Explore the vast range of titles available.

Forests provide crucial ecosystem services and are increasingly threatened by invasive plant species. The spread of these invasive species has affected biodiversity and has become a trending topic due to its impact on both endemic species and biodiversity. Therefore, it is imperative to implement conservation measures to protect native species such as mapping and monitoring invasive plant species in the forest realm. Mapping understory herb invasive plant species within forest categories is challenging, for example species such as Ageratum conyzoides and Cassia tora do not occur in distinct clusters, making them difficult to distinguish from the surrounding forest. In this paper, phenology plays a vital role for analysing the separability of both inter and intra-species discrimination to examine temporal curves for different vegetation indices that affect plant growth during the green and senescence periods. Machine learning algorithms, including regression tree-based algorithms, decision tree-based algorithms, and probabilistic algorithms, were used to determine the most effective algorithm for pixel-based classification. Support Vector Machine (SVM) classifier was the most effective method, with an overall accuracy of this classifier was calculated as 90.28% and a kappa of 0.88. The findings indicate that machine learning algorithms remain effective for pixel-based classification of understory invasive plant species from forest class. Thus, this study shows a technical method to distinguish invasive plant species from forest class which can help forest managers to locate invasion sites to eradicate them and conserve native biodiversity.

Forests are one of the most significant and major carbon sinks. Understanding the global carbon cycle requires assessing the quantity of carbon stored in forests. Remote sensing has been widely employed for estimating forest biomass from local to global. Several forest factors have been mapped or simulated using remote sensing data alone or in conjunction with field data, making forest mapping a broad field. The use of hyperspectral sensors has dominated the past ten years. The potential of the spaceborne hyperspectral sensor PRISMA is the main focus of this study. The goal of this study is to provide the most up-to-date information on hyperspectral remote sensing from space by focusing on estimating aboveground biomass (AGB) and the feasibility of PRISMA in the challenging phenological circumstances of a tropical dry deciduous forest. The objective of the study was to estimate AGB using PRISMA data and to check the phenological variations of AGB. Findings showed that the employed vegetation indices (c) and phenological conditions significantly impacted the predicted accuracy. Results conclude that the atmospherically resistant vegetation index outperforms the enhanced vegetation index (EVI), normalized difference vegetation index, and simple ratio index for PRISMA, with MAE = 5.42 t/ha, RMSE = 6.43 t/ha, and R 2  = 0.34. It is also predicted that adverse phenological circumstances were the cause of the poor correlation between field biomass and predicted biomass. To check the phenological variation, AGB was also assessed by Landsat 8 data using the same vegetation indices, in which EVI performed better than others with a 0.72 R 2 value. The study indicated that phenological variations have a significant impact on AGB estimation, and narrowband indices can be useful in such studies.

Excessive burning of fossil fuels is misbalancing the concentration of the gases and increasing greenhouse gases in the atmosphere. With the development and growing population, the demand for fossil fuels is increasing rapidly; and this is the leading factor of greenhouse gases emission. These greenhouse gases are creating new challenges for researchers such global warming and climate change. Therefore, it is a need to observe and monitor these gases on global scales. There are some satellites to monitor the atmospheric concentration of these gases. This chapter is exposing the importance and capacity of GOSAT satellite to observe and monitor the global distribution of carbon dioxide (CO 2 ). A global distribution of CO 2 was performed using kriging method from 2009 to 2020 for the months of December, January, February, and March.

In this study, four surface water quality datasets of upper Damodar river basin (DRB) covering three seasons; pre-monsoon, monsoon, post-monsoon and annual, for years 2007–2010 were generated by analyzing 280 grab water samples. Each dataset consist of water quality constituents of 35 monitoring stations and sample of each station was evaluated by 17 critical parameters (total 4760 observations). Furthermore, each dataset was treated using six water quality indices (WQIs): four developed simplified indices (WQIm, WQImin, WQIDO, and WQIpca) and two existing extended indices (WQIobj and WQIsub), to assess spatiotemporal variations and suitability for human use and aquatic life. Results revealed that developed indices show on an average similar spatiotemporal variations as compared to WQIobj at a lower analytical cost at most of sampling sites comes under good to medium categories of water quality. Geographical information system (GIS) technique was also used for generation of temporal pollution potential maps of DRB. Consequently, this study also presents the necessity and usefulness of developed indices over extended indices especially for the developing countries, because the cost of monitoring and expenses associated with the implementation is less compared to extended methods and generated maps may also facilitate the decision-making processes under various scenarios considering spatial and temporal variability in DRB.