Agroforestry systems (AFS) and practices followed in India are highly diverse due to varied climatic conditions ranging from temperate to humid tropics. The estimated area under AFS in India is 13.75 million ha with the highest concentration being in the states of Uttar Pradesh (1.86 million ha), followed by Maharashtra (1.61 million ha), Rajasthan (1.55 million ha) and Andhra Pradesh (1.17 million ha). There are many forms of agroforestry practice in India ranging from intensified simple systems of monoculture, such as block plantations and boundary planting, to far more diverse and complex systems, such as home gardens. As a result, the biomass production and carbon sequestration potential of AFS are highly variable across different agro-climatic zones of India. Studies pertaining to the assessment of biomass and carbon storage in different agroforestry systems in the Indian sub-continent are scanty and most of these studies have reported region and system specific carbon stocks. However, while biomass and carbon stock data from different AFS at national scale has been scanty hitherto, such information is essential for national accounting, reporting of C sinks and sources, as well as for realizing the benefits of carbon credit to farmers engaged in tree-based production activities. Therefore, the objective of this study was to collate and synthesize the existing information on biomass carbon and SOC stocks associated with agroforestry practices across agro-climatic zones of India. The results revealed considerable variation in biomass and carbon stocks among AFS, as well as between different agro-climatic zones. Higher total biomass (>200 Mg ha⁻¹) was observed in the humid tropics of India which are prevalent in southern and northeastern regions, while lower total biomass (<50 Mg ha⁻¹) was reported from Indo-Gangetic, western and central India. Total biomass carbon varied in the range of 1.84 to 131 Mg ha⁻¹ in the agrihorticulture systems of western and central India and the coffee agroforests of southern peninsular India. Similarly, soil organic carbon (SOC) ranged between 12.26–170.43 Mg ha⁻¹, with the highest SOC in the coffee agroforests of southern India and the lowest in the agrisilviculture systems of western India. The AFS which recorded relatively higher SOC included plantation crop-based practices of southern, eastern and northeastern India, followed by the agrihorticulture and agrisilviculture systems of the northern Himalayas. The meta-analysis indicated that the growth and nature of different agroforestry tree species is the key factor affecting the carbon storage capacity of an agroforestry system. The baseline data obtained across various regions could be useful for devising policies on carbon trading or financing for agroforestry.

Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm surges and reducing erosion. However, their resilience is being increasingly compromised due to climate change through sea level rises and the greater intensity of storms. This article examines the role of remote sensing tools in studying the impacts of hurricanes on mangrove forests in the coastal United States. Our results show that various remote sensing tools including satellite imagery, Light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been used to detect mangrove damage, monitor their recovery and analyze their 3D structural changes. Landsat 8 OLI (14%) has been particularly useful in long-term assessments, followed by Landsat 5 TM (9%) and NASA G-LiHT LiDAR (8%). Random forest (24%) and linear regression (24%) models were the most common modeling techniques, with the former being the most frequently used method for classifying satellite images. Some studies have shown significant mangrove canopy loss after major hurricanes, and damage was seen to vary spatially based on factors such as proximity to oceans, elevation and canopy structure, with taller mangroves typically experiencing greater damage. Recovery rates after hurricane-induced damage also vary, as some areas were seen to show rapid regrowth within months while others remained impacted after many years. The current challenges include capturing fine-scale changes owing to the dearth of remote sensing data with high temporal and spatial resolution. This review provides insights into the current remote sensing applications used in hurricane-prone mangrove habitats and is intended to guide future research directions, inform coastal management strategies and support conservation efforts.

Mangrove forests are vulnerable coastal ecosystems that provide multiple ecosystem services and act as blue carbon sinks. Mangroves in the Gulf Cooperation Council (GCC) countries of the Arabian peninsula (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE) have faced pressure from numerous anthropogenic factors including population growth, dredging and reclamation in lagoonal habitats, pollution and rapid urban development. Given the inconsistent outcomes of past mangrove ARR (Afforestation-Reforestation-Revegetation) efforts, our research aimed to identify high-potential ARR sites in the GCC using remote sensing. We identified eight factors related to mangrove ARR outcomes through correlation analyses: elevation, soil pH, median precipitation, median and minimum land surface temperature (LST), soil salinity, soil texture and distance from urban areas. To predict mangrove suitability, we compared the Random Forest (RF), XGBoost (XGB), Support Vector Machines (SVM) and Naive Bayes (NB) classification algorithms. The RF model performed best with an F1-score of 0.96, ROC-AUC of 0.99 and PR-AUC of 0.75. Variable importance analysis revealed that median LST, soil texture and median precipitation were the most influential variables. Favorable conditions for mangrove establishment included median temperatures of 32-37 °C, minimum temperatures around 27 °C, clayey soils, and monthly rainfall above 10 mm. Other suitable characteristics included lower elevation, greater distance from urban areas, slightly acidic to neutral pH, and moderate-to-high soil salinity. Our findings show that there is a large opportunity for mangrove afforestation in the GCC and also proposes a framework to identify optimal sites for mangrove growth, which can improve ARR success and support biodiversity and blue carbon goals in the Arabian peninsula.