Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020

Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technology such as remote sensing (RS) and cloud computing have made LC change mapping efficient. In this study, the LC of Rupandehi District of Nepal were mapped using a Landsat imagery and Random Forest (RF) classifier from 2005 to 2020 using Google Earth Engine (GEE) platform. GEE eases the way in extracting, analyzing, and performing different operations for the earth’s observed data. Land cover classification, Centre of gravity (CoG), and their trajectories for all LC classes: agriculture, built-up, water, forest, and barren area were extracted with five-year intervals, along with their Ecosystem service values (ESV) to understand the load on the ecosystem. We also discussed the aspects and problems of the spatiotemporal analysis of developing regions. It was observed that the built-up areas had been increasing over the years and more centered in between the two major cities. Other agriculture, water, and forest classes had been subjected to fluctuations with barren land in the decreasing trend. This alteration in the area of the LC classes also resulted in varying ESVs for individual land cover and total values for the years. The accuracy for the RF classifier was under substantial agreement for such fragmented LCs. Using LC, CoG, and ESV, the paper discusses the need for spatiotemporal analysis studies in Nepal to overcome the current limitations and later expansion to other regions. Studies such as these help in implementing proper plans and strategies by district administration offices and local governmental bodies to stop the exploitation of resources.

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Investigating impact of land-use and land cover changes on hydro-ecological balance using GIS: insights from IIT Bombay, India

SN Applied Sciences ◽

10.1007/s42452-021-04328-7 ◽

2021 ◽

Vol 3 (3) ◽

Cited By ~ 1

Author(s):

Aman Srivastava ◽

Pennan Chinnasamy

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Runoff ◽

Forest Cover ◽

Google Earth ◽

Negative Consequences ◽

Ecological Stability ◽

Barren Land ◽

Ecological Balance ◽

Lulc Changes

AbstractThe present study, for the first time, examined land-use land cover (LULC), changes using GIS, between 2000 and 2018 for the IIT Bombay campus, India. Objective was to evaluate hydro-ecological balance inside campus by determining spatio-temporal disparity between hydrological parameters (rainfall-runoff processes), ecological components (forest, vegetation, lake, barren land), and anthropogenic stressors (urbanization and encroachments). High-resolution satellite imageries were generated for the campus using Google Earth Pro, by manual supervised classification method. Rainfall patterns were studied using secondary data sources, and surface runoff was estimated using SCS-CN method. Additionally, reconnaissance surveys, ground-truthing, and qualitative investigations were conducted to validate LULC changes and hydro-ecological stability. LULC of 2018 showed forest, having an area cover of 52%, as the most dominating land use followed by built-up (43%). Results indicated that the area under built-up increased by 40% and playground by 7%. Despite rapid construction activities, forest cover and Powai lake remained unaffected. This anomaly was attributed to the drastically declining barren land area (up to ~ 98%) encompassing additional construction activities. Sustainability of the campus was demonstrated with appropriate measures undertaken to mitigate negative consequences of unwarranted floods owing to the rise of 6% in the forest cover and a decline of 21% in water hyacinth cover over Powai lake. Due to this, surface runoff (~ 61% of the rainfall) was observed approximately consistent and being managed appropriately despite major alterations in the LULC. Study concluded that systematic campus design with effective implementation of green initiatives can maintain a hydro-ecological balance without distressing the environmental services.

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Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽

10.3390/land10020173 ◽

2021 ◽

Vol 10 (2) ◽

pp. 173

Author(s):

Changjun Gu ◽

Yili Zhang ◽

Linshan Liu ◽

Lanhui Li ◽

Shicheng Li ◽

...

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Land Cover ◽

Ecosystem Service ◽

Forest Cover ◽

Google Earth ◽

Forest Cover Change ◽

Sacred Landscape ◽

Lulc Changes ◽

Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

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Urban Sprawl and Changes in Land-Use Efficiency in the Beijing–Tianjin–Hebei Region, China from 2000 to 2020: A Spatiotemporal Analysis Using Earth Observation Data

Remote Sensing ◽

10.3390/rs13152850 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2850

Author(s):

Meiling Zhou ◽

Linlin Lu ◽

Huadong Guo ◽

Qihao Weng ◽

Shisong Cao ◽

...

