scholarly journals LAND SALINIZATION DYNAMICS BASED ON FEATURE SPACE COMBINATIONS FROM LANDSAT IMAGE IN TONGYU COUNTY, NORTHEAST CHINA

2020 ◽  
Vol V-3-2020 ◽  
pp. 25-32
Author(s):  
F. Huang ◽  
P. Wang ◽  
Y. Liu ◽  
H. Y. Li
Keyword(s):  
Land Degradation ◽  
Northeast China ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Feature Space ◽  
Bare Soil ◽  
Remote Sensing Images ◽  
Degraded Land ◽  
Physical Conditions ◽  
Modified Soil

Abstract. Land salinization is one of the most common land degradation processes. Tongyu county exemplifies all the forms of land degradation in Northeast China and is prone to land salinization due to its fragile physical conditions. In this study, Landsat remote sensing images are adopted to invert surface albedo, MSAVI (modified soil adjusted vegetation index), and salinity index (SI) data. The feature space models of albedo-SI and MSAVI-SI, considering the bare soil and vegetation information respectively, are constructed and compared. Land salinization changes for the period of 1998–2017 are investigated using the salinization monitoring index (SMI) and salinization detection index (SDI) extracted from the feature space models. Our results show that the land salinization situation in Tongyu county have tended to improve, associated with the biological, ecological and engineering means for the degraded land rehabilitation. The feature space models is applicable for the extraction of salinization information, and albedo-SI may evaluate land salinization levels with higher accuracy.

Land change effects on ecosystem degradation across Nigerian agro-ecological zones from 1975 through 2020

2021 ◽  
Author(s):  
Felicia Akinyemi ◽  
Chinwe Ifejika Speranza
Keyword(s):  
Land Use ◽  
Land Degradation ◽  
Vegetation Index ◽  
Land Change ◽  
Land Area ◽  
Degraded Land ◽  
Vegetation Productivity ◽  
Ecological Zones ◽  
Settlement Areas ◽  
Agro Ecological Zones

<p>Land system change is implicated in many sustainability challenges as its alteration impacts ecosystems and exacerbate the vulnerability of communities, particularly where livelihoods are largely dependent on natural resources. The production of a land use-cover map for year 2020 extended the time-series for assessing land use-cover dynamics over a period of 45 years (1975-2020). The case of Nigeria is examined as the land area encompass several agro-ecological zones. The classification scheme countries utilise for estimating Land Degradation Neutrality baseline and monitoring of the Sustainable Development Goal 15.3.1 indicator (proportion of degraded land over total land area) was used, based on seven land use-cover classes (tree-covered area, grassland, cropland, wetland, artificial surface area, otherland, and waterbody). Severity of land degradation, computed as changes in vegetation productivity using the Enhanced Vegetation Index (EVI), as well as changes in ecosystem service values were examined across the different land use-cover types, in areas of change and persistence. Land degradation is most severe in settlement areas and wetlands with declining trends in 34% of settlement areas and 29% in wetlands respectively. About 19% of tree-covered areas experienced increasing trends. In some areas of land use-cover persistence, vegetation productivity declined despite no land change occurring. For example, vegetation productivity declined in about 35% and 9% of persistent wetlands and otherland respectively between 2000 and 2020, whereas there was improvement in 22% of persistent grasslands, 18% of persistent otherlands and 12% of persistent croplands. In land change areas, about 12% and 8% of wetlands and tree-covered areas had declining vegetation trends respectively, whereas it improved the most in croplands (20%), and grasslands (16%). With some wetland, cropland and otherland areas degrading the most, protecting these critical ecosystems is required to sustain their functions and services. The finding that vegetation productivity may decline in areas of persistence underscores the importance of intersecting land use-cover (in terms of persistence and change) with vegetation productivity to identify pathways for enhancing ecological sustainability.</p>

scholarly journals Broadband Albedo Observations in the Southern Great Plains

2006 ◽  
Vol 45 (1) ◽  
pp. 210-235 ◽  
Author(s):  
Claude E. Duchon ◽  
Kenneth G. Hamm
Keyword(s):  
Time Series ◽  
Great Plains ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographic Location ◽  
Bare Soil ◽  
Southern Great Plains ◽  
Oklahoma Mesonet ◽  
The One

Abstract Time series of daily broadband surface albedo for 1998 and 1999 have been analyzed from six locations in the network of 22 Atmospheric Radiation Measurement Program Solar–Infrared Radiation Stations distributed from central Kansas to central Oklahoma. Two of the stations are in Kansas, and four are in Oklahoma; together they reasonably encompass the variation in geography in the southern Great Plains. Daily precipitation totals locally measured or obtained from nearby Oklahoma Mesonet stations and time series of biweekly maximum normalized difference vegetation index obtained from NOAA’s Advanced Very High Resolution Radiometer were used to determine linkages between surface albedo and amount of precipitation and degree of green vegetation. As part of this determination, daily albedo was categorized according to sky condition, that is, clear, partly cloudy, or overcast, with appropriate boundaries for each category. The more notable results are the following: 1) 2-yr mean annual albedos varied by more than 20% among the six sites, the lowest albedo being 0.18 and the highest albedo being 0.22; 2) the numerical difference was about 4 times the maximum interannual mean difference among the six stations, indicating the importance of geographic location; 3) for sites with a large amount of bare soil, a systematic decrease in albedo in response to rainfall events and a systematic increase in albedo as the soil dried were observed; 4) at the one site with total vegetation cover, that is, no bare soil, albedo response to precipitation events was suppressed; 5) no relation was found between mean annual albedo and annual precipitation; 6) whether days were classified as clear or partly cloudy had little influence on daily albedo, but overcast days typically reduced albedo, sometimes substantially; and 7) the main contributor to low albedos on overcast days with rain was the wet surface; the contribution by the overcast sky was secondary.

