Baocheng Wei

The selection of data smoothing methods is one of the key steps in extracting land surface phenology parameters from time-series remote sensing data. However, existing studies often use default parameters for denoising the time-series data, neglecting the sensitivity of phenology extraction to different combinations of smoothing parameters. Therefore, this study systematically evaluated three parametric smoothing methods—Savitzky–Golay (SG), Whittaker Smoother (WS), and Harmonic Analysis of Time-Series (HANTS)—and two non-parametric methods—Asymmetric Gaussian (AG) and Double-Logistic (DL)—on the accuracy of Start of Season (SOS) and End of Season (EOS) extraction at eight ground phenology observation sites in Inner Mongolia, based on time-series MOD13Q1- Normalized Difference Vegetation Index data and using the derivative method as the background for phenology parameter extraction at the site scale. The results showed that (1) DL and HANTS yielded similar accuracy for phenology extraction in desert steppe, while parametric smoothing methods outperformed non-parametric methods in phenology simulation in typical and meadow steppe regions. (2) We proposed the optimal phenology parameter combination for different steppe types in Inner Mongolia. For desert steppe, DL or HANTS was recommended. For SOS extraction in typical steppe ecosystems, the WS parameter combination was used. For EOS and phenology in meadow steppe, the HANTS parameter combination yielded better simulation results. (3) In desert and meadow steppes, the window radius in SG contributed more to phenology accuracy than polynomial order. The opposite was true for typical steppe. In WS, the contribution of the differential order to SOS and EOS extraction in desert and typical steppes was higher than that of the smoothing factor. The opposite was observed in meadow steppe. In HANTS, the fitting tolerance error was the key factor controlling phenology extraction accuracy. (4) Based on the optimal phenology extraction scheme, the smallest extraction error occurred in meadow steppe at the site scale. This was followed by typical steppe. Desert steppe showed relatively larger errors. This study overcomes the reliance on default parameters in previous studies and proposes a practical framework for phenology extraction for different grassland ecosystems. The findings provide new empirical evidence for method selection and parameter setting in remote sensing phenology monitoring.

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions.

Vegetation phenology is one of the most sensitive indicators to understanding terrestrial ecosystem status and change. However, few studies have been conducted to reveal vegetation phenology variation characteristics over the past two decades, especially under the background of the global warming hiatus since 1998. The agricultural pastoral ecotone of northern China (APENC) is an ideal place to analyze land surface phenology (LSP) variation. Therefore, the spatiotemporal patterns of LSP were quantitatively analyzed at regional, basin and pixel scales based on time-series MODIS NDVI data (2001–2021). Results showed that: (i) The start of the growing season (SOS) occurred in 105–141 Julian days, the end of the growing season (EOS) was between 257 and 281 Julian days and the length of the growing season (LOS) varied from 130 to 172 days. The later SOS was mainly distributed in croplands and typical grassland areas, while the early SOS was observed in forests and sandy vegetation coverage areas. The early EOS occurred in typical grasslands, and the later EOS was concentrated in the southeast boundary. The magnitude of the SOS and LOS fluctuation was less than EOS. (ii) The SOS and EOS exhibited overall insignificant advanced and delayed trends at a rate of −0.09 days·yr⁻¹ and 0.12 days·yr⁻¹, respectively, and the LOS displayed an insignificant extended trend at a rate of 0.26 days·yr⁻¹ at a regional scale. The trends of phenological metrics were consistent with the APENC in the Yellow River and Haihe River Basins. The shortened trend of LOS occurred due to the delayed SOS and advanced EOS in the Songliao River and Continental Basins. (iii) The SOS variation gradually changed from an advanced trend to a delayed trend from a southwest to northeast direction in cropland and grassland ecosystems, whereas an opposite trend was found for EOS. The LOS exhibited a significant extended trend due to the significant advanced and delayed trend of SOS and EOS at p < 0.01 in forest ecosystems. This work provides a critical reference for the vegetation phenology dynamic research of semi-arid and semi-humid regions.

Forest aboveground biomass (AGB) plays a key role in assessing forest productivity. In this study, we extracted individual tree structural parameters using backpack LiDAR, assessed their accuracy using terrestrial laser scanning (TLS) data and field measurements as reference values, and reconstructed 3D models of trees based on parameter-optimized quantitative structural models (AdQSM). The individual tree AGB was estimated based on individual tree volumes obtained from the tree model reconstruction, combined with the basic wood density values of specific tree species. In addition, the AGB calculated using the allometric biomass models was validated to explore the feasibility of nondestructive estimation of individual tree AGB by backpack LiDAR. We found that (1) the backpack LiDAR point cloud extracted individual tree diameter at breast height (DBH) with high accuracy. In contrast, the accuracy of the tree height extraction was low; (2) the optimal parameter values of the AdQSM reconstruction models for Larix gmelinii and Betula platyphylla were HS = 0.4 m and HS = 0.6 m, respectively; (3) the individual tree AGB estimated based on the backpack LiDAR and AdQSM fit well with the reference values. Our study confirms that backpack LiDAR can nondestructively estimate individual tree AGB, which can provide a reliable basis for further forest resource management and carbon stock estimation.

