scholarly journals ESA SENTINEL 2 IMAGERY AND GBGEOAPP: INTEGRATED TOOLS FOR THE DEOSAI NATIONAL PARK MANAGEMENT PLAN

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 145-151
Author(s):  
M. T. Melis ◽  
F. Dessì ◽  
P. Loddo ◽  
A. Maccioni ◽  
M. Gallo ◽  
...  
Keyword(s):  
Land Cover ◽  
High Altitude ◽  
National Park ◽  
Brown Bear ◽  
Management Plan ◽  
Environmental Data ◽  
Mountain Ranges ◽  
Land Cover Map ◽  
Gilgit Baltistan ◽  
Sentinel 2

Abstract. Deosai plateau, in the Gilgit-Baltistan Province of Pakistan, for its average elevation of 4,114 meters, is the second highest plateau in the world after Changtang Tibetan Plateau. Two biogeographically important mountain ranges merge in Deosai: the Himalayan and Karakorum–Pamir highlands. The Deosai National Park, with its first recognition in 1993, encompasses an area of about 1620 km2, with the altitude ranging from 3500 to 5200 meters a.s.l. It is known and visited by tourists for the presence of brown bear, but a large number of species of fauna and flora leave, and can be seen during the summer season. This high-altitude ecosystem is particularly fragile and can be considered a sentinel for the effects of climate changes.Due to its geographic position and high altitude, the area of Deosai has never been studied in all its ecosystem components, producing high resolution maps. The first land cover map of Deosai with 10 meters of resolution is discussed in this study. This map has been obtained from Sentinel-2 imagery and improved through the new tool developed in this study: the GBGEOApp. This application for mobile has been done with three main ambitions: the validation of the new land cover map, its improvement with land use information, and the collection of new data in the field. On the basis of the results, the use of the GBGEOApp, as a tool for validation and increasing of environmental data collection, seems to be completely applicable involving the local technicians in a process of data sharing.

scholarly journals The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear

2021 ◽  
Vol 13 (6) ◽  
pp. 1138
Author(s):  
Pablo Cisneros-Araujo ◽  
Teresa Goicolea ◽  
María Cruz Mateo-Sánchez ◽  
Juan Ignacio García-Viñás ◽  
Miguel Marchamalo ◽  
...  
Keyword(s):  
Land Cover ◽  
Habitat Suitability ◽  
Brown Bear ◽  
Ecological Modeling ◽  
Environmental Data ◽  
Spatial Coverage ◽  
Wide Range ◽  
Spatial Datasets ◽  
Global Coverage ◽  
Connectivity Modeling

Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.

scholarly journals Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine

2020 ◽  
Vol 12 (21) ◽  
pp. 3663
Author(s):  
Meinan Zhang ◽  
Huabing Huang ◽  
Zhichao Li ◽  
Kwame Oppong Hackman ◽  
Chong Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Google Earth ◽  
Visual Interpretation ◽  
Entire Study ◽  
Heterogeneous Landscapes ◽  
Land Cover Map ◽  
Google Earth Engine ◽  
The Tropics ◽  
Sentinel 2

Madagascar, one of Earth’s biodiversity hotpots, is characterized by heterogeneous landscapes and huge land cover change. To date, fine, reliable and timely land cover information is scarce in Madagascar. However, mapping high-resolution land cover map in the tropics has been challenging due to limitations associated with heterogeneous landscapes, the volume of satellite data used, and the design of methodology. In this study, we proposed an automatic approach in which the tile-based model was used on each tile (defining an extent of 1° × 1° as a tile) for mapping land cover in Madagascar. We combined spectral-temporal, textural and topographical features derived from all available Sentinel-2 observations (i.e., 11,083 images) on Google Earth Engine (GEE). We generated a 10-m land cover map for Madagascar, with an overall accuracy of 89.2% based on independent validation samples obtained from a field survey and visual interpretation of very high-resolution (0.5–5 m) images. Compared with the conventional approach (i.e., the overall model used in the entire study area), our method enables reduce the misclassifications between several land cover types, including impervious land, grassland and wetland. The proposed approach demonstrates a great potential for mapping land cover in other tropical or subtropical regions.

