A5089650125- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Land Use and Ecosystem Services
- Remote Sensing and Land Use
- Species Distribution and Climate Change
- Environmental Changes in China
- Urban Heat Island Mitigation
- Soil Moisture and Remote Sensing
- Remote-Sensing Image Classification
- Precipitation Measurement and Analysis
- Plant Water Relations and Carbon Dynamics
- Smart Agriculture and AI
- Climate change and permafrost
Jiangsu Police Officer College
2024-2025
Ministry of Public Security of the People's Republic of China
2024
Nanjing University
2019-2022
Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting imprecise mathematical algorithms for vegetation monitoring physiological diagnoses; therefore, research on shadows from forest canopy internal composition is very important. Red edge an ideal indicator vegetation’s photosynthesis biomass because its strong connection physicochemical parameters. In this study, red parameters (curve...
Wheat is one of the most important staple crops globally. Timely mapping and monitoring wheat harvests are essential for efficiently scheduling large-scale harvesters, ensuring timely completion harvest, maintaining grain quality. Traditional manual survey methods obtaining harvest information neither highly accurate nor cost-effective do not meet needs agricultural management departments. This study introduces two novel indices detection: optical-band brightness index (OBHI) visible-band...
Using remote-sensing technology to accurately map the composition and distribution of tree species is vital for sustainable forest resource management. Sentinel-2 data with dense time-series observations enable identify species. However, few studies clarify differences in classification using images natural planted forest. Two study areas different environments (planted forest) were selected evaluate potential imagery. Our results show that red-edge band, short-wave infrared (SWIR) band...
Most natural forests are mixed forests, a broadleaf-conifer forest is essentially heterogeneously pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the level, resulting decrease inversion accuracy, which an issue for heterogeneous forests. Therefore, information canopy composition of basis accurately retrieving parameters using sensing. Medium...
The fractional coverage of the illuminated vegetation (IV), soil (IS), shaded (SV), and (SS) are four essential structural parameters (fIV, fIS, fSV, fSS) describing canopy. Their correct estimation can help improve monitoring growth. In this study, we propose evaluate coastal/aerosol-band-based vegetation-shadow indices (CVSIs) using normalized difference top-of-atmosphere (TOA) reflectance in coastal/aerosol band blue, green, red, near-infrared (NIR) bands. We used CVSIs to illumination...
林龄结构信息能够有效反映区域森林群落不同生长阶段的固碳能力,对于评估森林生态系统的健康状况具有重要意义。本研究以中国温带典型优势树种落叶松林为研究对象,分别选择其芽萌动期、展叶期和落叶期时段的Sentinel-2影像,采用多元线性回归(MLR)、随机森林(RF)、支持向量机回归(SVR)、前馈反向传播神经网络(BP)以及多元自适应回归样条(MARS)等5种方法依次构建落叶松林龄反演模型。通过相关性分析首先确定最佳遥感反演物候期,并在此基础上根据相关性差异筛选出5个最优特征变量用于模型反演,分别为冠层含水量(CWC),归一化水体指数(NDWI),叶面积指数(LAI),光合有效辐射吸收率(FAPAR)和植被覆盖度(FVC)。研究结果表明,展叶期为落叶松林最佳遥感反演物候期。除植被衰减指数(PSRI)以及落叶期的NDVI、RVI外,落叶松林龄与各指标之间均呈负相关关系,其中与冠层含水量(CWC)的相关性最高,pearson相关系数达到-0.74(p<0.01)。此外,不同模型反演结果表明,随机森林模型(RF)为最佳落叶松林龄估测模型,其平均决定系数R2和平均均方根误差RMSE分别为0....
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Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, assumption underlying MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over 16-day retrieval period—may be unreliable, especially since canopy structure vegetation undergoes stark changes at start season (SOS) and end (EOS). Therefore, to...
We show an improvement in extraction of remotely sensed phenology the Qinghai–Tibet Plateau (QTP). The includes multiple preprocessing, newly proposed absolute growth rate and relative models based on botany phenology, selection appropriate model at different growing stages. refer to this as tempo-differentially selected (TDSGM). developed TDSGM is a comprehensive accurate remote sensing phenological without manual intervention, which better than current mainstream methods. Results that: (1)...