Grassland aboveground biomass (AGB) is a key indicator of grassland ecosystem structure and function, and its accurate monitoring is of great importance for assessing grassland ecological conditions and supporting sustainable grassland management. Traditional biomass estimation methods based on vegetation indices (VIs) often suffer from saturation due to canopy shading. However, comparative studies on VI saturation and the saturation height of AGB detectable by different indices remain limited. In this study, we evaluated 12 commonly used VIs based on field-measured AGB and hyperspectral data in the Hulunbuir meadow steppe. Relationships between vertically accumulated biomass and VIs were analyzed to identify optimal AGB fitting models and to determine the saturation height of each index. Results showed that vertical distribution of AGB followed a unimodal pattern, with biomass peaking at approximately 36 cm in this region. This study employed four models (namely the Linear model, the Logarithmic model, the Power Function model and the Gompertz model) to fit the relationship between the vegetation index and AGB. Among them, Gompertz models consistently outperformed other models, indicating saturation across all indices. Based on saturation height, the 12 VIs were classified into two groups: ARVI, GNDVI, NDRE, OSAVI, and SAVI saturated at 40 cm, whereas DVI, EVI, MSAVI, NDPI, NDVI, RVI, and VARI maintained sensitivity up to 50 cm, demonstrating a stronger anti-saturation capacity. NDVI and NDPI exhibited the highest fitting accuracy and resistance to saturation. These findings validate the saturation limitations of VIs and provide guidance for selecting appropriate indices to improve the accuracy of grassland biomass retrieval.

Satellite remote sensing precipitation products have been widely used to estimate precipitation,espe-cially in regions with sparse ground observation stations.However,the lower spatial resolution of these satellite products limits their application in localized regions and watersheds.This study proposes a fused downscaling framework based on the area-to-point kriging(ATPOK)algorithm and geographic weighted regression kriging(GWRK)algorithm to downscale TRMM 3B43 data for the Qinghai region of China from 2000 to 2019.The framework incorporates ground observation station data,normalized difference vegetation index(NDVI),digital elevation model,slope,and auxiliary factors for calibration,ultimately obtaining precipitation products at 1 km resolution.The results showed that:(1)The proposed fused downscaling framework can effectively improve the accuracy of TRMM 3B43 products;however,it cannot eliminate the overestimation of TRMM 3B43.(2)Com-pared to the ATPOK algorithm,the GWRK algorithm using data TRMM precipitation data and auxiliary factors can better estimate annual/monthly precipitation data.(3)Based on the study of the relationship between TRMM 3B43 and NDVI,it was found that NDVI responds to precipitation with a delay of 0-2 months.(4)Based on the spatiotemporal variation analysis of downscaled precipitation products,significant increases in monthly precipita-tion were observed in the Qinghai region,with an interannual change rate of 3.33%in dry month(December)and 1.79%in wet month(July).

The terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern. However, there is still a lack of a high-resolution GPP dataset for Qinghai Province. To address this issue, we processed all Landsat images of Qinghai from 1987 to 2021 using the GEE, and we combined multi-source auxiliary data to estimate GPP using the revised EC-LUE model. We compared our GPP dataset with flux observations to verify its accuracy. The results showed that our GPP dataset had a high correlation with the flux tower observations, with correlation coefficients of 0.984 at CF-AM site and 0.976 at CN-Ha2 site, respectively, and each site had an RMSE of 11.960 g C & sdot; m - 2 & sdot; 16 d - 1 and 12.986 g C & sdot; m - 2 & sdot; 16 d - 1 , respectively. There are different deviations between our GPP dataset and the mainstream GPP datasets in various vegetation types, with the average correlation coefficient ranging from 0.431 to 0.943. By comparing with the flux observations and the related analysis, we demonstrated that our GPP dataset features better accuracy, higher spatial resolution, and more temporal coverage than mainstream GPP datasets. This study offers the first long-term high-resolution GPP dataset for Qinghai Province, and we believe that this dataset has important implications for ecological management and climate research.