scholarly journals A reclassification and description of the Waterberg Mountain vegetation of the Marakele National Park, Limpopo province, South Africa

Koedoe ◽  
2021 ◽  
Vol 63 (1) ◽  
Author(s):  
Petrus J. Van Staden ◽  
George J. Bredenkamp ◽  
Hugo Bezuidenhout ◽  
Leslie R. Brown
Keyword(s):  
South Africa ◽  
High Altitude ◽  
Plant Species ◽  
Plant Communities ◽  
National Park ◽  
Stocking Density ◽  
Management Plan ◽  
Limpopo Province ◽  
Mountain Vegetation ◽  
Floristic Data

The description and classification of vegetation are important for conservation and resource management. The aim of this study was to identify, reclassify and describe the plant communities present in the Waterberg Mountain vegetation of the Marakele National Park in the Limpopo province, South Africa. A phytosociological classification, mapping and description of sections of the Waterberg Mountain vegetation in the park were done in 1995. Since 1995, various farms adjacent to the park have been bought and incorporated into it. Little is known about the vegetation and habitat status of these newly acquired areas, which led to this study. The floristic data were analysed according to the Braun-Blanquet procedure using the Braun Blanquet Personal Computer (BBPC) suite as well as the JUICE software package, whilst the diversity of the plant communities was determined using the Shannon–Wiener and Gini–Simpson indices. A total of 12 plant communities were identified and are described according to their diagnostic, constant and dominant plant species as determined from the synoptic table analysis as well as their characteristic species as derived from the phytosociological table. Based on the topography and plant species composition, the vegetation can be grouped into five major groups, namely the: (1) lower midslope and plateau shrub- and woodlands, (2) high altitude midslope woodland, (3) high-lying plateau and midslope grass-, shrub- and woodlands, (4) ravine, footslope and drainage line forests and woodland, and (5) higher-lying plateau wetlands and forblands. The high altitude midslope grassland and shrubland and the lower midslope and plateau areas have the highest diversity. The high-lying vegetation has affinity with Bankenveld and Drakensberg vegetation, whilst the relatively low-lying plateaus and midslope vegetation are typical of the bushveld areas.Conservation implications: This reclassification, mapping and description of the Waterberg Mountain vegetation have been incorporated into the Management Plan for the park. It will enable managers to make scientifically based decisions on the management of the different ecosystems to ensure biodiversity conservation. This vegetation study also provides baseline information that allows for vegetation assessments to determine veld condition, carrying capacity and stocking density for the park.

Spatiotemporal effects of wastewater ponds from a geoenvironmental perspective in the Kharga region, Egypt

2019 ◽  
Vol 44 (3) ◽  
pp. 376-397 ◽  
Author(s):  
Mahmoud H Darwish ◽  
Wael F Galal
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Groundwater Resources ◽  
Management Plan ◽  
Water Levels ◽  
Environmental Data ◽  
Gis And Remote Sensing ◽  
Comprehensive Management ◽  
Surrounding Areas ◽  
The Impact

One of the major geoenvironmental problems in the Kharga region arises from the haphazard exploitation of groundwater resources and sewage dumping, which have resulted in wastewater accumulation in the form of ponds. The impact of the spatial expansion of wastewater ponds in Kharga and the surrounding area has been so pervasive that ponds have become a source of environmental degradation. These ponds are distributed throughout the area, but the major lakes are located in the eastern and southeastern provinces. The water levels of these ponds are rising at a remarkable rate, especially in the winter, when there is no evaporation and rainfall can lead to overflows that flow towards cities, villages and farmlands. As a result of untreated sewage inflows, all the low surrounding spaces are at high risk of being influenced by these ponds. The objectives of this study were to evaluate the spatiotemporal threats posed by wastewater ponds and develop a conceptual model to estimate the geoenvironmental impacts on the surrounding areas. GIS and remote sensing were used to process all available geological, topographical, hydrogeological, hydrological, land use and environmental data. The pond expansion trend was estimated from Landsat time series from 1984 to 2018, and the results indicated that the wastewater bodies continuously increased and the land cover percentage decreased. The encroachment of wastewater ponds has resulted in extensive land cover disturbances in recent years, and land use change has affected nearly 2.5% of the region. The complexity of the problems associated with wastewater ponds in the Kharga district requires a comprehensive management plan that is effective in not only maintaining the stability of the ponds but also in improving the sociocultural and economic conditions around the ponds. Specifically, the wastewater drainage and accumulation system should be managed according to the surrounding functional context.

