50 %) of land cover types. Correlation results of mean daily precipitation from 16 September to 15 January and percentage of yearly classified area of each land cover type are medium up to high (max. r2=0.64). In all, an offset of nearly 1.5 months is detected between precipitation rates and NDVI values. High-productive vegetation (cropland) is proved to be mainly rain-fed. We conclude that identification, understanding and knowledge about vegetation phenology, and current variability of vegetation and land cover, as well as prediction methods of land cover change, can be improved using multi-year MODIS NDVI time series data. This study enhances the comprehension of current land surface dynamics and variability of vegetation and land cover in north-western Morocco. It especially offers a quick access when estimating the extent of agricultural lands.

scholarly journals ANNUAL URBAN BUILT-UP CHANGE AREA ONLINE EXTRACTION USING LANDSAT TIME SERIES DATA

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 735-739
Author(s):  
J. Zhang ◽  
H. Wu ◽  
C. Cai
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Series Data ◽  
Landsat Images ◽  
Area Change ◽  
Land Cover Types ◽  
Online Processing ◽  
Online Extraction ◽  
Trajectory Modelling

Abstract. Urban built-up area change information in multiple periods is a pivotal factor in global climate change application and sustainable development research. Due to spatial-temporal expression of land cover types, processing speed and operability, built-up area change information extraction using Landsat time series data is still a challenging task. To provide insights into the inter-annual dynamic of land use change, focusing on how time series characteristics improves recognition of urban change and how much online extraction convenience is facilitated, this paper presents a new methodology to built-up change area extraction using inter-annual time series of Landsat images. The central premise of the approach is that time series characteristics are firstly expressed by spectral index. The logistic algorithm is then used in time series trajectory modelling of land cover types for annual urban built-up change area extraction. Finally, the individual steps of the whole process, including image selection, time series trajectory modelling and results display, are converted to web service for online processing. The further comparison is also conducted between the proposed method and post-classification comparison method. Results show that the online processing mode has strengths regarding the provision of functionality to user-end, the automation of recurring tasks or the sharing of workflows. Results also demonstrate that the proposed method improves the accuracy of annual urban built-up change area extraction.

scholarly journals Analysis of vegetation and land cover dynamics in north-western Morocco during the last decade using MODIS NDVI time series data

2011 ◽  
Vol 8 (2) ◽  
pp. 3953-3998 ◽  
Author(s):  
C. Höpfner ◽  
D. Scherer
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Land Cover Types ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Western Morocco ◽  
North Western

Abstract. Vegetation phenology as well as current variability and dynamics of vegetation and land cover including its climatic and human drivers are examined in a region in north-western Morocco of nearly 22 700 km2. A gapless time series of Normalized Differenced Vegetation Index (NDVI) composite raster data from 29 September 2000 to 29 September 2009 with a spatial resolution of 250 m and acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is utilised. The presented approach allows to compose and analyse yearly land cover maps in a widely unknown region with scarce validated ground truth data by deriving phenological parameters. Results show that high temporal resolution of 16 d is sufficient (a) for determining land cover better than global land cover classifications of Plant Functional Types (PFT) and Global Land Cover 2000 (GLC2000), and (b) for drawing conclusions on vegetation dynamics and its drivers. Areas of stably classified land cover types show climatically driven inter- and intra-annual variability with indicated influence of droughts. The presented approach to determine human-driven influence on vegetation dynamics caused by agriculture results in a more than ten times larger area compared to the stably classified areas. Change detection based on yearly land cover maps shows a gain of high-productive vegetation (cropland) of about 259.3 km2. However, statistically significant inter-annual trends in vegetation dynamics during the last decade could not be discovered. A sequence of correlations was done to extract the most important period of rainfall for production of green biomass and for the extent of land cover types, respectively. Results show that mean daily precipitation from 1 October to 15 December has high correlation results (max. r2=0.85) at intra-annual time scale to NDVI percentiles (50%) of land cover types. Correlation results of mean daily precipitation from 16 September to 15 January and percentage of yearly classified area of each land cover type are medium up to high (max. r2=0.64). In all, an offset of nearly 1.5 months is detected between precipitation rates and NDVI in 16 d steps. High-productive vegetation (cropland) is proved to be mainly rain-fed. We conclude that identification, understanding and knowledge about vegetation phenology, and current variability of vegetation and land cover as well as prediction methods of land cover change can be improved using multi-year MODIS NDVI time series data. This study enhances the comprehension of current land surface dynamics and variability of vegetation and land cover in north-western Morocco offering a fast access especially for estimating the extent of agricultural lands.

scholarly journals Measurements of nitric oxide and ammonia soil fluxes from a wet savanna ecosystem site in West Africa during the DACCIWA field campaign

2018 ◽  
Author(s):  
Federica Pacifico ◽  
Claire Delon ◽  
Corinne Jambert ◽  
Pierre Durand ◽  
Eleanor Morris ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Land Cover ◽  
West Africa ◽  
Bare Soil ◽  
Rural Site ◽  
Strong Increase ◽  
Field Campaign ◽  
Land Cover Types ◽  
No Fluxes ◽  
No Emissions

Abstract. It is important to correctly simulate biogenic fluxes from soil in atmospheric chemistry models at a local and regional scale to study air pollution and climate in an area of the world, West Africa, that has been subject to a strong increase in anthropogenic emissions due to a massive growth in population and urbanization. Anthropogenic pollutants are transported inland and northward from the mega cities located on the coast, where the reaction with biogenic emissions may lead to enhanced ozone production outside urban areas, as well as secondary organic aerosols formation, with detrimental effects on humans, animals, natural vegetation and crops. Here we present field measurements of soil fluxes of nitric oxide (NO) and ammonia (NH3) observed over four different land cover types, i.e. bare soil, grassland, maize field and forest, at an inland rural site in Benin, West Africa, during the DACCIWA field campaign in June and July 2016. We observe NO fluxes up to 48.05 ngN m−2 s−1. NO fluxes averaged over all land cover types are 4.79 ± 5.59 ngN m−2 s−1, maximum soil emissions of NO are recorded over bare soil. NH3 is dominated by deposition for all land cover types. NH3 fluxes range between −6.59 and 4.96 ngN m−2 s−1. NH3 fluxes averaged over all land cover types are −0.91 ± 1.27 ngN m−2 s−1 and maximum NH3 deposition is measured over bare soil. The observations show high spatial variability even for the same soil type, same day and same meteorological conditions. We compare point daily average measurements of NO emissions recorded during the field campaign with those simulated by GEOS-Chem (Goddard Earth Observing System Chemistry Model) for the same site and find good agreement. In an attempt to quantify NO emissions at the regional and national scale, we also provide a tentative estimate of total NO emissions for the entire country of Benin for the month of July using two distinct methods: upscaling point measurements and using the GEOS-Chem model. The two methods give similar results: 1.17 ± 0.6 GgN/month and 1.44 GgN/month, respectively. Total NH3 deposition estimated by upscaling point measurements for the month of July is 0.21 GgN/month.

scholarly journals Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification

2019 ◽  
Vol 11 (6) ◽  
pp. 660 ◽  
Author(s):  
Chang-An Liu ◽  
Zhongxin Chen ◽  
Di Wang ◽  
Dandan Li
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Agricultural Land ◽  
Test Site ◽  
Decomposition Methods ◽  
Bare Soil ◽  
Classification Error ◽  
Land Cover Types ◽  
Land Covers

We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing.

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