Net primary productivity (NPP), as an indicator of ecological functioning, plays an important role in regional and global carbon cycles. Although many studies have estimated the NPP of vegetation on the Qinghai Plateau (QP), the existing NPP datasets over the QP are either of low spatial resolution or limited-duration time-series. These shortcomings restrict our ability to explore the spatial distribution and long-term trends of NPP at a finer scale. To address this gap, we present a new monthly NPP dataset (QP_NPP30) at a high spatial resolution (30 m) over the QP for the period 1987–2021. We constructed this dataset using the Carnegie-Ames-Stanford-Approach (CASA) model and multisource data, including reconstructed normalized difference vegetation index (NDVI) data, reanalysis data, land cover, and other ancillary data. To reconstruct the NDVI, a harmonic regression model based on the Google Earth Engine (GEE) was applied to the NDVI time series data. Statistical analysis of QP_NPP30 showed that the NPP in the QP has increased over the past 35 years (0.92 $g C/m^{2}/yr$). Furthermore, we found that NPP is concentrated in June, July, and August, accounting for approximately 73% of the annual total. To validate our dataset, we compared it with measured NPP and with the MODIS NPP product (MOD-NPP). Our results demonstrated that QP_NPP30 has similar spatial patterns to MOD-NPP, but offers richer spatial detail. Specifically, QP_NPP30 has a higher accuracy than MOD-NPP, by comparing with the measured data (r = 0.695, RMSE = 132.823 $g C/m^{2}/yr$ for QP_NPP30; r = 0.328, RMSE = 158.586 $g C/m^{2}/yr$ for MOD-NPP).

The lack of long-duration, high-frequency grassland classification products limits further understanding of the grasslands’ long-term succession. This study first explored the annual mapping of grassland with fourteen categories at 30 m in Qinghai, China, from 1986 to 2020 based on Google Earth Engine (GEE) and the Integrated Orderly Classification System (IOCSG). Specifically, we proposed an image composite strategy to obtain annual source images for classification, by quarterly compositing multi-sensor and multi-temporal Landsat surface reflectance images. Subsequently, the 35-year area time series of each category was analyzed in terms of trend, degree of change, and succession of each category. The results indicate that the different grasslands of the IOCSG can be effectively differentiated by utilizing the designed feature bands of remote sensing data. Additionally, the proposed annual image composition strategy can not only decrease the invalid pixels but also promote classification accuracy. The grasslands transition analysis from 1986 to 2020 implies the progressive urbanization, warming, and wetting trend in Qinghai. The generated 35-year annual grassland thematic data in Qinghai can serve as an elementary dataset for further regional ecological and climate change studies. The proposed methodology of large-scale grassland classification can also be referenced to other applications like land use/cover mapping and ecological resource monitoring.

In this paper, we propose a deep learning-based model to detect extratropical cyclones (ETCs) of the northern hemisphere, while developing a novel workflow of processing images and generating labels for ETCs. We first labeled the cyclone center by adapting an approach from Bonfanti et al. in 2017 and set up criteria of labeling ETCs of three categories: developing, mature, and declining stages. We then gave a framework of labeling and preprocessing the images in our dataset. Once the images and labels were ready to serve as inputs, an object detection model was built with Single Shot Detector (SSD) and adjusted to fit the format of the dataset. We trained and evaluated our model with our labeled dataset on two settings (binary and multiclass classifications), while keeping a record of the results. We found that the model achieves relatively high performance with detecting ETCs of mature stage (mean Average Precision is 86.64%), and an acceptable result for detecting ETCs of all three categories (mean Average Precision 79.34%). The single-shot detector model can succeed in detecting ETCs of different stages, and it has demonstrated great potential in the future applications of ETC detection in other relevant settings.

Satellite-based PM2.5 estimation has been widely used to assess health impact associated with PM2.5 exposure and might be affected by spatial resolutions of satellite input data, e.g., aerosol optical depth (AOD). Here, based on Multi-Angle Implementation of Atmospheric Correction (MA-IAC) AOD in 2020 over the Yangtze River Delta (YRD) and three PM2.5 retrieval models, i.e., the mixed effects model (ME), the land-use regression model (LUR) and the Random Forest model (RF), we compare these model performances at different spatial resolutions (1, 3, 5 and 10 km). The PM2.5 estimations are further used to investigate the impact of spatial resolution on health assessment. Our cross-validated results show that the model performance is not sensitive to spatial resolution change for the ME and LUR models. By contrast, the RF model can create a more accurate PM2.5 prediction with a finer AOD spatial resolution. Additionally, we find that annual population-weighted mean (PWM) PM2.5 concentration and attributable mortality strongly depend on spatial resolution, with larger values estimated from coarser resolution. Specifically, compared to PWM PM2.5 at 1 km resolution, the estimation at 10 km resolution increases by 7.8%, 22.9%, and 9.7% for ME, LUR, and RF models, respectively. The corresponding increases in mortality are 7.3%, 18.3%, and 8.4%. Our results also show that PWM PM2.5 at 10 km resolution from the three models fails to meet the national air quality standard, whereas the estimations at 1, 3 and 5 km resolutions generally meet the standard. These findings suggest that satellite-based health assessment should consider the spatial resolution effect.

