Improved air quality leads to enhanced vegetation growth during the COVID-19 lockdown in India.

The direct effect of pandemic induced lockdown (LD) on environment is widely explored, but its secondary impacts remain largely unexplored. Therefore, we assess the response of surface greenness and photosynthetic activity to the LD-induced improvement of air quality in India. Our analysis reveals a significant improvement in air quality marked by reduced levels of aerosols (AOD, -19.27%) and Particulate Matter (PM 2.5, -23%) during LD (2020)from pre-LD (March-September months for the period 2017-2019). The vegetation exhibits a positive response, reflected by the increase in surface greenness [Enhanced Vegetation Index (EVI, +10.4%)] and photosynthetic activity [Solar Induced Fluorescence (SiF, +11%)], during LD from pre-LD that coincides with two major agricultural seasons of India; Zaid (March-May) and Kharif (June-September). In addition, the croplands show a higher response [two-fold in EVI (14.45%) and four-fold in SiF (17.7%)] than that of forests. The prolonged growing period (phenology) and high rate of photosynthesis (intensification) led to the enhanced greening during LD owing to the reduced atmospheric pollution. This study, therefore, provides new insights into the response of vegetation to the improved air quality, which would give ideas to counter the challenges of food security in the context of climate pollution, and combat global warming by more greening.

Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment

Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.

First description of the breeding biology of the North Philippine Hawk-Eagle (Nisaetus philippensis): a Journal of Ornithology

This study presents the first description of the breeding biology of the IUCN Endangered North Philippine Hawk-Eagle (Nisaetus philippensis). We describe a single pair's breeding phenology, nest characteristics, diet, chick development, and behavior through on-the-ground and remote observations from 1 February to 14 May 2020. Due to limited mobility during the COVID-19 pandemic, we improvised a video recording setup for remote monitoring and used machine learning to extract data from images. The nest was a low cup/fork type stick nest placed on a Malabulak tree (Bombax ceiba) in a heavily disturbed secondary forest. When it was first found, the incubation stage was underway and lasted for 1 month as the nestling emerged on 1 March 2020. Both adults provided parental care throughout the breeding period, with the male primarily providing food and the female attending to the nest, egg, and chick. They preyed on a wide range of vertebrates such as lizards, ground birds, bats, rodents, and domestic animals. With a single egg per clutch and a relatively long breeding cycle, the species has a slow reproductive output that may contribute to its current threatened status.

Telling the Wood from the Trees: Ranking a Tree Species List to Aid Urban Afforestation in the Amazon

The vast Amazonian biome still poses challenges for botanists seeking to know and recognize its plant diversity. Brazilian northern cities are expanding fast, without considering the regional biodiversity, and urban plantings of almost exclusively exotic species are taking place. It is paramount that the correct identity of such trees is ascertained before procurement of the seeds and young plants, as the use of popular names may lead to importation of plant material from elsewhere, with potential introduction of invasive species. The abundant local diversity also leads to the need to score the most suitable species within a given region. Following the preparation of authoritatively named floristic lists in Southeastern Pará state, we proceeded to score and rank the most suitable trees for urban planning using different characteristics such as size, ornamental value, ecologic role, resilience and known methods of propagation. From an initial 375 species list, 263 species were ranked according to their suitability for street and urban area plantings and visualized using a Venn diagram. A final list with the 49 of the highest-ranking species was further analysed regarding their pollination and phenology period and two types of dissimilarity analyses were provided to aid practitioners in matching and choosing groups of species. Different local vegetation types mean that similar floristic lists must be used to extract cohorts of suitable plants to increase the urban richness in the eight Brazilian states that are included in the Amazonian biome.

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data. [ FROM AUTHOR] Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)