scholarly journals Statistical assessment of vegetation dynamics within protected areas using remote sensing data

2013 ◽  
Vol 44 (1) ◽  
pp. 5
Author(s):  
Maria Elena Menconi ◽  
David Grohmann
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Vegetation Dynamics ◽  
Remote Sensing Data ◽  
Statistical Assessment ◽  
Sensing Data

scholarly journals Statistical assessment of vegetation dynamics within protected areas using remote sensing data

2013 ◽  
Vol 43 (4) ◽  
pp. 5
Author(s):  
Maria Elena Menconi ◽  
David Grohmann
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Photosynthetic Activity ◽  
Remote Sensing Data ◽  
Ratio Method ◽  
Vegetation Development ◽  
Sensing Data ◽  
Landscape Factors

This study aimed to test the effectiveness of protected areas to preserve vegetation. The first step was to identify vegetation suitable areas, designed as areas with optimal morphological terrain features for a good photosynthetic activity. These areas were defined according to the following landscape factors: slope, altitude, aspect and land use. Enhanced vegetation index (EVI) was chosen as vegetation dynamics indicator. This method is based on a statistical approach using remote sensing data in a geographic information system (GIS) environment. The correlation between EVI and landscape factor was evaluated using the frequency ratio method. Classes of landscape factors that show good correlation with a high EVI were combined to obtain vegetation suitable areas. Once identified, these areas and their vegetation dynamics were analysed by comparing the results obtained whenever these areas are included or not included in protected areas. A second EVI dataset was used to verify the accuracy in identifying vegetation suitable areas and the influence of each landscape factor considered in their identification. This validation process showed that vegetation suitable areas are significant in identifying areas with good photosynthetic activity. The effects analysis showed a positive influence of all landscape factors in determining suitability. This methodology, applied to central regions of Italy, shows that the vegetation suitable areas located inside protected areas are <em>greener</em> than those outside protected areas. This suggests that the protective measures established by the institution of the parks have proved to be effective, at least as far as the status of vegetation development is concerned.

scholarly journals LIGHT POLLUTION IN SLOVENIA IN 2019 WITH SPECIAL REGARD TO NATURA 2000 AREAS

2020 ◽  
Vol 42 ◽  
pp. 69-81
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Remote Sensing Data ◽  
Natura 2000 ◽  
The State ◽  
Light Pollution ◽  
Sensing Data ◽  
Special Regard ◽  
Ljubljansko Barje

Light pollution in Slovenia in 2019 with special regard to Natura 2000 areas The article shows the state of light pollution in Slovenia. Remote sensing data from the Suomi satellite were analysed. Light pollution is shown by radiance expressed in nW/(sr cm2 ). In Slovenia, there are large differences in state of light polution. The most polluted areas are located in the area of larger settlements and in areas with higher levels of infrastructure. The spread of light does not stop at the borders of protected areas, so we also analyzed the state of light pollution in Natura 2000 sites in Slovenia. It turns out that the most lightpolluted areas are those that lie around larger settlements or suburbanised regions (Ljubljansko Barje, Šmarna gora, Drava).

scholarly journals Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

2017 ◽  
Vol 21 (12) ◽  
pp. 6235-6251 ◽  
Author(s):  
Guiomar Ruiz-Pérez ◽  
Julian Koch ◽  
Salvatore Manfreda ◽  
Kelly Caylor ◽  
Félix Francés
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sub Saharan Africa ◽  
Automatic Calibration ◽  
Calibration And Validation ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

scholarly journals Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data

2018 ◽  
Vol 7 (7) ◽  
pp. 243 ◽  
Author(s):  
Wei Jiang ◽  
Guojin He ◽  
Wanchun Leng ◽  
Tengfei Long ◽  
Guizhou Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Remote Sensing Data ◽  
Light Pollution ◽  
Sensing Data ◽  
Nighttime Light

scholarly journals Deforestation pattern dynamics in protected areas of the Brazilian Legal Amazon using remote sensing data

2018 ◽  
Vol 100 ◽  
pp. 101-115 ◽  
Author(s):  
Ana I.R. Cabral ◽  
Carlos Saito ◽  
Henrique Pereira ◽  
Anne Elisabeth Laques
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Pattern Dynamics

scholarly journals Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade

2020 ◽  
Vol 12 (12) ◽  
pp. 5016
Author(s):  
Lijun Mao ◽  
Mingshi Li ◽  
Wenjuan Shen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Protected Areas ◽  
Driving Forces ◽  
Remote Sensing Data ◽  
Natural Disturbance ◽  
Vegetation Dynamic ◽  
Sensing Data ◽  
Sensing Applications

