scholarly journals Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

2020 ◽  
Vol 12 (22) ◽  
pp. 3729
Author(s):  
Leon T. Hauser ◽  
Nguyen An Binh ◽  
Pham Viet Hoa ◽  
Nguyen Hong Quan ◽  
Joris Timmermans
Keyword(s):  
Land Use ◽  
Mangrove Forest ◽  
Temporal Dynamics ◽  
Mekong Delta ◽  
Google Earth ◽  
Data Availability ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Landsat 7

Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

scholarly journals Deteksi Deforestasi Menggunakan Citra Satelit Resolusi Tinggi Pada Lanskap Hutan Mangrove Percut Sei Tuan

2018 ◽  
Vol 1 (2) ◽  
pp. 213-217
Author(s):  
Nurdin Sulistiyono ◽  
Pindi Patana ◽  
Achmad Siddik Thoha ◽  
Khairil Amri ◽  
Onrizal Onrizal
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Mangrove Forest ◽  
Satellite Images ◽  
Methodological Approach ◽  
Google Earth ◽  
Forest Landscape ◽  
Mangrove Forests ◽  
High Resolution Satellite Imagery ◽  
Spatial Plan

Hutan mangrove telah diketahui memiliki peran penting sebagai penyangga kehidupan kawasan pantai dan mata rantai penghubung ekosistem daratan dan ekosistem laut. Namun demikian tekanan terhadap lanskap hutan mangrove dirasakan semakin besar tiap tahunnya dalam bentuk deforestasi. Pemanfaatan citra satelit resolusi tinggi dari Google Earth dapat digunakan untuk kegiatan monitoring lanskap hutan mangrove dalam skala yang lebih detail. Penelitian ini bertujuan untuk mengidentifikasi deforestasi serta kesesuaian pola peruntukan ruang pada lanskap hutan mangrove di Percut Sei Tuan pada periode tahun 2011 - 2016.  Pendekatan metodelogi yang digunakan adalah dengan menggunakan post classificaion comparism dengan metode on screen pada citra satelit dari google earth.  Hasil penelitian menunjukan besarnya laju deforestasi pada lanskap hutan mangrove sebesar 77,43ha atau sebesar 15,43 ha/tahun. Penggunaan lahan pada lanskap hutan mangrove di Percut Sei Tuan pada tahun 2011 dan 2016 tidak sesuai dengan peruntukan lahan sebagaimana yang tercantum dalam Rencana Tata Ruang Wilayah Kabupaten Deli Serdang.   Mangrove forests have been known to have an important role as a buffer to the life of coastal areas and links to land ecosystems and marine ecosystems. However, the pressure on the landscape of mangrove forests is greater each year in the form of deforestation. The use of high-resolution satellite imagery from Google Earth can be used to monitor the landscape of mangrove forests on a more detailed scale. This study aims to identify deforestation and the suitability of space designation patterns in the mangrove forest landscape in Percut Sei Tuan in the period 2011 - 2016. The methodological approach used is to use post classificaion comparism with on-screen methods on satellite images from Google Earth. The results showed that the rate of deforestation in the mangrove forest landscape was 77.43 ha or 15.43 ha / year. Land use in the mangrove forest landscape in Percut Sei Tuan in 2011 and 2016 is not in accordance with the land allotment as stated in the Deli Serdang Regency Spatial Plan.

scholarly journals Agricultural land use and cover change in the Cerrado/Amazon ecotone: A case study of the upper Teles Pires River basin

2018 ◽  
Vol 48 (2) ◽  
pp. 168-177 ◽  
Author(s):  
Ana Paula Sousa Rodrigues ZAIATZ ◽  
Cornélio Alberto ZOLIN ◽  
Laurimar Goncalves VENDRUSCULO ◽  
Tarcio Rocha LOPES ◽  
Janaina PAULINO
Keyword(s):  
Land Use ◽  
River Basin ◽  
Large Scale ◽  
Agricultural Land ◽  
Temporal Dynamics ◽  
Direct Conversion ◽  
Landsat 8 ◽  
Error Matrix ◽  
Mato Grosso ◽  
Land Use And Cover

