An Intelligent Approach to Elevation Profiling for LADAKH using QGIS techniques

With the trending technological advancement, geographic information systems are finding their supremacy in many fields of research and technology, ranging from geographical inputs to population trends to medical advancements in full spheres of work in today’s progressing world. The emergence of Quantum GIS has now facilitated areas like change monitoring, forecasting. The paper aims to prove the elevation system forecast for the Ladakh region, which has lesser mobility in the present times; the paper’s findings focus on opening a new gateway for the engineered constructions in the region for improved connectivity that is confined to the summer months. Thus, ESRI’s GIS software helps analyse the terrain difficulties in the stipulated area via topics like raster feed, georeferencing, and mesh layer creation.

Technical Report on Assembling Indicators to Monitor Climate-Driven Change in Northeastern Forests

Forest ecosystems are experiencing the impacts of climate change in many forms, however, comprehensive monitoring efforts are not always available to identify changing baselines. In order to improve our understanding of the impacts of climate change on ecosystem processes, the FEMC developed the Forest Impacts of Climate Change: Monitoring Indicators tool (Version 1.0). The Forest Impacts of Climate Change: Monitoring Indicators tool was developed for use by researchers and professionals to be able to easily access protocols used to monitor high priority indicators of the impacts of climate change in New England and New York. The monitoring protocols provide information for landowners and managers to implement their own monitoring programs that will be comparable to other studies being conducted across the region. By centralizing information about this network of monitoring sites, more data will become available to the community to help discern how forest ecosystems are changing. This report describes the methods and implementation used to build this tool. To develop the Forest Impacts of Climate Change: Monitoring Indicators tool, FEMC formed a committee of partners to select indicators and provide guidance about the literature review and eventual tool. The committee identified four ecological categories as important for monitoring climate change in the Northeast: Wildlife, Forest Systems, Trees, and Aquatic Systems. FEMC identified who is currently conducting monitoring efforts, what monitoring protocols are available for replication, gaps in monitoring data, and how we can make data and monitoring information easily available so that land managers can have the most up-to -date information possible. The developed tool compiles over 350 studies across 24 different indicators of the impacts of climate change. Through a filterable webtool users can find these studies, as well as 168 replicable protocols to direct implementation. The tool helps to identify gaps in monitoring efforts and provides a platform for users to contribute to regionally cohesive datasets. Monitoring of indicators across systems is critical for tracking and understanding climate change impacts. The Forest Impacts of Climate Change: Monitoring Indicators tool, developed for use by researchers, professionals, and land managers across the region, lets users find methods and protocols for monitoring climate change impacts and see where these monitoring efforts are already being conducted in our region. In addition, you can quickly visualize where there are gaps in our monitoring. As contributors in the Cooperative region share more information about their own monitoring efforts, this will become available to the community through this tool, increasing our ability to track and identify change in our forested ecosystems.

Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images

The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring.

Monitoring and Mathematical Model Analysis of Dynamic Changes in Land Resources Based on SAR Sensor Image

The monitoring and analysis of dynamic changes in land resources can detect the changes of land aimed at a single-band or multiband remote sensing image of multiple phases in a given region or target with image processing methods and can also extract the change information and realize remote sensing monitoring through the comprehensive analysis of multiphase remote sensing images. Synthetic aperture radar (SAR) image change monitoring technology, with the advantages of high resolution, high precision, real-time service, and rapid imaging, can achieve qualitative or quantitative analysis of targets and is gradually widely used in quarterly monitoring, emergency monitoring, postbatch verification, law-enforcement inspection and land inspection, and other remote sensing data acquisitions and analyses. Therefore, on the basis of summarizing the research results of previous research works, this paper expounded the current situation and significance of the researches on the monitoring and analysis of dynamic changes in land resources; elaborated the development background, current situation, and future challenges of SAR sensor data; introduced the methods and principles of band setting, polarization mode, geometric correction, and image filtering; proposed the status target identification of land resources; explored the dynamic information discovery of land resources; conducted the dynamic change monitoring of land resources based on SAR sensor data; analyzed the basis and characteristics of SAR sensor data; performed the generalization and optimization of land resource information; demonstrated the dynamic change analysis of land resources based on SAR sensor data; compared the acceptance ability and accuracy of SAR sensor data; and discussed the discovery and extraction of dynamic information of land resources. The results show that the SAR sensor data can monitor the characteristics of scattering points in land resource observation scenes and can obtain the change information of ground object by distance component and band component, so that the SAR system can make two-dimensional imaging of land resources directly in front of the receiving platform. Thus, the SAR data obtained by multisystem parameters shows great application potential in land resource monitoring, which provides the possibility of decoupling to remove land resources and surface roughness and thus provides possible solutions for land resource analysis in complex environment. The results of this paper provide a reference for the follow-up studies on the monitoring and analysis of dynamic changes in land resources based on SAR sensor data.

The International Phenological Garden network (1959 to 2021): its 131 gardens, cloned study species, data archiving, and future

Collaborative networks that involve the compilation of observations from diverse sources can provide important data, but are difficult to maintain over long periods. The International Phenological Garden (IPG) network, begun in 1959 and still functioning 60 years later, has been no exception. Here we document its history, its monitored 23 species (initially all propagated by cloning), and the locations and years of data contribution of its 131 gardens, of which 63 from 19 countries contributed data in 2021. The decision to use clones, rather than multiple, locally adapted individuals, was based on the idea that this would “control” for genetic effects, and it affects the applicability of the data and duration of the network. We also describe the overlap among the IPG network, the Pan-European Phenology network (PEP725), and the phenological data offered by the German Weather Service. Sustainable data storage and accessibility, as well as the continued monitoring of all 23 species/clones, are under discussion at the moment, as is the fate of other phenological networks, despite a politically mandatory plant-based climate-change monitoring.