Plants, people and long‐term ecological monitoring in the tropics

Pennington, R. Toby; Baker, Tim R.

doi:10.1002/ppp3.10196

Cited by 3 publications

(2 citation statements)

References 40 publications

(47 reference statements)

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“…ere are many ground data sources and a large amount of data collected by remote sensing technology. For example, the sensors of different monitoring equipment may obtain data with different resolutions in the same area, and the same equipment may also obtain data with different spatial and spectral resolutions in the same area [5]. It is known from the existing research that using the data collected by different devices and sensors to study the changes of the ecological environment is no longer a static mode, but a dynamic realtime monitoring mode, which will lead to the existing system unable to meet the effective integration of multi-source data.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Wang

2022

Journal of Robotics

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With the continuous improvement of people’s demand for ecological environment quality, the research on ecological environment monitoring, analysis and evaluation had been paid more and more attention by relevant departments and personnel. Because the images collected by remote sensing technology were many and multi-source, the features extracted from remote sensing images using traditional methods had been difficult to meet the needs of related industry applications. Therefore, this paper made use of the advantages of PIE remote sensing image processing software in data analysis and processing, and put forward the research on ecological environment monitoring, analysis and evaluation methods. Firstly, on the basis of summarizing the concepts and related problems of ecological environment, this paper analyzed the processing methods of remote sensing data sources of ecological environment, and explained the evaluation standards and common methods of ecological environment. Secondly, the composition of PIE remote sensing image processing technology system and its application advantages were described, the common indicators and analysis methods of ecological environment monitoring were given, and the index system and model of ecological environment comprehensive evaluation were established. Finally, through the analysis of experimental cases, the results showed that the ecological environment monitoring analysis and evaluation method proposed in this paper was feasible. Compared with the traditional methods, the method proposed in this paper could objectively evaluate the ecological environment. This paper can not only provide support for the analysis and processing of remote sensing image data, but also provide an important reference for the application of remote sensing technology in the field of ecological environment.

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Section: Introductionmentioning

confidence: 99%

Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Wang

2022

Journal of Robotics

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“…Due to the complexities of factors that can affect carbon stocks and sinks in (sub)tropical undisturbed and disturbed forests, their quantification is riddled with uncertainties Le Quéré et al, 2016). Therefore, attempting to do so requires building on several years of knowledge in tropical forest ecology, collaborating with research networks that estimate and study carbon stocks and sinks at different locations whilst employing similar methodologies (ForestPlots.net et al, 2020;Pennington and Baker, 2021) To obtain forest carbon stocks and sink estimates for a given site, tree-by-tree biomass is estimated in ground forest plots. According to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2006), forest biomass is assessed by measuring 5 carbon pools: aboveground live biomass (AGB), belowground biomass (BGB), dead wood, litter and soil biomass.…”

Section: From Trees and Biomass To Hectares And Carbonmentioning

confidence: 99%

Aboveground carbon stocks and sinks in recovering tropical forests

Suárez¹

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Chapter 2 Aboveground forest biomass varies across continents, ecological zones and successional stages: refinement of IPCC default values for tropical and subtropical forests 13Chapter 3 Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data 39Chapter 4 Variation in aboveground biomass in forests and woodlands in Tanzania along gradients in environmental conditions and human use 69Chapter 5 Forest disturbance and recovery in Peruvian Amazonia 87Chapter 6 Synthesis 109References 127Acknowledgements 159About the author 163 PE&RC Training and Education Statement 167For the UNFCCC parties it is necessary to quantify and understand current (sub)tropical forest carbon sources, stocks and sinks, as well as identify ways in which stocks and sinks can be enhanced. For these purposes, the Intergovernmental Panel on Climate Change (IPCC) supports the UNFCCC by providing monitoring guidelines as well as up-to-date scientific information on GHG emissions and removals from land use, land use changeRecently, the extent of undisturbed forests has been decreasing through deforestation and degradation. The latter process has led to the increase of disturbed forests (Lewis et al., 2015;Potapov et al., 2017). It is estimated that 30% of existing tropical forests and 40% of existing subtropical forests considered as degraded (Mercer, 2015). Due to the extent, importance and vulnerability of disturbed forests, disturbed forests need to be integrated into GHG monitoring frameworks (Bustamante et al., 2016).Plot-level AGB (in Mg ha −1 ) is calculated as the sum of tree-level AGB estimates (in Mg) per plot size (in ha). The size of plots can vary greatly: in this thesis, forest plot sizes range from 0.001 to 42 ha. To represent large areas of forests, we expect better plot-level estimates in larger plots (Hernández-Stefanoni et al., 2018;Mauya et al., 2015). However, Thesis overviewThis PhD thesis is divided into 6 chapters. Chapter 1, which you are now reading, provides a brief introduction into the current state of knowledge regarding aboveground forest carbon stocks and sinks, current research gaps and possibilities to address them. The following four chapters seek to answer both research objectives by leveraging AGB forest plot data from different sources with remote sensing products and analysis, considering varying environmental and human use gradients and providing AGB and ∆AGB estimates at different scales. Figure 1.2 provides a conceptual overview of how Chapters 2 through 5 relate to R.O.1 and R.O.2. Chapter 2 provides a refinement of the IPCC 2006 default AGB values for natural forests in (sub)tropical forest ecozones. This is done by incorporating AGB data from forest plots, and deriving separate estimates for young secondary forests, older secondary forests and for old-growth (i.e. undisturbed) forest. For areas where plot data is not available, remote-sensing datasets are used to estimate default values. Furthermore, areas with limited forest ...

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Long-Term Ecological Monitoring in India: A Synthesis

Tiruvaimozhi,

Borah,

Kala

et al. 2024

Preprint

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Long-term ecological monitoring (LTEM) is crucial for understanding ecological processes and responses to environmental change, informing management of natural resources, and biodiversity conservation. Systematic LTEM efforts began in India in the mid-1900s, but there is a lack of comprehensive synthesis of LTEM efforts in the country. Here, we use a wide-ranging questionnaire survey of ecologists coupled with a survey of published literature on LTEM efforts in India to synthesise their thematic and geographical spread, and types of data being collected, and identify key challenges to the establishment and maintenance of LTEM projects in the country. Studies monitor 77 unique subjects across 272 LTEM efforts in India. LTEM efforts are more often located in the Western Ghats and Eastern Himalaya, focused on forest vegetation, and monitoring factors such as abundance, distribution, species richness, and biomass. Regions such as North-Eastern, Eastern, Central, and North-Western India, ecosystems such as grasslands, deserts and wetlands, organisms such as macrofungi, amphibians, and reptiles (other than turtles) are underrepresented. Short turnover times of funding and permits were most frequently reported as hurdles in sustaining LTEM efforts. Data from LTEM efforts have been largely used to produce academic outputs such as journal articles, but have also found use for on-the-ground conservation efforts. Overall, this synthesis can help draw attention to the need for systematic long-term ecological monitoring, help efficient utilisation of existing long-term ecological data, identify regions, species and ecosystem components that are underrepresented in Indian LTEM efforts, foster collaborations, and serve as a starting point to address challenges in sustaining LTEM efforts in India.

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Plants, people and long‐term ecological monitoring in the tropics

Cited by 3 publications

References 40 publications

Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Aboveground carbon stocks and sinks in recovering tropical forests

Long-Term Ecological Monitoring in India: A Synthesis

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