高分辨率的降水数据对于复杂地形区的精确水文预报和气候模拟至关重要.利用青藏高原的植被、地形和地理位置特征,建立了与降水的回归模型,将全球降水测量(GPM)IMERG的年降水量从0.1°降尺度至1 km,通过分解年降水获得月降水量数据,并用气象站点的实测数据进行校准.得出以下结论:①GPM IMERG月降水量略大于地面观测值,与2015～2017年的站点数据相关性较高(R2=0.79);②通过建立降尺度模型,提高了研究区GPM IMERG的空间分辨率;③利用站点数据校准后的月降水量,可以反映降水的细节特征,尤其是在雨季和湿润地区.该模型可用于获得地形复杂地区的高空间分辨率降水资料,对水文学和气象学研究具有重要意义.

Unsupervised change detection(CD) from remotely sensed images is a fundamental challenge when the ground truth for supervised learning is not easily available. Inspired by the visual attention mechanism and multi-level sensation capacity of human vision, we proposed a novel multi-scale analysis framework based on multi-scale visual saliency coarse-to-fine fusion (MVSF) for unsupervised CD in this paper. As a preface of MVSF, we generalized the connotations of scale as four classes in the field of remote sensing (RS) covering the RS process from imaging to image processing, including intrinsic scale, observation scale, analysis scale and modeling scale. In MVSF, superpixels were considered as the primitives for analysing the difference image(DI) obtained by the change vector analysis method. Then, multi-scale saliency maps at the superpixel level were generated according to the global contrast of each superpixel. Finally, a weighted fusion strategy was designed to incorporate multi-scale saliency at a pixel level. The fusion weight for the pixel at each scale is adaptively obtained by considering the heterogeneity of the superpixel it belongs to and the spectral distance between the pixel and the superpixel. The experimental study was conducted on three bi-temporal remotely sensed image pairs, and the effectiveness of the proposed MVSF was verified qualitatively and quantitatively. The results suggest that it is not entirely true that finer scale brings better CD result, and fusing multi-scale superpixel based saliency at a pixel level obtained a higher F1 score in the three experiments. MVSF is capable of maintaining the detailed changed areas while resisting image noise in the final change map. Analysis of the scale factors in MVSF implied that the performance of MVSF is not sensitive to the manually selected scales in the MVSF framework.

Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated. Ecosystem responses to each of these drivers are perhaps most clearly seen in phenology changes due to global climate change (warming climate) and urbanization (heat island effect). The phenology of vegetation can influence many important ecological processes, including primary production, evapotranspiration, and plant fitness. Therefore, evaluating the interacting effects of urbanization and climate change on vegetation phenology has the potential to provide information about the long-term impact of global change. Using remotely sensed time series of vegetation on the Yangtze River Delta in China, this study evaluated the impacts of rapid urbanization and climate change on vegetation phenology along an urban to rural gradient over time. Phenology markers were extracted annually from an 18-year time series by fitting the asymmetric Gaussian function model. Thermal remote sensing acquired at daytime and nighttime was used to explore the relationship between land surface temperature and vegetation phenology. On average, the spring phenology marker was 9.6 days earlier and the autumn marker was 6.63 days later in urban areas compared with rural areas. The spring phenology of urban areas advanced and the autumn phenology delayed over time. Across space and time, warmer spring daytime and nighttime land surface temperatures were related to earlier spring, while autumn daytime and nighttime land surface temperatures were related to later autumn phenology. These results suggest that urbanization, through surface warming, compounds the effect of climate change on vegetation phenology.