ABSTRACT The upper Teles Pires River basin is a key hydrological resource for the state of Mato Grosso, but has suffered rapid land use and cover change. The basin includes areas of Cerrado biome, as well as transitional areas between the Amazon and Cerrado vegetation types, with intensive large-scale agriculture widely-spread throughout the region. The objective of this study was to explore the spatial and temporal dynamics of land use and cover change from 1986 to 2014 in the upper Teles Pires basin using remote sensing and GIS techniques. TM (Thematic Mapper) and TIRS (Thermal Infrared Sensor) sensor images aboard the Landsat 5 and Landsat 8, respectively, were employed for supervised classification using the “Classification Workflow” in ENVI 5.0. To evaluate classification accuracy, an error matrix was generated, and the Kappa, overall accuracy, errors of omission and commission, user accuracy and producer accuracy indexes calculated. The classes showing greatest variation across the study period were “Agriculture” and “Rainforest”. Results indicated that deforested areas are often replaced by pasture and then by agriculture, while direct conversion of forest to agriculture occured less frequently. The indices with satisfactory accuracy levels included the Kappa and Global indices, which showed accuracy levels above 80% for all study years. In addition, the producer and user accuracy indices ranged from 59-100% and 68-100%, while the errors of omission and commission ranged from 0-32% and 0-40.6%, respectively.

National-scale mangrove forest mapping by using Sentinel-1 SAR and Sentinel-2 MSI imagery on the Google Earth Engine Platform

2020 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Lei Wang
Keyword(s):  
Spatial Resolution ◽  
Mangrove Forest ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Accuracy Evaluation ◽  
Mangrove Forests ◽  
Forest Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Mangrove forest is considered as one of the pivotal ecosystems to near-shore environment health, adjacent terrestrial ecosystems and even global climate change migration. However, for past two decades, they are declining rapidly. In order to take effective steps to prevent the extinction of mangroves, high spatial resolution information of large-scale mangrove distribution is urgent. Recent study has indicated that a suitable pixel size for extracting mangroves should be at least equal to 10 m. Hence, Sentinel imagery (Sentinel-1 C-band synthetic aperture radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery) whose spatial resolution is 10 m may hold great potentials to achieve this goal, but there are limited researches investigating it. Therefore, in this study, we will explore the potential of Sentinel imagery to extract mangrove forests in China on the Google Earth Engine platform. Specifically, our study was mainly conducted around 3 questions: (1) Which Sentinel imagery provides a higher accuracy for mangrove forest mapping, Sentinel-1 SAR data or Sentinel-2 multi-spectral data? (2) which combination of features from Sentinel imagery provides the most accurate mangrove forest map? (3) Compared to 30-m resolution mangrove products derived from Landsat imagery, how does 10-m resolution map improve our knowledge about the distribution of mangrove forest in China?</p><p> </p><p>Our results show that: (1) The highest producer’s accuracies (the reason why using producer’s accuracy as an accuracy evaluation indicator here is that the omission errors in mangrove forest extent map are much larger than commission errors) of mangrove forest maps derived from Sentinel-1 and Sentinel-2 imagery are 91.76% and 90.39%, respectively, which means that the contributions of Sentinel-1 SAR and Sentinel-2 MSI imagery to mangrove mapping are similar; (2) The highest producer’s accuracy of mangrove forest map at 10-m resolution is 95.4%. The mangrove forest map with the highest accuracy is obtained by combining quantiles of spectral and backscatter bands, spectral index, and texture index derived from time series of Sentinel-1 and Sentinel-2 imagery, indicating that the combination of Sentinel-1 SAR and Sentinel-2 MSI imagery is more useful in mangrove forest mapping than using them separately; (3) In China, the total area of mangrove forest extent at 10-m resolution is similar to that at 30-m resolution (20003 ha vs. 19220 ha). However, compared to 30-m resolution mangrove products, the 10-m resolution mangrove map identifies 1741 ha (occupying 8.7% of total mangrove forest area in China) mangrove forests in size smaller than 1 ha, which are especially important to low-lying coastal zone. This study demonstrates the feasibility of Sentinel imagery in large-scale mangrove forest mapping and gives guidance to map global mangrove forest at 10-m resolution in the future.  </p><p> </p>

