Timely and accurate estimates of crop parameters are crucial for agriculture management. Unmanned aerial vehicles (UAVs) carrying sophisticated cameras are very pertinent for this work because they can obtain remote-sensing images with higher temporal, spatial, and ground resolution than satellites. In this study, we evaluated (i) the performance of crop parameters estimates using a near-surface spectroscopy (350~2500 nm, 3 nm at 700 nm, 8.5 nm at 1400 nm, 6.5 nm at 2100 nm), a UAV-mounted snapshot hyperspectral sensor (450~950 nm, 8 nm at 532 nm) and a high-definition digital camera (Visible, R, G, B); (ii) the crop surface models (CSMs), RGB-based vegetation indices (VIs), hyperspectral-based VIs, and methods combined therefrom to make multi-temporal estimates of crop parameters and to map the parameters. The estimated leaf area index (LAI) and above-ground biomass (AGB) are obtained by using linear and exponential equations, random forest (RF) regression, and partial least squares regression (PLSR) to combine the UAV based spectral VIs and crop heights (from the CSMs). The results show that: (i) spectral VIs correlate strongly with LAI and AGB over single growing stages when crop height correlates positively with AGB over multiple growth stages; (ii) the correlation between the VIs multiplying crop height and AGB is greater than that between a single VI and crop height; (iii) the AGB estimate from the UAV-mounted snapshot hyperspectral sensor and high-definition digital camera is similar to the results from the ground spectrometer when using the combined methods (i.e., using VIs multiplying crop height, RF and PLSR to combine VIs and crop heights); and (iv) the spectral performance of the sensors is crucial in LAI estimates (the wheat LAI cannot be accurately estimated over multiple growing stages when using only crop height). The LAI estimates ranked from best to worst are ground spectrometer, UAV snapshot hyperspectral sensor, and UAV high-definition digital camera.

Coastal is the coupling of socio-economic and fragile ecosystems area existing development and protection problems, with lots of reserve land resources (i.e., bottomland and tidal flats). Analyzing and predicting the carbon storage changes caused by land use/land cover (LULC) on the Jiangsu coast were critical for revealing the potential problems of land surface changes and sustainable development. Then, we utilized the single dynamic degree and transfer in/out contribution, exploring the characteristics of LULC change in the study area from 1980 to 2018. Using the InVEST model, PLUS-LEAS model, and PLUS-Markov chain module, we assessed the spatiotemporal of the study area at the county level to reveal the LULC change strategy and driving factor contribution, as well as the composition of LULC and carbon storage in 2036. The results show that the LULC structure in the study area significantly changed from 1980 to 2018, in which the tidal flat and high coverage grassland decreased by 552.84 km² and 383.71 km² while the reservoir ponds and urban residential land increased by 1210.69 km² and 101.70 km². The major driving factor of LULC change has shifted from a single-factor to multi-factor coupling, and the influence contribution of human activity increased by 6.73%, especially the population. The carbon storage of study areas showed a significant decrease trend during 1980–2010, followed by a slight increase during 2010–2018. High-density carbon storage was mainly distributed in Lianyungang and Nantong and presented a decreasing trend along the coastline extending inland. The dry land and reservoir ponds are the main composition of LULC types in 2036, and the carbon storage increased to 2.39 × 10⁸ t. In addition, more than decades of LULC change will cover part or all of the land use change process and trends, especially high-covered grasslands, so we suggest a 10-year LULC change to analyze coastal areas with lots of tidal flats and bottomlands. Therefore, this study can provide reference and theoretical guidance for ecologically sustainable development and future LULC evolution in coastal cities.

Forests are complex systems in which subtle variations in terrain can reveal much about plant community structure and interspecific interactions. Despite a wealth of studies focusing on broad-scale environmental gradients, the role of fine-scale topographic nuances often remains underappreciated, particularly in subtropical settings. In our study, we explore how minute differences in microtopography—encompassing local elevation, slope, aspect, terrain position index (TPI), terrain ruggedness index (TRI), and flow direction—affect neighborhood-scale interactions among plants. We established an 11.56-hectare dynamic plot in a subtropical forest at the northern margin of China’s subtropical zone, where both microtopographic factors and neighborhood indices (density, competition, diversity) were systematically measured using 5 m × 5 m quadrats. Parameter estimation and mixed-effects models were employed to examine how microtopography influences plant spatial patterns, growth, and competitive dynamics across various life stages. Our findings demonstrate that aspect and TPI act as key drivers, redistributing light and moisture to shape conspecific clustering, heterospecific competition, and tree growth. Remarkably, sun-facing slopes promoted sapling aggregation yet intensified competitive interactions, while shaded slopes maintained stable moisture conditions that benefited mature tree survival. Moreover, in contrast to broader-scale observations, fine-scale TRI was associated with reduced species richness, highlighting scale-dependent heterogeneity effects. The intensification of plant responses with life stage indicates shifting resource demands, where light is critical during early growth, and water becomes increasingly important for later survival. This study thus advances our multiscale understanding of forest dynamics and underscores the need to integrate fine-scale abiotic and biotic interactions into conservation strategies under global change conditions.

Autumn phenology plays a crucial role in shaping the capacity for carbon sequestration. However, understories, a vital yet often neglected ecosystem component, have complicated autumn phenology prediction. We address the challenge of monitoring understory phenological dynamics by using a UVL4 trail camera and selecting appropriate deriving processes and vegetation indices (VIs). We found the understory photoperiod was on average 1.88 h shorter than the canopy’s, while the understory temperature was 2.11 °C higher than the canopy’s open-air temperature. The maximum temperature inside the understories was on average 1.37 °C lower than in open-air conditions. Specifically, the 60% quantile of the daily VI in July and the 15% quantile in November effectively captured the prolonged minimum and the minimum in the VI time series when applying logistic modeling. The excess green vegetation index (ExG) outperformed other VIs in estimating understory greenness change. The cold degree days model (CDD) and low-temperature and photoperiod multiplicative model (TPM) revealed that senescence progressed from the upper crown downwards, causing over 13 days of lag in the understory. These findings offer a new perspective on quantifying autumn phenology in subtropical forests and provide insights into asynchronous changes in vertical microclimatic gradients in Earth system and vegetation models.