Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth’s surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth’s surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring.

In the past years, various efforts have been made to extract critical information for land-use and land-cover (LULC) areas using hyperspectral imagery which was not possible with the help of multispectral imagery. Remote sensing plays an important role in providing information over large areas and in monitoring the various changes over LULC. Classification is one of the main methods which is used for the detection of changes over the earth's surface. The evaluation of different classifiers using hyperspectral imaging is essential be performed. Therefore, the main objectives of current research work are to analyze and implement the various supervised classifiers such as support vector machine (SVM), neural networks (NN), maximum likelihood classifier (MLC), and K-means as an unsupervised classifier. These classifiers have been implemented and evaluated using a hyperspectral dataset over a part of Haryana and Uttar Pradesh states, India. The results have shown that NN (89.20%) algorithm has achieved higher accuracy than other algorithms and is useful in the mapping of changes over LULC using hyperspectral imagery. This study is useful in many applications such as monitoring and mapping seasonal variations and natural resources.

Global navigation satellite systems (GNSSs) are widely utilized in various scientific domains such as agriculture, health monitoring, weather forecasting, and crop health detection. GNSS is a satellite network utilized in positioning and navigation including location-based services and precise orbit determination. The satellite navigation system offers various advantages such as real-time location and temporal synchronization. The Global Positioning System (GPS) is one of the well-known GNSSs, operated by the United States. Some of the other examples may include GPS; Galileo; Russia’s GLONASS, known as the Global Navigation Satellite System; and the BeiDou Navigation Satellite System (BDS). GNSS has found many applications in the field of precision agriculture, timing and synchronization in telecommunication, search and rescue operations, military and defense purposes, Earth observation, construction, and weather forecasting. However, GNSS has also been explored in the accurate and reliable positioning and timing information for climate change analysis and modeling. Therefore, it is essential to examine the potential of GNSS in climate monitoring and modeling. This chapter will present a review of GNSS types, GNSS remote sensing applications, and challenges faced by GNSS. Additionally, this chapter also explores the meta-analysis of the publications or research contributions made by different authors in the field of GNSSs. <!----> <!----> <!----> <!---->