scholarly journals Remote Sensing Application for Exploring Land Use and Land Cover Dynamics in and Around Chatra Wetland, English Bazar, West Bengal

2021 ◽  
Vol 9 (1) ◽  
pp. 15-27
Author(s):  
Saleha Jamal ◽  
Md Ashif Ali
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
United States Geological Survey ◽  
Landsat 8 ◽  
Gis And Remote Sensing ◽  
Erdas Imagine ◽  
Landsat 7 ◽  
Vegetation Water

Wetlands are often called as biological “supermarket” and “kidneys of the landscape” due to their multiple functions, including water purification, water storage, processing of carbon and other nutrients, stabilization of shorelines and support of aquatic lives. Unfortunately, although being dynamic and productive ecosystem, these wetlands have been affected by human induced land use changes. India is losing wetlands at the rate of 2 to 3 per cent each year due to over-population, direct deforestation, urban encroachment, over fishing, irrigation and agriculture etc (Prasher, 2018). The present study tries to investigate the nature and degree of land use/land cover transformation, their causes and resultant effects on Chatra Wetland. To fulfil the purpose of the study, GIS and remote sensing techniques have been employed. Satellite imageries have been used from United States Geological Survey (USGS) Landsat 7 Enhanced Thematic Mapper plus and Landsat 8 Operational Land Imager for the year 2003 and 2018. Cloud free imageries of 2003 and 2018 have been downloaded from USGS (https://glovis.usgs.gov/) for the month of March and April respectively. Image processing, supervised classificationhas been done in ArcGis 10.5 and ERDAS IMAGINE 14. The study reveals that the settlement hasincreased by about 90.43 per cent in the last 15 years around the Chatra wetland within the bufferzone of 2 Sq km. Similarly agriculture, vegetation, water body, swamp and wasteland witnessed asignificant decrease by 5.94 per cent, 57.69 per cent, 26.64 per cent 4.52 per cent and 55.27 per centrespectively from 2003 to 2018.

scholarly journals Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Xue Liu ◽  
Temilola E. Fatoyinbo ◽  
Nathan M. Thomas ◽  
Weihe Wendy Guan ◽  
Yanni Zhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
West Africa ◽  
Spatial Resolution ◽  
Mangrove Forest ◽  
Large Scale ◽  
Google Earth ◽  
Mangrove Forests ◽  
Data Products ◽  
Sentinel 2

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

scholarly journals Land use and land cover changes in the Lower Neretva Region from 1990 to 2020

2021 ◽  
Vol 83 (2) ◽  
pp. 7-31
Author(s):  
Josip Šetka ◽  
◽  
Petra Radeljak Kaufmann ◽  
Luka Valožić ◽  
◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Confusion Matrix ◽  
Hybrid Approach ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Economic Changes ◽  
Sparse Vegetation ◽  
Landsat 7

Changes in land use and land cover are the result of complex interactions between humans and their environment. This study examines land use and land cover changes in the Lower Neretva Region between 1990 and 2020. Political and economic changes in the early 1990s resulted in changes in the landscape, both directly and indirectly. Multispectral image processing was used to create thematic maps of land use and land cover for 1990, 2005, and 2020. Satellite images from Landsat 5, Landsat 7 and Landsat 8 were the main source of data. Land use and land cover structure was assessed using a hybrid approach, combining unsupervised and manual (visual) classification methods. An assessment of classification accuracy was carried out using a confusion matrix and kappa coefficient. According to the results of the study, the percentage of built-up areas increased by almost 33%. Agricultural land and forests and grasslands also increased, while the proportion of swamps and sparse vegetation areas decreased.