Terrestrial protected areas (PAs) play an essential role in maintaining biodiversity and ecological processes worldwide, and the monitoring of PAs is a useful tool in assessing the effectiveness of PA management. Advanced remote sensing technologies have been increasingly used for mapping and monitoring the dynamics of PAs. We review the advances in remote sensing-based approaches for monitoring terrestrial PAs in the last decade and identify four types of studies in this field: land use & land cover and vegetation community classification, vegetation structure quantification, natural disturbance monitoring, and land use & land cover and vegetation dynamic analysis. We systematically discuss the satellite data and methods used for monitoring PAs for the four research objectives. Moreover, we summarize the approaches used in the different types of studies. The following suggestions are provided for future studies: (1) development of remote sensing frameworks for local PA monitoring worldwide; (2) comprehensive utilization of multisource remote sensing data; (3) improving methods to investigate the details of PA dynamics; (4) discovering the driving forces and providing measures for PA management. Overall, the integration of remote sensing data and advanced processing methods can support PA management and decision-making procedures.

scholarly journals Calibration of a parsimonious distributed ecohydrological daily model in a data scarce basin using exclusively the spatio-temporal variation of NDVI

2016 ◽  
Author(s):  
Guiomar Ruiz-Pérez ◽  
Julian Koch ◽  
Salvatore Manfreda ◽  
Kelly Caylor ◽  
Félix Francés
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Vegetation Dynamics ◽  
Hydrological Cycle ◽  
Remote Sensing Data ◽  
Empirical Orthogonal Functions ◽  
Sub Saharan Africa ◽  
Automatic Calibration ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Ecohydrological models provide a tool to investigate the mutual relationships between vegetation and the hydrological cycle. Ecohydrological modelling studies in developing countries, such as sub-saharan Africa often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit its spatio-temporal information that could potentially be incorporated in ecohydrological model calibration and validation. The present study aims to implement a distributed ecohydrological daily model in a data scarce environment with the support of remote sensing data. An automatic calibration procedure, based on Empirical Orthogonal Functions techniques, is proposed and applied in the Upper Ewaso river basin in Kenya. The model is calibrated only using NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The obtained results demonstrate that: (1) satellite data of vegetation dynamics contains an extraordinary amount of information that can be used to implement ecohydrological models in scarce data dry regions; (2) the model calibrated only using satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet point; and (3) the proposed semi-automatic calibration methodology works satisfactorily and it allows to incorporate spatio-temporal data in the model parametrization.

scholarly journals Recognition of indicative landscape objects within protected areas

2020 ◽  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Practical Significance ◽  
Conservation Areas ◽  
Field Methods ◽  
Sensing Data ◽  
High Resolution Satellite Imagery ◽  
Visual Changes

Formulation of the problem. In this article the author describes monitoring of landscape objects within protected area. We created 'image of landscape' from remote sensing data. The developed methodology allows to obtain remotely information about visual changes, to analyze and predict the further development of landscapes of the facies level. It is difficult to investigate nature conservation areas at the facies level in areas with plant diversity. Field methods are time-consuming and labor-intensive, but changes can occur frequently. We offer a methodology for identifying indicative landscape objects by creating an image and its visualization using high-resolution satellite imagery decoding Sentinel-2 (resolution 10 m) and Planet Scope (resolution 3 m). This method with using satellite imagery of study makes it possible to gain access to the terrain that is accessible in hard-to-reach places, namely in swampy areas, in dense forest impassable territories and others. The purpose of the article. The main goal is creating methodic for recognition indicative objects of landscape within protected territories through the appearance of visual changes by the cameral method. Materials and methods. We have improved the method of processing satellite images to identify indicative objects of changes in landscapes at the facies level. We used the method of controlled classification to obtain "a picture" of the landscape in office conditions, carried out an analysis of comparison on the ground and identified objects of interest. Based on experiments we chosen supervised classification and methods for different resolution of remote sensing data. Results and scientific novelty. We have changed the traditional landscape study process and approach in our work. We created a landscape rendering model and then carried out work directly on the ground, comparing the characteristics. this allows you to explore the territory at a distance, in hard-to-reach places and in protected areas, which allows a person to analyze information at a distance, predict and take further measures to preserve landscapes and individual objects. Practical significance. Identification of indicative objects within protected areas allows monitoring changes in landscapes, analyzing and taking measures to preserve them. Systematization of the entire analysis during processing allows you to identify changes in time even in hard-to-reach regions and quickly receive information remotely. The analyzed data allow designing a successful combination of the normal functioning of nature and human activity.

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