scholarly journals Land Cover Classification of Complex Agroecosystems in the Non-Protected Highlands of the Galapagos Islands

2019 ◽  
Vol 12 (1) ◽  
pp. 65 ◽  
Author(s):  
Francisco J. Laso ◽  
Fátima L. Benítez ◽  
Gonzalo Rivas-Torres ◽  
Carolina Sampedro ◽  
Javier Arce-Nazario
Keyword(s):  
Land Cover ◽  
Invasive Plants ◽  
Mixed Forest ◽  
High Spectral Resolution ◽  
Object Based ◽  
Pioneer Plants ◽  
Land Cover Map ◽  
Aerial Vehicle ◽  
Sentinel 2

The humid highlands of the Galapagos are the islands’ most biologically productive regions and a key habitat for endemic animal and plant species. These areas are crucial for the region’s food security and for the control of invasive plants, but little is known about the spatial distribution of its land cover. We generated a baseline high-resolution land cover map of the agricultural zones and their surrounding protected areas. We combined the high spatial resolution of PlanetScope images with the high spectral resolution of Sentinel-2 images in an object-based classification using a RandomForest algorithm. We used images collected with an unmanned aerial vehicle (UAV) to verify and validate our classified map. Despite the astounding diversity and heterogeneity of the highland landscape, our classification yielded useful results (overall Kappa: 0.7, R2: 0.69) and revealed that across all four inhabited islands, invasive plants cover the largest fraction (28.5%) of the agricultural area, followed by pastures (22.3%), native vegetation (18.6%), food crops (18.3%), and mixed forest and pioneer plants (11.6%). Our results are consistent with historical trajectories of colonization and abandonment of the highlands. The produced dataset is designed to suit the needs of practitioners of both conservation and agriculture and aims to foster collaboration between the two areas.

scholarly journals Impact of Livestock Exclusion on Sidi Toui National Park Vegetation Communities, Tunisia

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mohamed Tarhouni ◽  
Farah Ben Salem ◽  
Azaiez Ouled Belgacem ◽  
Mohamed Neffati
Keyword(s):  
Land Cover ◽  
National Park ◽  
Landscape Heterogeneity ◽  
Diversity Index ◽  
Environmental Indicators ◽  
Vegetation Dynamic ◽  
Restoration Technique ◽  
Land Cover Map ◽  
The One ◽  
Impact On Biodiversity

The restoration technique importance resides on the assessment of its impact on biodiversity. This assessment is possible by the use of some environmental indicators extracted from a diachronic study of land cover changes in protected areas. Our study is carried out with the evaluation of some indicators inside Sidi Toui national park. These indicators are measured on the one hand from a land cover map of 1988 (3 years before the creation of the park) and the map of 2007 on the other hand (16 years after the park creation). An important landscape heterogeneity, as a result of the progressive vegetation dynamic, was observed in 2007. This heterogeneity is indicated by an increasing of the Shannon diversity index under fencing impacts. The majority of 1988 vegetation units are replaced by new ones in 2007. The cover of all vegetation units is more important in 2007.

scholarly journals LAND USE / LAND COVER MAPPING FROM SENTINEL 2 DATA USING MACHINE LEARNING ALGORITHMS

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Gordana Jakovljević
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Learning Algorithms ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Remotely Sensed Data ◽  
Kappa Value ◽  
Land Cover Map ◽  
Moderate Resolution ◽  
Sentinel 2