scholarly journals Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

2018 ◽  
Vol 10 (10) ◽  
pp. 1614 ◽  
Author(s):  
Haishuo Wei ◽  
Juanle Wang ◽  
Kai Cheng ◽  
Ge Li ◽  
Altansukh Ochir ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Feature Space ◽  
Gobi Desert ◽  
Landsat 8 ◽  
Mongolian Plateau ◽  
Size Index ◽  
Modified Soil

The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau.

scholarly journals Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3676 ◽  
Author(s):  
Hao Chen ◽  
Xiangnan Liu ◽  
Chao Ding ◽  
Fang Huang
Keyword(s):  
Trend Analysis ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Practical Method ◽  
Large Area ◽  
Modis Ndvi ◽  
Daily Precipitation Data ◽  
Improved Model

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000–2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

scholarly journals A Study of Desertification Using Remote Sensing Techniques in Basra Governorate, South Iraq

2021 ◽  
pp. 912-926
Author(s):  
Fadel Abbas Zwain ◽  
Thair Thamer Al-Samarrai ◽  
Younus I. Al-Saady
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arid Zone ◽  
Sand Dunes ◽  
Risk Index ◽  
Anthropogenic Factors ◽  
Landsat Images

Iraq territory as a whole and south of Iraq in particular encountered rapid desertification and signs of severe land degradation in the last decades. Both natural and anthropogenic factors are responsible for the extent of desertification. Remote sensing data and image analysis tools were employed to identify, detect, and monitor desertification in Basra governorate. Different remote sensing indicators and image indices were applied in order to better identify the desertification development in the study area, including the Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Salinity index (SI), Top Soil Grain Size Index (GSI) , Land Surface Temperature (LST) , Land Surface Soil Moisture (LSM), and Land Degradation Risk Index (LDI) which was used for the assessment of degradation severity .Three Landsat images, acquired in 1973, 1993, and 2013, were used to evaluate the potential of using remote sensing analysis in desertification monitoring. The approach applied in this study for evaluating this phenomenon was proven to be an effective tool for the recognition of areas at risk of desertification. The results indicated that the arid zone of Basra governorate encounters substantial changes in the environment, such as decreasing surface water, degradation of agricultural lands (as palm orchards and crops), and deterioration of marshlands. Additional changes include increased salinization with the creeping of sand dunes to agricultural areas, as well as the impacts of oil fields and other facilities.

scholarly journals Spatial and temporal variation of vegetation cover in Al- Soudah area and its relationship to precipitation during the period (2014- 2018 AD) using remote sensing and geographic information systems: التباين المكاني والزمني للغطاء النباتي بمنطقة السودة وعلاقته بالتساقط خلال الفترة (2014- 2018م) باستخدام الاستشعار عن بعد ونظم المعلومات الجغرافية

2021 ◽  
Vol 5 (3) ◽  
pp. 107-87
Author(s):  
Fadi Abdullah alanazi, Yaser Rashed Alzannan, Faten Hamed Na Fadi Abdullah alanazi, Yaser Rashed Alzannan, Faten Hamed Na
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Geographic Information ◽  
Temporal Changes ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Changes ◽  
Modified Soil

Souda is one of the important regions in Saudi Arabia in terms of spatial and temporal changes in vegetation cover; It includes the National Park, which is a leading tourist destination and one of the most beautiful parks in it. by tracking the spatial and temporal changes of vegetation cover by integrating remote sensing and geographic information systems, through the application of the modified soil vegetation index MSAVI during the period (2014- 2018), it became clear the decrease in the quantity and density of vegetation cover in the area. Thus, the study concluded that this indicator is one of the best indicators that can be used to extract vegetation cover from satellite images.

scholarly journals Minimizing the Impacts of Desertification in an Arid Region: A Case Study of the West Desert of Iraq

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Khamis Naba Sayl ◽  
Sadeq Oleiwi Sulaiman ◽  
Ammar Hatem Kamel ◽  
Nur Shazwani Muhammad ◽  
Jazuri Abdullah ◽  
...  
Keyword(s):  
Land Degradation ◽  
Vegetation Index ◽  
Rainwater Harvesting ◽  
Large Change ◽  
Cost Effective ◽  
Western Desert ◽  
Gis Environment ◽  
Small Change ◽  
Western Desert Of Iraq ◽  
Suitable Land

Currently, desertification is a major problem in the western desert of Iraq. The harsh nature, remoteness, and size of the desert make it difficult and expensive to monitor and mitigate desertification. Therefore, this study proposed a comprehensive and cost-effective method, via the integration of geographic information systems (GISs) and remote sensing (RS) techniques to estimate the potential risk of desertification, to identify the most vulnerable areas and determine the most appropriate sites for rainwater conservation. Two indices, namely, the Normalized Differential Vegetation Index (NDVI) and Land Degradation Index (LDI), were used for a cadastral assessment of land degradation. The findings of the combined rainwater harvesting appropriateness map, and the maps of NDVI and LDI changes found that 65% of highly suitable land for rainwater harvesting lies in the large change and 35% lies in the small change of NDVI, and 85% of highly suitable land lies in areas with a moderate change and 12% lies in strong change of LDI. The adoption of the weighted linear combination (WLC) and Boolean methods within the GIS environment, and the analysis of NDVI with LDI changes can allow hydrologists, decision-makers, and planners to quickly determine and minimize the risk of desertification and to prioritize the determination of suitable sites for rainwater harvesting.

scholarly journals Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Tahmid Anam Chowdhury ◽  
◽  
Md. Saiful Islam ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Land Use Land Cover ◽  
Remarkable Change

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

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