Keyword(s):

Land Use ◽

Sustainable Development ◽

Urban Areas ◽

Landsat Imagery ◽

Spatiotemporal Analysis ◽

Google Earth ◽

Observation Data ◽

Land Use Efficiency ◽

Use Efficiency ◽

Bth Region

Sustainable development in urban areas is at the core of the implementation of the UN 2030 Agenda and the Sustainable Development Goals (SDG). Analysis of SDG indicator 11.3.1—Land-use efficiency based on functional urban boundaries—provides a globally harmonized avenue for tracking changes in urban settlements in different areas. In this study, a methodology was developed to map built-up areas using time-series of Landsat imagery on the Google Earth Engine cloud platform. By fusing the mapping results with four available land-cover products—GlobeLand30, GHS-Built, GAIA and GLC_FCS-2020—a new built-up area product (BTH_BU) was generated for the Beijing–Tianjin–Hebei (BTH) region, China for the time period 2000–2020. Using the BTH_BU product, functional urban boundaries were created, and changes in the size of the urban areas and their form were analyzed for the 13 cities in the BTH region from 2000 to 2020. Finally, the spatiotemporal dynamics of SDG 11.3.1 indicators were analyzed for these cities. The results showed that the urban built-up area could be extracted effectively using the BTH_BU method, giving an overall accuracy and kappa coefficient of 0.93 and 0.85, respectively. The overall ratio of the land consumption rate to population growth rate (LCRPGR) in the BTH region fluctuated from 1.142 in 2000–2005 to 0.946 in 2005–2010, 2.232 in 2010–2015 and 1.538 in 2015–2020. Diverged changing trends of LCRPGR values in cities with different population sizes in the study area. Apart from the megacities of Beijing and Tianjin, after 2010, the LCRPGR values were greater than 2 in all the cities in the region. The cities classed as either small or very small had the highest LCRPGR values; however, some of these cities, such as Chengde and Hengshui, experienced population loss in 2005–2010. To mitigate the negative impacts of low-density sprawl on environment and resources, local decision makers should optimize the utilization of land resources and improve land-use efficiency in cities, especially in the small cities in the BTH region.

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Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China

Sensors ◽

10.3390/s20072091 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2091 ◽

Cited By ~ 4

Author(s):

Dong-Dong Zhang ◽

Lei Zhang

Keyword(s):

Surface Water ◽

Land Cover ◽

Central Region ◽

Yangtze River ◽

Industrial Development ◽

Landsat Imagery ◽

Google Earth ◽

Google Earth Engine ◽

Main Factors ◽

Lower Yangtze

Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined land-cover changes in the central region of the lower Yangtze River and exemplifies the application of GEE using the random forest classification algorithm on Landsat dense stacks spanning the 30 years from 1987 to 2017. Based on the obtained time-series land-cover classification results, the spatiotemporal land-use/cover changes were analyzed, as well as the main factors driving the changes in different land-cover categories. The results show that: (1) The obtained land datasets were reliable and highly accurate, with an overall accuracy ranging from 88% to 92%. (2) Over the past 30 years, built-up areas have continued to expand, increasing from 537.9 km2 to 1500.5 km2, and the total area occupied by built-up regions has expanded by 178.9% to occupy an additional 962.7 km2. The surface water area first decreased, then increased, and generally showed an increasing trend, expanding by 17.9%, with an area increase of approximately 131 km2. Barren areas accounted for 6.6% of the total area in the period 2015–2017, which was an increase of 94.8% relative to the period 1987–1989. The expansion of the built-up area was accompanied by an overall 25.6% (1305.7 km2) reduction in vegetation. (3) The complexity of the key factors driving the changes in the regional surface water extent was made apparent, mainly including the changes in runoff of the Yangtze River and the construction of various water conservancy projects. The effects of increasing the urban population and expanding industrial development were the main factors driving the expansion of urban built-up areas and the significant reduction in vegetation. The advantages and limitations arising from land-cover mapping by using the Google Earth Engine are also discussed.

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UHI SPATIO-TEMPORAL ANALYSIS WITH GEOSPATIAL TECHNIQUES: A CASE OF AHMEDABAD CITY.

