scholarly journals Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leonardo Fiusa de Morais ◽  
Ana Clara Rodrigues Cavalcante ◽  
Deodato do Nascimento Aquino ◽  
Felipe Hermínio Meireles Nogueira ◽  
Magno José Duarte Cândido
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Google Earth ◽  
Political Support ◽  
Classification Model ◽  
Landsat 8 ◽  
Kappa Index ◽  
Infrared Band ◽  
Caatinga Biome ◽  
Spectral Responses

AbstractThis study aimed to analyze fragments of rangelands through spectral responses and land cover change by livestock in regions of the Caatinga biome through remote sensing. For spectral behavior, the surface reflectance bidirectional (SRB) and spectral indexes of vegetation were used to verify the ragelands seasonality. Land cover change detection of Ouricuri and Tauá through Landsat-8 images with a 16-day revisit interval, were processed in the Google Earth Engine platform (GEE) and software Quantum GIS version 2.18 (QGIS). In the GEE platform, annual mosaics and stacking of the spectral bands were generated for the classification of images, and in sequence the production of thematic maps in QGIS. The analysis of land cover change considered the classes: thinned Caatinga, conserved Caatinga, herbaceous vegetation, bare soil, water and others. The analysis of the spectral responses showed that the vegetation monitored in Ouricuri presented higher SRB in the infrared band and lower SRB in the red and blue bands, and that caused the pasture to produce higher vegetation indexes than the other locations. Through validation, it was observed that in Tauá, there was an overall accuracy of 91% and Kappa index of 89%, and in Ouricuri there was an overall accuracy of 90% and Kappa index of 86%, indicating excellent correctness of the classification model. The classification model proved to be effective in verifying the temporal and spatial land cover change, making it possible to identify places with the vegetation that was most affected and susceptible to degradation and generation of political support to minimize damage to the Caatinga Biome.

scholarly journals Land use and land cover change-induced landscape dynamics: a geospatial study of Durgapur Sub-Division, West Bengal (India).

2021 ◽  
Author(s):  
Sribas Patra ◽  
Kapil Kumar Gavsker
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Supervised Classification ◽  
West Bengal ◽  
Google Earth ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Process Of Change ◽  
Detection Analysis

Abstract This article examines the factors and process of change in the land use and land cover change-induced landscape dynamics in the Durgapur Sub-Division region of West Bengal in 1989, 2003, and 2018 by employing the satellite imageries of Landsat 5 (1989 and 2003) and Landsat 8 (2018). The images of the study area were categorized into seven specific land use classes with the help of Google Earth Pro. Based on the supervised classification methodology, the change detection analysis identified a significant increase in built-up land from 11% to 23% between 1989 and 2003 and from 23% to 29% in 2003 and 2018. The areas under fallow land and vegetation cover have mainly decreased, while the areas of industrial activities and urbanization expanded during the study period.

scholarly journals Supervised Classification Model Using Google Earth Engine Development Environment for Wasit Governorate

2022 ◽  
Vol 961 (1) ◽  
pp. 012051
Author(s):  
Sajjad H Hasan ◽  
Amjed N M AL-Hameedawi ◽  
H S Ismael
Keyword(s):  
Land Cover ◽  
Satellite Image ◽  
Large Data ◽  
Bare Soil ◽  
Google Earth ◽  
Machine Intelligence ◽  
Classification Model ◽  
Landsat 8 ◽  
Development Environment ◽  
Google Earth Engine

Abstract As a result of the advancements that have occurred in the technical field of geomatics, particularly after the development of developmental programming environments, they have become the most important machine for conducting image analyses of satellite data, creating and modifying spatial analysis tools, and performing large data analyses at a fast rate without the need for high-end specifications on the personal computer. This study has several objectives, including the definition and popularization of the use of the power of Google Earth Engine (GEE) in the speed of conducting spatial analyzes, which cite by conducting a classification at the level of a governorate and obtaining results with speed and relatively good quality. By using the Google Earth Engine (GEE) platform and through Javascript programming language, a classification of the land cover of Wasit Governorate, Iraq was created under the supervision of a satellite image (Landsat 8) by creating a training sample, Google Maps’ High Resolution basemap imagery was used to create this map to identify classes of landcover (water, bare soil, vegetation, and urban). Each source pixel is assigned to one of the previously mentioned classes. Then to create a land cover map of the region using the Statistical Machine Intelligence and Learning Engine (SMILE) classifier from the JAVA library, which is used by Google Earth Engine (GEE) to implement these algorithms. The result is an array of pixels (raster data). The pixel value represents the class that was previously determined by the samples.

