scholarly journals A study of the relationship between wetland vegetation communities and water regimes using a combined remote sensing and hydraulic modeling approach

2016 ◽  
Vol 47 (S1) ◽  
pp. 278-292 ◽  
Author(s):  
Tan Zhiqiang ◽  
Zhang Qi ◽  
Li Mengfan ◽  
Li Yunliang ◽  
Xu Xiuli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Poyang Lake ◽  
Spatial Scales ◽  
Hydraulic Modeling ◽  
Wetland Vegetation ◽  
Vegetation Distribution ◽  
Vegetation Communities ◽  
Water Regimes ◽  
Hydrological Conditions ◽  
Hydrologic Condition

Hydrologic condition is a major driving force for wetland ecosystems. The influence of water regimes on vegetation distribution is of growing interest as wetlands are increasingly disturbed by climate change and intensive human activities. However, at large spatial scales, the linkage between water regimes and vegetation distribution remains poorly understood. In this study, vegetation communities in Poyang Lake wetland were classified from remote sensing imagery. Water regimes characterized by inundation duration (IDU), inundation depth (IDE), and inundation frequency were simulated using physics-based hydraulic models and were then linked with vegetation communities by a Gaussian regression model. The results showed that the Carex community was found to favor more hydrologic environments with longer IDU and deeper IDE in comparison to the Phragmites community. In addition, we found that the Carex community could survive in a relatively wider variety of hydrological conditions than the Phragmites community. For the typical sub-wetlands of the Poyang Lake National Nature Reserve (PLNNR), only the influence of IDU on the distribution of vegetation communities was significant. Outcomes of this research extend our knowledge of the dependence of wetland vegetation on hydrological conditions at larger spatial scales. The results provide practical information for ecosystem management.

scholarly journals Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil

2011 ◽  
Vol 8 (3) ◽  
pp. 667-686 ◽  
Author(s):  
J. Arieira ◽  
D. Karssenberg ◽  
S. M. de Jong ◽  
E. A. Addink ◽  
E. G. Couto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Cross Validation ◽  
Remotely Sensed ◽  
Wetland Vegetation ◽  
Universal Kriging ◽  
Field Sampling ◽  
Remotely Sensed Data ◽  
Vegetation Communities

Abstract. Development of efficient methodologies for mapping wetland vegetation is of key importance to wetland conservation. Here we propose the integration of a number of statistical techniques, in particular cluster analysis, universal kriging and error propagation modelling, to integrate observations from remote sensing and field sampling for mapping vegetation communities and estimating uncertainty. The approach results in seven vegetation communities with a known floral composition that can be mapped over large areas using remotely sensed data. The relationship between remotely sensed data and vegetation patterns, captured in four factorial axes, were described using multiple linear regression models. There were then used in a universal kriging procedure to reduce the mapping uncertainty. Cross-validation procedures and Monte Carlo simulations were used to quantify the uncertainty in the resulting map. Cross-validation showed that accuracy in classification varies according with the community type, as a result of sampling density and configuration. A map of uncertainty derived from Monte Carlo simulations revealed significant spatial variation in classification, but this had little impact on the proportion and arrangement of the communities observed. These results suggested that mapping improvement could be achieved by increasing the number of field observations of those communities with a scattered and small patch size distribution; or by including a larger number of digital images as explanatory variables in the model. Comparison of the resulting plant community map with a flood duration map, revealed that flooding duration is an important driver of vegetation zonation. This mapping approach is able to integrate field point data and high-resolution remote-sensing images, providing a new basis to map wetland vegetation and allow its future application in habitat management, conservation assessment and long-term ecological monitoring in wetland landscapes.

scholarly journals Integrating field sampling, spatial statistics and remote sensing to map wetland vegetation in the Pantanal, Brazil

2010 ◽  
Vol 7 (5) ◽  
pp. 6889-6934 ◽  
Author(s):  
J. Arieira ◽  
D. Karssenberg ◽  
S. M. de Jong ◽  
E. A. Addink ◽  
E. G. Couto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Plant Community ◽  
Habitat Management ◽  
Cross Validation ◽  
Wetland Vegetation ◽  
Universal Kriging ◽  
Future Application ◽  
Vegetation Communities

Abstract. To improve the protection of wetlands, it is imperative to have a thorough understanding of their structuring elements and of the identification of efficient methods to describe and monitor them. This article uses sophisticated statistical classification, interpolation and error propagation techniques, in order to describe vegetation spatial patterns, map plant community distribution and evaluate the capability of statistical approaches to produce high-quality vegetation maps. The approach results in seven vegetation communities with a known floral composition that can be mapped over large areas using remotely sensed data. The relations between remotely sensing data and vegetation patterns, captured in four factorial axes, were formalized mathematically in multiple linear regression models and used in a universal kriging procedure to reduce the uncertainty in mapped communities. Universal kriging has shown to be a valuable interpolation technique because parts of vegetation variability not explained by the images could be modeled as spatially correlated residuals, increasing prediction accuracy. Differences in spatial dependence of the vegetation gradients evidenced the multi-scale nature of vegetation communities. Cross validation procedures and Monte Carlo simulations were used to quantify the uncertainty in the resulting map. Cross-validation showed that accuracy in classification varies according with the community type, as a result of sampling density and configuration. A map of uncertainty resulted from Monte Carlo simulations displayed the spatial variation in classification accuracy, showing that the quality of classification varies spatially, even though the proportion and arrangement of communities observed in the original map is preserved to a great extent. These results suggested that mapping improvement could be achieved by increasing the number of field observations of those communities with a scattered and small patch size distribution; or by including new digital images as explanatory variables in the model. By comparing the resulting plant community map with a flood duration map, we verified that flooding duration is an important driver of vegetation zonation. We discuss our study in the context of developing a mapping approach that is able to integrate field point data and high-resolution remote sensing images, providing new basis to map wetland vegetation and allowing its future application in habitat management, conservation assessment and long-term ecological monitoring in wetland landscapes.

The importance of water regimes operating at small spatial scales for the diversity and structure of wetland vegetation

2010 ◽  
Vol 55 (3) ◽  
pp. 701-715 ◽  
Author(s):  
ELISA J. RAULINGS ◽  
KAY MORRIS ◽  
MICHAEL C. ROACHE ◽  
PAUL I. BOON
Keyword(s):  
Spatial Scales ◽  
Wetland Vegetation ◽  
Water Regimes

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

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.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals Imaging Spectroscopy for Conservation Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 292
Author(s):  
Megan Seeley ◽  
Gregory P. Asner
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Case Studies ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Decision Makers ◽  
High Spectral Resolution ◽  
Conservation Areas ◽  
Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

scholarly journals Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

2014 ◽  
Vol 369 (1643) ◽  
pp. 20130194 ◽  
Author(s):  
Michael D. Madritch ◽  
Clayton C. Kingdon ◽  
Aditya Singh ◽  
Karen E. Mock ◽  
Richard L. Lindroth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Populus Tremuloides ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Spectral Signature ◽  
Spectral Variation ◽  
Fine Scale ◽  
Below Ground ◽  
Landscape Scales ◽  
Spectrometer Data

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

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