降水的季节性时空分布研究对东北地区的生态保护和农业生产有重要意义.基于植被指数、地形因子与降水的相关性,采用深度学习模型,对2009—2018年10 a间平均1,4,7,10月TRMM_3B43产品降尺度至0.01°(约1 km),使用站点实测数据进行精度校正,并填补TRMM未覆盖的50°N以上地区.结果表明,该模型效果优于随机森林,可有效获得各季节较高空间分辨率与精度的研究区域降水分布,校正后全局决定系数R2介于0.881～0.952之间,均方根误差介于1.222～13.11 mm之间,平均相对误差介于7.425％～28.41％之间,其中4月和10月份拟合度较好,1月和7月份相对稍差.

航拍影像富含光谱信息、纹理信息和空间信息,机载LiDAR(Light Detection and Ranging)能够提供地物的三维信息.综合利用两类数据的优势,研究了一种面向对象的城市地物分类方法.通过预处理将LiDAR点云转换成二维栅格数据,与航拍影像进行配准;结合光谱信息和高度信息对研究影像进行多尺度分割,依据最优分割尺度计算模型选择最优分割尺度;对分割对象提取各类特征,采用XGBoost算法进行特征选择,选择支持向量机(Support Vector Machine,SVM)分类器进行分类,为体现XGBoost算法的优势,借助SVM分类器与Relief和RFE两种传统的特征选择算法比较;基于一定规则将阴影区域地物区分以及合并到真实地物类别中,实现最终的城市地物分类.在3个区域测试分类方法,结果表明本文研究方法可行有效,能够较好地应用于城市地物分类.

Maximum tree height is an important indicator of forest vegetation in understanding the properties of plant communities. In this paper, we estimated regional maximum tree heights across the forest of the Great Khingan Mountain in Inner Mongolia with the allometric scaling and resource limitations model. The model integrates metabolic scaling theory and the water–energy balance equation (Penman–Monteith equation) to predict maximum tree height constrained by local resource availability. Monthly climate data, including precipitation, wind speed, vapor pressure, air temperature, and solar radiation are inputs of this model. Ground measurements, such as tree heights, diameters at breast height, and crown heights, have been used to compute the parameters of the model. In addition, Geoscience Laser Altimeter System (GLAS) data is used to verify the results of model prediction. We found that the prediction of regional maximum tree heights is highly correlated with the GLAS tree heights (R2 = 0.64, RMSE = 2.87 m, MPSE = 12.45%). All trees are between 10 to 40 m in height, and trees in the north are taller than those in the south of the region of research. Furthermore, we analyzed the sensitivity of the input variables and found the model predictions are most sensitive to air temperature and vapor pressure.

针对现有图像能见度测量方法存在目标物架设复杂和物理量解算困难的问题,该文提出一种简单有效的基于图像特征的大气能见度估算方法.采用图像梯度和对比度作为能见度的特征变量,将图像感兴趣区进行分窗处理后,利用支持向量机(SVM)和随机森林(RF)两种算法建立能见度数值与图像特征的关系模型.结果表明,图像梯度和对比度特征能够准确反映能见度数值大小;窗口总数对模型精度有影响.窗口总数小于35时SVM算法优于RF算法;在窗口总数为70、窗口大小为140×10时,RF算法的像素精度最高,最优模型决定系数R2为0.965,均方根误差为658.13 m.

立木测量,尤其是树高,一直是树木信息的关键参数.手机图像处理技术,为我们提供了一种更加方便,可以进行较为精确树高量测的方法.本文利用经过检校场检校过的手机获取被测树木的立体像对,采用计算机视觉中的surf算法进行同名像点匹配,经过相对定向重建树木在像空间辅助坐标系中比例系数未知的立体模型,利用已知标杆长度确定比例最后求得树高.结果显示,绝对相对误差在6％以内.此方法测量结果精度较高,能够应用于实际测量,具有较高的实用性.

青藏高原对全球气候研究具有重要意义,而降水数据对水文、气象和生态等领域的研究也至关重要,且随着研究内容和尺度的变化,对高时空分辨率的历史降水数据的需求越发迫切.本文基于TRMM 3B43降水数据,采用随机森林算法,引入归一化植被指数(AVHRR NDVI)、高程(SRTM DEM)、坡度、坡向、经度、纬度6个地理因子,建立历史降水重建模型,获得1982-1997年分辨率为0.0833°的青藏高原年降水数据,然后根据比例系数法计算出月降水数据.为提高精度,利用站点数据对月降水数据进行校正.结果表明,该方法能简单有效地获得高时空分辨率的历史降水数据,决定系数R2大部分在0.4-0).9之间,平均值为0.6767,其中夏季效果最好,冬季效果最差;均方根误差RMSE和平均绝对误差MAE均在50 mm以下,RMSE均值为22.66mm,MAE均值为15.97mm;偏差Bias较小,基本在0.0～0.1之间.