10.36106/paripex/1912691 ◽

2021 ◽

pp. 194-200

Author(s):

Darshana Rawal ◽

Vishal Gupta

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Land Surface ◽

Google Earth ◽

Landsat Data ◽

Land Use Land Cover ◽

Barren Land ◽

Thermal Data ◽

Spatio Temporal

Spatio-temporal changes in land use land cover (LULC) have been relevant factors in causing the changes in Urban Heat Island (UHI) pattern across rural and urban areas all over the world. Studies conducted have shown that the relation between LULC on scale of the UHI can be an important factor assessing the condition not only for a country but for environment of a city also. Over the years it is reflected in health of vegetation and urbanization pattern of cities. As the thermal remote sensing has been evolved, the measurement of the temperature through satellite products has become possible. Thermal data derived through remote sensing gives us birds-eye-view to see how the thermal data varies in the entire city. In this study such relations are shown over Ahmedabad city of India for the period of 2007 to 2020 using Landsat series satellite data. Land Surface Temperature (LST) is calculated using Google Earth Engine Platform Surface Brightness Temperature for Landsat data and using Radiative Transfer Equation for Landsat data. LST is correlated with land use land cover mainly Built-up, Vegetation, Barren land, Water & Other and corresponding Land Use and Land Cover respectively, and it is found that LST is positively related with all indices except for Normalize Difference Vegetation Index (NDVI) with strong negative correlation and R 2 of 0.51.

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Land Use and Land Cover Dynamics and Assessing the Ecosystem Service Values in the Trans-Boundary Gandaki River Basin, Central Himalayas

Sustainability ◽

10.3390/su10093052 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3052 ◽

Cited By ~ 22

Author(s):

Raju Rai ◽

Yili Zhang ◽

Basanta Paudel ◽

Bipin Acharya ◽

Laxmi Basnet

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Land Cover ◽

River Basin ◽

Ecosystem Service ◽

Satellite Image ◽

Landsat 8 ◽

Barren Land ◽

Central Himalayas ◽

Land Cover Types

Land use and land cover is a fundamental variable that affects many parts of social and physical environmental aspects. Land use and land cover changes (LUCC) has been known as one of the key drivers of affecting in ecosystem services. The trans-boundary Gandaki River Basin (GRB) is the part of Central Himalayas, a tributary of Ganges mega-river basin plays a crucial role on LUCC and ecosystem services. Due to the large topographic variances, the basin has existed various land cover types including cropland, forest cover, built-up area, river/lake, wetland, snow/glacier, grassland, barren land and bush/shrub. This study used Landsat 5-TM (1990), Landsat 8-OLI (2015) satellite image and existing national land cover database of Nepal of the year 1990 to analyze LUCC and impact on ecosystem service values between 1990 and 2015. Supervised classification with maximum likelihood algorithm was applied to obtain the various land cover types. To estimate the ecosystem services values, this study used coefficients values of ecosystem services delivered by each land cover class. The combined use of GIS and remote sensing analysis has revealed that grassland and snow cover decreased from 10.62% to 7.62% and 9.55% to 7.27%, respectively compared to other land cover types during the 25 years study period. Conversely, cropland, forest and built-up area have increased from 31.78% to 32.67%, 32.47–33.22% and 0.19–0.59%, respectively in the same period. The total ecosystem service values (ESV) was increased from 50.16 × 108 USD y−1 to 51.84 × 108 USD y−1 during the 25 years in the GRB. In terms of ESV of each of land cover types, the ESV of cropland, forest, water bodies, barren land were increased, whereas, the ESV of snow/glacier and grassland were decreased. The total ESV of grassland and snow/glacier cover were decreased from 3.12 × 108 USD y−1 to 1.93 × 108 USD y−1 and 0.26 × 108 USD y−1 to 0.19 × 108 USD y−1, respectively between 1990 and 2015. The findings of the study could be a scientific reference for the watershed management and policy formulation to the trans-boundary watershed.

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Assessment of Land Use And Land Cover Change Detection And Prediction Using Remote Sensing And CA Markov In The Northern Coastal Districts of Tamil Nadu, India

10.21203/rs.3.rs-576976/v1 ◽

2021 ◽

Author(s):

Devanantham abijith ◽

Subbarayan Saravanan

Keyword(s):

Land Use ◽

Land Cover ◽

Environmental Sustainability ◽

Tamil Nadu ◽

Google Earth ◽

Geographical Information ◽

Change Analysis ◽

Lulc Change ◽

Study Region ◽

Barren Land

Abstract Land use and land cover (LULC) change analysis and forecasting aids the upcoming generation in research and evaluate the global climate change for managing and controlling environmental sustainability. This research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA- Markov model to forecast future LULC change. The CA-Markov’s Land change modeler (LCM) was set up to create future LULC. It was carried out in four steps: (1) Change analysis, (2) Transition potential, (3) Change prediction, and (4) Model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are under stress and developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all three years. This region is highly susceptible to inland floods, coastal floods, and other natural disasters; thus, this study’s results support future development plans and decision-making.

