Tropical Ecology最新文献

Associated with farming practices (between 300 and 2000 m elevations), human-ignited small, and patchy surface forest fires occur almost every year in Uttarakhand (between 28°43`- 31°27` N and 77°34`- 81°02`E; area 51,125 km²), a Himalayan state of India. Using fire incidence data of 19 years (2002-2020) generated by MODIS, we analysed the factors which drive temporal and spatial patterns of fire in the region. The fire incidence data were organized by 24 forest divisions, the unit of state forest management and administration. The standardized regression model showed that pre-monsoon temperature (March to May or mid-June), proportional area of the forest division under chir pine (Pinus roxburghii) forest (positive effect), and pre-monsoon and winter precipitation (negative effect) accounted for 56% of the variance in fire incidence density (FID). The pre-monsoon temperature (warmer) and precipitation (lower) were significantly different in 2009, 2012, 2016 and 2019, the years with high FID (average 54.9 fire/100 km²) than the rest of years with low FID (average 20.9 fire/100 km²). During the two decades of warming, high FID (> 30 incidence per year /100 km²) occurred after every three to four years, and fire peaks tended to increase with time. The study suggests that effective fire management can be attained by improving pre-monsoon precipitation forecasting and targeting forest compartments with a higher occurrence of chir pine and fire-vulnerable oaks. Furthermore, since fires are human-ignited, periodical analysis of changes in population distribution and communities' dependence on forests would need to be conducted.

Supplementary information: The online version contains supplementary material available at 10.1007/s42965-023-00306-9.

Background: Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration.

Objectives: The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2.

Methods: A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations.

Results: The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45-89%. During 32 years (1990-2022), about 1280.47 km² (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96).

Conclusions: This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover.

For the last several years, the air quality of India's capital Delhi and surrounding region (NCR) has been degrading to a very poor and severe category during the autumn season. In addition to the various sources of air pollutants within the NCR region, the stubble burning in Punjab and Haryana states contributes to the poor air quality in this region. The current study employs the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products and TROPOspheric Monitoring Instrument (TROPOMI) products on carbon monoxide (CO) and nitrogen dioxide (NO₂) concentrations for spatio-temporal assessment of stubble burning and associated emissions. The analysis performed in the Google Earth Engine (GEE) platform indicated a nearly threefold rise in crop residue burning in November than in October, with 92.58% and 7.42% reported from Punjab and the Haryana states in November, respectively. The study highlights the availability of near-real-time remote sensing observations and the utility of the GEE platform for rapid assessment of stubble burning and emissions thereof, having the potential for developing mitigation strategies.