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 Change Investigation in the Southern Syrian Coastal Basins During the Past 30-Years Using Landsat Remote Sensing Data

2018 ◽  
Vol 11 (1-2) ◽  
pp. 45-51 ◽  
Author(s):  
Muhannad Hammad ◽  
László Mucsi ◽  
Boudewijn van Leeuwen
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Coastal Basins ◽  
Major Increase ◽  
Temporal And Spatial ◽  
Land Cover Maps ◽  
Conflict Period

Abstract Land cover change and deforestation are important global ecosystem hazards. As for Syria, the current conflict and the subsequent absence of the forest preservation are main reasons for land cover change. This study aims to investigate the temporal and spatial aspects and trends of the land cover alterations in the southern Syrian coastal basins. In this study, land cover maps were made from surface reflectance images of Landsat-5(TM), Landsat-7(ETM+) and Landsat-8(OLI) during May (period of maximum vegetation cover) in 1987, 2002 and 2017. The images were classified into four different thematic classes using the maximum likelihood supervised classification method. The classification results were validated using 160 validation points in 2017, where overall accuracy was 83.75%. Spatial analysis was applied to investigate the land cover change during the period of 30 years for each basin and the whole study area. The results show 262.40 km2 reduction of forest and natural vegetation area during (1987-2017) period, and 72.5% of this reduction occurred during (2002-2017) period due to over-cutting of forest trees as a source of heating by local people, especially during the conflict period. This reduction was particularly high in the Alabrash and Hseen basins with 76.13 and 79.49 km2 respectively, and was accompanied by major increase of agriculture lands area which is attributed to dam construction in these basins which allowed people to cultivate rural lands for subsistence or to enhance their economic situation. The results of this study must draw the relevant authorities’ attention to preserve the remaining forest area.

scholarly journals Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Trinh Le Hung
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Urban Land ◽  
Landsat 8 ◽  
Land Classification ◽  
Infrared Band ◽  
Urban Land Cover ◽  
Bare Land ◽  
Sentinel 2

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.

scholarly journals Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing

2020 ◽  
Vol 12 (18) ◽  
pp. 2883
Author(s):  
Theodomir Mugiraneza ◽  
Andrea Nascetti ◽  
Yifang Ban
Keyword(s):  
Growth Rate ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Land ◽  
Google Earth ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Urban Land Cover ◽  
Urban Land Cover Change ◽  
Land Cover Maps

Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed on the two Landsat scenes, respectively, acquired in 1987 and 2019 over Kigali, Rwanda. The resulting land cover maps were then imported in the GEE platform and used to label the interannual LandTrendr-derived changes. The changes in duration, year, and magnitude of land cover disturbance were derived from six different indices/bands using the LandTrendr algorithm. The interannual change LandTrendr results were then combined using a robust estimation procedure based on principal component analysis (PCA) for reconstructing the annual land cover change maps. The produced yearly land cover maps were assessed using validation data and the GEE-based Area Estimation and Accuracy Assessment (Area2) application. The results were used to study the Kigali’s urbanization in the last three decades since 1987. The results illustrated that from 1987 to 1998, the urbanization was characterized by slow development, with less than a 2% annual growth rate. The post-conflict period was characterized by accelerated urbanization, with a 4.5% annual growth rate, particularly from 2004 onwards due to migration flows and investment promotion in the construction industry. The five-year interval analysis from 1990 to 2019 revealed that impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7% average annual growth rate. The proposed scheme was found to be cost-effective and useful for continuously monitoring the complex urban land cover dynamics, especially in environments with EO data affordability issues, and in data-sparse regions.

scholarly journals Identifying Climatic Variables with Rice Yield Relationship and Land Cover Change Detection at Sylhet Region

2019 ◽  
pp. 1-12 ◽  
Author(s):  
Gazi Tamiz Uddin ◽  
Md. Altaf Hossain ◽  
Fahmida Ishaque
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Land Cover Change ◽  
Rice Yield ◽  
Flash Flood ◽  
Climatic Variables ◽  
Landsat 8 ◽  
Yield Data ◽  
Vegetation Condition ◽  
Boro Rice

The study is conducted to determine the correlation between climatic parameters and rice yield. The present study is also undertaken to analyze the land cover change in Sylhet district between 2013 and 2018 using LANDSAT-8 images. Local climate and rice yield data are collected from BMD (Bangladesh Meteorological Department) and BRRI (Bangladesh Rice Research Institute) and BBS (Bangladesh Bureau of Statistics). ArcGIS 10.5 and SPSS software are used to show the vegetation condition and correlation coefficient between rice yield and climatic variables respectively. It is revealed from the result that rainfall is negatively correlated with Aman and Boro (local and HYV) rice whereas temperature and relative humidity showed a positive correlation with local Aman and Boro rice. On the other hand, relative humidity showed a strong linear relationship with HYV Boro rice. Finally, both temperature and relative humidity have substantial effects on yields in the Boro rice. Furthermore, vegetation condition is observed through NDVI and found the moderate-high vegetation in 2013. After that NDVI value is fluctuating which evidently signifies the rapid vegetation cover change due to a flash flood, flood and other climate changing aspects. Additionally, Forested and high land vegetation’s are endangered rapidly. Some adaptation strategies should be followed to minimize the effects of natural calamities for improving better vegetation condition.

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