To curb land degradation, a series of ecological restoration projects have been carried out since 1999, leading to dramatic land cover change (LCC) in the agricultural pastoral ecotone of northern China (APENC). To date, there is still lack of timely and accurate land cover (LC) information for management and assessment actions. This paper presents a LC mapping scheme to map annual LC information based on dynamic time warping (DTW) approach and time‐series MODIS‐NDVI product for the APENC. The DTW approach was optimized firstly based on the change of vegetation phenology phase. Next, typical reference time‐series curves set of different LC types were established and pixel‐wise similarity was examined to identify the LC type. Before that, the reconstruction of time‐series MODIS‐NDVI was performed using harmonic analysis of time‐series and reconstruction accuracy was evaluated quantitatively at a regional scale. Results showed that the gap error was relatively large for the high vegetation cover areas while it might be ignored for the moderate and low vegetation cover areas. The overall accuracy of LC mapping in year 2010 (LC‐2010) was 77.89% and kappa coefficient reached 0.71, which suggested that LC mapping scheme could extract LC information with high accuracy. The producer's accuracy ranged between 69.83% (grasslands) and 100% (water body), while the user's accuracy changed from 58% (built‐up) to 86% (forest). The overall spatial agreement between LC‐2010 and GlobaLand30‐2010 was about 64.44%. The total area of forests, croplands, water body and built‐up exhibited an increasing trend while the contrary trend were found for the grasslands and bare land during the 2001–2017. These LC maps are valuable for research the land degradation investigation and monitoring, benefit evaluation of ecological restoration projects and effect of LCC on surface hydrothermal process in APENC.

Inner Mongolia, as a fragile ecological zone in northern China, is prone to severe fires due to natural forces and intensive human disturbances. The development of a fire risk assessment system at the finer spatial scale is not sufficient in this region. In this study, we obtained the data of burned areas and fire hotspots numbers for Inner Mongolia from the Terra/Aqua Moderate-resolution Imaging Spectroradiometer data (MCD45A1 and MOD14A1/MYD14A1, 2002~2016). These fire maps were used to determine the fire spatial and temporal variability, as well as the interactions with environmental controls (climatic, vegetation, topography, and anthropic characteristics) derived in geographic information system (GIS) layers. Based on this, the fire-causing variables were selected as the dependent variables for model building, whereas data on burned area and number of fire hotspots were used for model validation. The fire risk assessment map was then generated in a 500 × 500 m grid cell using an analytic hierarchy process approach and a GIS technique. This work could be easily used for the ultimate aim of supporting fire management.

The development of cities in the vertical dimension is important in valley-type cities where physical growth is limited by terrain. However, little research has focused on three-dimensional urban expansion of valley-type cities. Lanzhou is a typical valley-type city in China and Chengguan District is the core area of Lanzhou City. This research is aimed at understanding the development of valley-type cities through the analysis of the three-dimensional urban expansion of Lanzhou Chengguan District and providing a reference for urban planning. We extracted five periods of architectural contours and height information between 1975 to 2018 with the support of multi-source remote sensing and network data. We used overlay analysis and mathematical statistical methods to analyze urban horizontal expansion and used the building density, floor area ratio, vertical expansion speed, fluctuation degree, and skyline to analyze urban vertical expansion. We found that the mode of horizontal expansion of Chengguan District shifted from adjacency to enclave through mountain area reclamation. The area with the fastest vertical expansion speed first appeared in the horizontal expansion completed area, and then in both the rapid horizontal expansion area and in the horizontal expansion completed area. Before 2007, the speed of horizontal expansion increased and reached its peak while the vertical expansion speed was relatively stable. After that, the former decreased, and the vertical expansion increased rapidly and dominated the urban development. The vertical expansion of the valley-type city gradually dominates urban development. Urban planning should consider the three-dimensional expansion, especially in the vertical dimension.

The agricultural pastoral ecotone of Northern China (APENC), as a fragile ecological zone, has experienced dramatic land use/land cover changes (LULCC) owing to intensive human disturbances. The understanding of the impacts of LULCC on climate, especially the diurnal temperature range (DTR), is not sufficient in this region. The spatio‐temporal processes of LULCC and its impacts on DTR were quantitatively analyzed, using the Terra/Aqua MODIS data (MYD11A2 and MCD12Q1, 2003–2013), on the premise of eliminating or weakening the effects of background climate. Results showed that the areas of croplands, forests, and waterbody increased with the annual rate of 2.84%, 0.95%, and 8.2%, respectively, whereas grasslands and bare land decreased with the annual rate of 0.69% and 7.32%, and built‐up remained quite stable during the period of 2003–2013. The annual changing rate of LULC for 2008–2013 was nearly 2.84 times the value for 2003–2008. In general, LULC contributed to a decline of DTR over the whole APENC both at monthly/seasonal timescale. Croplands, forests, and mutual transformation between croplands and grasslands tended to decrease daytime maximum temperature (T_max), resulting in a decline of DTR. The grasslands could reduce the DTR by decreasing T_max and increasing nighttime minimum temperature (T_min). By contrast, the conversion of grasslands into forests tended to increase T_max and decrease T_min, thus a rise of DTR for winter, summer, and autumn had observed. These results can assist land resource development and relevant policies making in APENC.