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r<sup>2</sup>&thinsp;=&thinsp;0.7 and r<sup>2</sup>&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals Remote Sensing and GIS based Land Use Land Cover Analysis in Chandel District, Manipur, India

2021 ◽  
Vol 889 (1) ◽  
pp. 012046
Author(s):  
Ashangbam Inaoba Singh ◽  
Kanwarpreet Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Geographical Area ◽  
Temporal Trends ◽  
Water Bodies ◽  
Landsat 8 ◽  
Rapid Urbanization ◽  
Barren Area ◽  
Landsat 7

Abstract Rapid urbanization has dramatically altered land use and land cover (LULC). The focus of this research is on the examination of the last two decades. The research was conducted in the Chandel district of Manipur, India. The LULC of Chandel (encompassing a 3313 km2 geographical area) was mapped using remotely sensed images from LANDSAT4-5, LANDSAT 7 ETM+, and LANDSAT 8 (OLI) to focus on spatial and temporal trends between years 2000 and 2021. The LULC maps with six major classifications viz., Thickly Vegetated Area (TVA), Sparsely Vegetated Area (SVA), Agriculture Area (AA), Population Area (PA), Water Bodies (WB), and Barren Area (BA) of the were generated using supervised classification approach. For the image classification procedure, interactive supervised classification is adopted to calculate the area percentage. The results interpreted that the TVA covers approximately 65% of the total mapped area in year 2002, which has been decreased up to 60% in 2007, 56% in 2011, 55 % in 2017, and 52% in 2021. The populated area also increases significantly in these two decades. The change and increase in the PA has been observed from year 2000 (8%) to 2021 (11%). Water Bodies remain same throughout the study period. Deforestation occurs as a result of the rapid rise of the population and the extension of the territory.

scholarly journals Impact of land use change on hydrological conditions in the Karajae watershed, South Sulawesi Province

2021 ◽  
Vol 886 (1) ◽  
pp. 012079
Author(s):  
Chairil A ◽  
Syamsu Rijal ◽  
Munajat Nursaputra ◽  
Muh. Faisal Mappiase
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Image Data ◽  
Water Source ◽  
Assessment Tools ◽  
Landsat 8 ◽  
Dryland Agriculture ◽  
Land Use Patterns ◽  
Hydrological Conditions ◽  
Landsat 7

Abstract Land use is a representation of activities and utilization of land resources by the community. Land use has a big influence on the hydrological condition of a watershed. One of the small watersheds, in general, is the Karajae watershed, but it has a very large impact on the City of Pare-Pare, and the surrounding community. The Karajae watershed is the main water source for the people of Pare-Pare and agriculture. This study aims to analyze land use patterns that have a major impact on hydrological conditions in the Karajae watershed. The analysis begins with remote sensing methods to interpret land use using Landsat 7 image data in 2010 and Landsat 8 imagery in 2020. Next, analyze the pattern of land use change in detail in each forest area with a geographic information system approach. Analysis of hydrological conditions using the Soil and Water Assessment Tools approach with the input of the land use data. Land use Change 2010-2020 in the Karajae watershed shows additional land use in the form of settlements, rice fields, and dryland agriculture as a form of community activity. There are two forest areas in the Karajae watershed, namely production forest and protected forest. Production forest is dominated by dryland agriculture in the form of corn, beans, and horticulture, while the protected forest is dominated by and secondary dryland forest. This has an impact on hydrological conditions that there are fluctuations in discharge and an increase in sediment a decade ago. Optimal application of forest functions reduces discharge and sediment. Different forest planning for each forest function and land use within. Production forest with many activities directed towards community-based forest management such as community forest and village forest. As for the Protected Forest, which is dominated by grassland and shrubs, forest rehabilitation is carried out.

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