This study develops a modeling framework for utilizing the large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and Land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-Radiometer (MODIS) imagery, meteorological data, and forest measurements for monitoring stocks of total biomass (including aboveground biomass and root biomass). The forest tree height models were separately used according to the artificial neural network (ANN) and the allometric scaling and resource limitation (ASRL) tree height models which can both combine the climate data and satellite data to predict forest tree heights. Based on the allometric approach, the forest aboveground biomass model was developed from the field measured aboveground biomass data and the tree heights derived from two tree height models. Then, the root biomass should scale with the aboveground biomass. To investigate whether this approach is efficient for estimating forest total biomass, we used Northeast China as the object of study. Our results generally proved that the method proposed in this study could be meaningful for forest total biomass estimation (R2 = 0.699, RMSE = 55.86).

With the support of airborne Light Detection and Ranging (LiDAR)data and high spatial resolu-tion aerial imagery,this paper presents an individual tree extraction method that takes the region of urban as the study area.The elevation difference model originated from LiDAR data was used to extract regions of interest including trees.Then,masking was applied to the high spatial resolution aerial imagery to get the same regions.Besides,image segmentations,based on the marked watershed algorithm,were processed on the high spatial resolution aerial imagery and the elevation difference model separately to extract individual tree crowns.Finally,we took a visual interpretation to delineate tree crowns manually and this result was regarded as the reference crowns map.The extraction accuracies were assessed by comparing the spatial re-lationships of the reference crowns and the automated delineated tree crowns based on the elevation differ-ence model and the high resolution imagery.The results show that the LiDAR data is developed to improve the efficiency of obtaining forest region that the canopy height model include 85.25% forest information.In addition,the tree crowns extraction accuracy based on the high resolution aerial imagery is 57.14%,while another extraction accuracy based on the elevation difference model is 42.47%,which indicated that the marked watershed algorithm proposed in this paper is effective and the high resolution imagery is better than the elevation difference model to extract tree crowns.

We used 12 height curve models with 400 kinds of height-diameter data of sampling trees,including Larix gmelinii Rupr.,Pinus sylvestris var.mongholica and the mixed forest of Pinus koraiensis Sieb.et Zucc.and Larix gmelinii Rupr.,to evaluate the coefficient determination and study the optimal fitting model of three different trees,the total error,average relative error,bias,and root mean square error for model accuracy and precision.With the 11 model of tree height diameter curve fitting parameters,significant correlation between tree height and diameter,the power model is the best fitting model.The height-diameter curves conform to the theory of allometric scaling.According to the speed error index data,the fitting is ideal with higher precision.

树木信息的采集是森林资源调查的基础,通过安卓智能手机图像处理的研究,为单株树木胸径信息的采集提供简单、高效的测量方式,在野外环境复杂或者没有仪器的时候,也可以进行较为准确的树木胸径估测.利用安卓智能手机上的图像处理技术,结合近景摄影测量原理,该文提出了2种测定单株树木胸径的方法:第1种是基于近景摄影测量技术的测量方法.此方法通过手机摄像头摄像时传感器获取的手机倾斜角度和变化测量位置后测得的高度差,利用三角函数计算得到立木胸径;第2种是基于图像立木边缘提取的测量方法.此法利用边缘检测算子得到树木的轮廓,再根据比例因子,结合用户输入的参数和应用程序获取的手机相关参数计算立木胸径.最后开发了相应的智能手机App(Application,应用软件).结果显示,基于近景摄影测量技术的测量方法,其绝对误差的绝对值在8 cm以内,平均相对误差为6％;基于图像立木边缘提取的测量方法,其绝对误差绝对值在5 cm以内,软件测量胸径与卷尺间接测量的直径值之间平均误差为11.08％.这说明,2种方法软件测量结果精度较高,适合野外操作;相比较而言,第2种方法测量结果更加稳定,更加具有实用性.