scholarly journals Isolating Anthropogenic Wetland Loss by Concurrently Tracking Inundation and Land Cover Disturbance across the Mid-Atlantic Region, U.S.

2020 ◽  
Vol 12 (9) ◽  
pp. 1464
Author(s):  
Melanie K. Vanderhoof ◽  
Jay Christensen ◽  
Yen-Ju G. Beal ◽  
Ben DeVries ◽  
Megan W. Lang ◽  
...  
Keyword(s):  
Land Cover ◽  
Wetland Loss ◽  
Google Earth ◽  
Atlantic Region ◽  
Dynamic Surface ◽  
Global Trends ◽  
Forested Areas ◽  
Wetland Inventory ◽  
Wetland Extent ◽  
Inundation Extent

Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey’s Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015–2018) disturbance averaged 0.32% (1095 km2 year-1) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km2 over the four-year period, and 186 km2, using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands.

scholarly journals Relationship between Fire Events and Land Use Changes in the State of São Paulo, Brazil

2021 ◽  
Vol 13 (15) ◽  
pp. 2853
Author(s):  
Sheena Philogene ◽  
Wenge Ni-Meister
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
Sao Paulo ◽  
Google Earth ◽  
The State ◽  
Direct Result ◽  
São Paulo ◽  
Forested Areas ◽  
In Fire

This study investigated the land use and land cover changes in the state of São Paulo, Brazil, for the period of 2002 through 2017, to determine if forested areas were burned or converted to other land uses, to analyze the use of fire as a catalyst and mechanism for land cover change, and to determine if there was a relationship between land use changes and gross domestic product (GDP). MapBiomas classifications and MODIS data were analyzed using the Google Earth Engine. The results of the analysis found that there were minimal changes in the forested areas in São Paulo during the study period; however, there was a 5% increase in natural forest and a 75% increase in planted forest cover. On the other hand, there was a 128% increase in sugarcane, and nearly a 50% decrease in pasture land coverage, suggesting that land was converted from pasture to more profitable agricultural land. Finally, there was a strong positive correlation (r = 0.96) between the increase in sugarcane and the GDP, and a negative correlation between the frequency of fire events and economic production (r = −0.62). Overall, there was a decline in fire events in São Paulo, with fire events occurring in less than 2% of the total observed land area by 2017. This overall declining trend in fire events are likely the direct result of increases in green harvest methods, which prevent the need for pre-harvest burning.

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

2021 ◽  
Vol 3 (3) ◽  
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.

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Zander Venter ◽  
Markus Sydenham
Keyword(s):  
Land Cover ◽  
Atmospheric Correction ◽  
Google Earth ◽  
Tree Planting ◽  
Training Data ◽  
Classification Model ◽  
Night Time ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

scholarly journals Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

2021 ◽  
Vol 13 (9) ◽  
pp. 1743
Author(s):  
Daniel Paluba ◽  
Josef Laštovička ◽  
Antonios Mouratidis ◽  
Přemysl Štych
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Forest Type ◽  
Incidence Angle ◽  
Correction Method ◽  
Google Earth ◽  
Forest Change ◽  
Corine Land Cover ◽  
Angle Correction ◽  
Wide Range

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

scholarly journals 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 ◽  
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.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals ANALYSIS OF LAND COVER CHANGES IN THE AREA OF THE TOWN OF GORAŽDE

2020 ◽  
Vol 43 ◽  
pp. 37-46
Keyword(s):  
Land Cover ◽  
Spatial Planning ◽  
Geographical Location ◽  
Research Area ◽  
Google Earth ◽  
Land Cover Changes ◽  
The Third ◽  
Complete Procedure ◽  
The Subject ◽  
The Town

The subject of the research is the analysis of the land cover changes in the area of the town of Goražde. In the period after 1995, throughout the entire Bosnia and Herzegovina, a visible spontaneous spatial planning contributed to significant changes in the land cover. The research aims to determine how many changes have taken place in the last few decades in the researched area when it comes to land cover. The analysis is based on topographic maps from 1980, Google Earth images, then Corina Land Cover images from 2000, 2006, 2012, and 2018. Research on the topic was conducted through several phases. The first part of the paper refers to determining the geographical location of the research area and defining all its components. The second part of the paper deals with a detailed analysis of all categories of land cover in the last twenty years. The third part of the paper involves a comparative analysis of quantitative and qualitative indicators of land cover in the town of Goražde. The complete procedure of the analysis was performed using GIS, where the corresponding databases were created and a cartographic visualization for the investigated area was performed.

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