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Land Use Change and Urban Ecosystem Services: A Case Study of Urban Wetlands in a Rapidly Sprawling City in the Highlands of Chiapas, Mexico

Journal of Management and Sustainability ◽

10.5539/jms.v9n1p67 ◽

2019 ◽

Vol 9 (1) ◽

pp. 67

Author(s):

Vera Camacho-Valdez ◽

Eva M. Tello-Alcaide ◽

Allen Wootton ◽

Emmanuel Valencia-Barrera

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Land Cover ◽

Ecosystem Service ◽

Google Earth ◽

Land Use Land Cover ◽

Urban Wetlands ◽

Rapid Urbanization ◽

Wide Range ◽

The Impact

Urban wetlands provide a wide range of ecosystem services that are important for human-well-being. Despite their social and environmental importance, the degradation of urban wetlands continues mainly due to land use changes induced by rapid urbanization. Estimating the impact of these changes on ecosystem services is crucial to support the decision-making process of city planners at different levels. In this study, the spatial extents of the urban wetlands of San Cristobal de Las Casas, Chiapas, Mexico were determined for the years 2001 and 2018 in order to relate the spatial changes between these years with the provision and economic value of ecosystem services. Google Earth and SPOT imagery were used to evaluate land use/land cover changes while international coefficients were used to assess the value of the ecosystem services by category. Findings reveal a 7.3% decrease in the urban wetland area and a 12.5% increase of urban areas during the study period. The ecosystem service valuation shows that the total value flow decreased around $5 million (2007 USD) during the 17-year period, mainly due to decreases in the potential for regulating and cultural services. The use of freely available land use/land cover data together with global ecosystem service estimates reduce the cost of ground data collection and provides quick and reliable information that could help decision makers with land use planning in the context of data-scarce regions.

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Correction: Gu et al. Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine. Land 2021, 10, 173

Land ◽

10.3390/land10050506 ◽

2021 ◽

Vol 10 (5) ◽

pp. 506

Author(s):

Changjun Gu ◽

Yili Zhang ◽

Linshan Liu ◽

Lanhui Li ◽

Shicheng Li ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Ecosystem Service ◽

Google Earth ◽

Central Himalaya ◽

Land Cover Dynamics ◽

Google Earth Engine

In the original article [...]

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Spatiotemporal Analysis of Land Cover Changes in the Chemoga Basin, Ethiopia, Using Landsat and Google Earth Images

Sustainability ◽

10.3390/su12093607 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3607 ◽

Cited By ~ 3

Author(s):

Wubeshet Damtea ◽

Dongyeob Kim ◽

Sangjun Im

Keyword(s):

Land Cover ◽

Agricultural Land ◽

Environmental Concern ◽

Kappa Statistic ◽

Spatiotemporal Analysis ◽

Google Earth ◽

Agricultural Lands ◽

Landsat Images ◽

The Past ◽

Land Cover Maps

Land cover change is a major environmental concern in the northwestern highlands of Ethiopia. This study detected land cover transitions over the past 30 years in the Chemoga basin (total area = 118,359 ha). Land cover maps were generated via the supervised classification of Landsat images with the help of the Google Earth (GE) images. A total of 218 unchanged land features sampled from GE images were used as the training datasets. Classification accuracy was evaluated by comparing classified images with 165 field observations during the 2017 field visit. The overall accuracy was 85.4% and the kappa statistic was 0.81, implying that the land classification was satisfactory. Agricultural land is the dominant land cover in the study basin, and increased in extent by 2,337 ha from 1987 to 2017. The second and third most dominant land cover types, grassland and woodland, decreased by 1.9% and 3.6%, respectively, over the past 30 years. The increase in agricultural lands was mostly due to the conversion of grasslands and woodlands, although some agricultural lands changed to Eucalyptus plantations and human settlements. The results revealed that the expansion of built-up space and agricultural lands was the major driver of fragmentation of the landscape, and degradation of natural resources in the Chemoga basin, Ethiopia.

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