Land cover/land use (LULC) have an important impact on land degradation,erosion and water availability therefore mapping of patterns and spatial distribution ofLULC is essential for land management. Accurate mapping of complex land cover andland use classes using remotely sensed data requires robust classification methods.Various classification algorithms and satellite images have been used in recent years. Forthis study, moderate resolution Sentinel-2 image was used. In order to evaluate thepotential of the input image and derive land cover map in complex urban area of BanjaLuka, Republic of Srpska with highest possible precision, two machine learningalgorithms where applied: Supported Vector Machines (SVM) and Random Forst (RF).An overall classification accuracy of 90,82% with kappa value of 0,87 and 88,29 withkappa value of 0,84 was achieved using SVM and RF. The study showed that ofmachine learning algorithms on Sentinel-2 imagery can results in accurate land covermaps.

scholarly journals Using machine learning to produce a very high resolution land-cover map for Ireland

2021 ◽  
Vol 18 ◽  
pp. 65-87
Author(s):  
Eoin Walsh ◽  
Geoffrey Bessardon ◽  
Emily Gleeson ◽  
Priit Ulmas
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Learning Algorithm ◽  
Corine Land Cover ◽  
Flood Plains ◽  
Novel Approach ◽  
Weather And Climate ◽  
Land Cover Map ◽  
Very High ◽  
Sentinel 2

Abstract. Land-cover classifications in the form of maps are required for numerical modelling of weather and climate. Such maps are often of coarse resolution and are infrequently updated. Here we propose a novel approach for land-cover classification using a Convolutional Neural Network machine learning algorithm to segment satellite images into various land-cover classes. Sentinel-2 satellite imagery, the CORINE land-cover database and the BigEarthNet dataset are used. A 10 m resolution map, called the Ulmas-Walsh map, has been created for Ireland that outperforms ECO-SG in terms of accuracy, as well as demonstrating a capacity for identifying features not labelled correctly in CORINE. The map can be updated on demand for any time of the year, subject to cloud cover. This is particularly useful for regions with large seasonal variation in land classifications such as Turloughs – seasonal lakes, flood plains and rotational crops.

scholarly journals Analysis of a Landscape Intensely Modified by Agriculture in the Tietê–Jacaré Watershed, Brazil

2021 ◽  
Vol 13 (16) ◽  
pp. 9304
Author(s):  
Diego Peruchi Trevisan ◽  
Polyanna da Conceição Bispo ◽  
Yaqing Gou ◽  
Bianca Fogaça de Souza ◽  
Veraldo Liesenberg ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Driving Forces ◽  
Temporal Analysis ◽  
Environmental Data ◽  
Kappa Index ◽  
Paulo State ◽  
Proposed Model ◽  
Environmental Responses ◽  
Sentinel 2

Anthropogenic actions influence landscapes, and the resulting mosaic is a mix of natural and anthropogenic elements that vary in size, shape, and pattern. Considering this, our study aimed to analyse the land use and land cover changes in the Tietê–Jacaré watershed (São Paulo state, Brazil), using the random forest (RF) algorithm and Sentinel-2 satellite data from 2016 to 2018 to detect landscape changes. By overlapping the environmental data and the proposed model evaluation, it was possible to observe the landscape structure, produce information about the state of this region, and assess the environmental responses to anthropic impacts. The land use and land cover analysis identified eight classes: exposed soil, citriculture, pasture, silviculture, sugar cane, urban area, vegetation, and water. The RF classification for the three years reached high accuracy with a kappa index of 0.87 in 2016, 0.85 in 2017, and 0.85 in 2018. The model developed was essential for the temporal analysis since it allowed us to comprehend the driving forces that act in this landscape and contribute to the discussions about their impacts over time. The results showed a predominance of agricultural activities over the three years, with approximately 900.000 ha (76% of the area), mainly covered by sugarcane cultivation.

Sign in / Sign up

Export Citation Format

Share Document