A review on crocodilian nesting habitats and their characterisation via remote sensing

2019 ◽  
Vol 40 (4) ◽  
pp. 403-423 ◽  
Author(s):  
Gabriela Banon ◽  
Eduardo Arraut ◽  
Francisco Villamarín ◽  
Boris Marioni ◽  
Gabriel Moulatlet ◽  
...  

Abstract Crocodilians usually remain inside or near their nests during most vulnerable life stages (as eggs, neonates and reproductive females). Thus, protection of nesting sites is one of the most appropriate conservation actions for these species. Nesting sites are often found across areas with difficult access, making remote sensing a valuable tool used to derive environmental variables for characterisation of nesting habitats. In this study, we (i) review crocodilian nesting habitats worldwide to identify key variables for nesting site distribution: proximity to open-water, open-water stability, vegetation, light, precipitation, salinity, soil properties, temperature, topography, and flooding status, (ii) present a summary of the relative importance of these variables for each crocodilian species, (iii) identify knowledge gaps in the use of remote sensing methods currently used to map potential crocodilian nesting sites, and (iv) provide insight into how these remotely sensed variables can be derived to promote research on crocodilian ecology and conservation. We show that few studies have used remote sensing and that the range of images and methods used comprises a tiny fraction of what is available at little to no cost. Finally, we discuss how the combined use of remote sensing methods – optical, radar, and laser – may help overcome difficulties routinely faced in nest mapping (e.g., cloud cover, flooding beneath the forest canopy, or complicated relief) in a relevant way to crocodilians and to other semiaquatic vertebrates in different environments.

2017 ◽  
Vol 63 (No. 3) ◽  
pp. 107-116 ◽  
Author(s):  
Abdollahnejad Azadeh ◽  
Panagiotidis Dimitrios ◽  
Surový Peter

Crown canopy is a significant regulator of forest, affecting microclimate, soil conditions and having an undeniable role in a forest ecosystem. Among the different materials and approaches that have been used for the estimation of crown canopy, satellite based methods are among the most successful methods regarding cost-saving efforts and different kinds of options for measuring the crown canopy. Different types of satellite sensors can result in different outputs due to their various spectral and spatial resolution, even when using the same methodologies. The aim of this review is to assess different remote sensing methods for forest crown canopy density assessment.


2021 ◽  
Author(s):  
E.V. Dmitriev ◽  
T.V. Kondranin ◽  
P.G. Melnik ◽  
S.A. Donskoy

Aerospace images with a spatial resolution of less than 1 m are actively used by regional services to obtain and update information about various environmental objects. Considerable efforts are being devoted to the development of remote sensing methods for forest areas. The structure of the forest canopy depends on various parameters, most of which are determined by ground-based methods during forest management works. Remote sensing methods for assessing the structural parameters of forest stands are based on texture analysis of panchromatic and multispectral images. A statistical approach is often used to extract texture features. The basis of this approach is the description of the distributions characterizing the mutual arrangement of image pixels in grayscale. This paper compares the effectiveness of matrix based statistical methods for extracting textural features for solving the problem of classifying various natural and manmade objects, as well as structures of the forest canopy. We consider statistics of various orders based on estimates of the distributions of gray levels, as well as the mutual occurrence, frequency, difference and structuring of gray levels. The results of assessing the informativeness of statistical textural characteristics in determining various structures of the forest canopy are presented. Dependences of the classification results on the choice of distribution parameters are determined. For the quantitative validation of the results obtained, data from ground surveys and expert visual classification of very high resolution WorldView-2 images of the territories of Savvatyevkoe and Bronnitskoe forestries are used.


2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.


2000 ◽  
pp. 16-25
Author(s):  
E. I. Rachkovskaya ◽  
S. S. Temirbekov ◽  
R. E. Sadvokasov

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.


Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.


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