GF-1 Data Research Articles

Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images

Seagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two methods to map seagrass in 2020 using Chinese GF2 satellite images: maximum-likelihood and object-oriented classification. Sentinel-2 images from 2016 to 2020 were used to extract information on seagrass distribution changes. The following conclusions were obtained. (1) Based on GF2 imagery, both the classical maximum likelihood classification (MLC) method and the object-based image analysis (OBIA) method can effectively extract seagrass information, and OBIA can also portray the overall condition of seagrass patches. (2) The total seagrass area in the study area in 2020 was about 395 hectares, most of which was distributed in Xincun Harbor. The southern coast of Xincun Harbor is an important area where seagrass is concentrated over about 228 hectares in a strip-like continuous distribution along the coastline. (3) The distribution of seagrasses in the study area showed a significant decaying trend from 2016 to 2020. The total area of seagrass decreased by 79.224 ha during the five years from 2016 to 2020, with a decay rate of 16.458%. This study is the first on the comprehensive monitoring of seagrass in Xincun Bay using satellite remote sensing images, and comprises the first use of GF2 data in seagrass research, aiming to provide a reference for remote sensing monitoring of seagrass in the South China Sea.

Estimation of Winter Wheat Residue Coverage Based on GF-1 Imagery and Machine Learning Algorithm

Crop residue is an important component of farmland ecosystems, which is of great significance for increasing soil organic carbon, mitigating wind erosion and water erosion and conserving soil and water. Crop residue coverage (CRC) is an important parameter to characterize the number and distribution of crop residues, and also a key indicator of conservation tillage. In this study, the CRC of wheat was taken as the research object. Based on the high-resolution GF-1 satellite remote sensing imagery from China, decision tree (DT), gradient boosting decision tree (GBDT), random forest (RF), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting regression (XGBR) and other machine learning algorithms were used to carry out the estimation of wheat CRC by remote sensing. In addition, the comparisons with sentinel-2 imagery data were also utilized to assess the potential of GF satellite data for CRC estimates. The results show the following: (1) Among the spectral indexes using shortwave infrared characteristic bands from sentinel-2 imagery, the dead fuel index (DFI) was the best for estimating wheat CRC, with an R2 of 0.54 and an RMSE of 10.26%. The ratio vegetation index (RVI) extracted from visible and near-infrared characteristic bands from GF-1 data performed the best, with an R2 of 0.46 and an RMSE of 11.39%. The spectral index extracted from GF-1 and sentinel-2 images had a significant response relationship with wheat residue coverage. (2) When only the characteristic bands from the visible and near-infrared spectral ranges were applied, the effects of the spatial resolution differences of different images on wheat CRC had to be taken into account. The estimations of wheat CRC with the high-resolution GF-1 data were significantly better than those with the Sentinel-2 data, and among multiple machine learning algorithms adopted to estimate wheat CRC, LASSO had the most stable capability, with an R2 of 0.46 and an RMSE of 11.4%. This indicates that GF-1 high-resolution satellite imagery without shortwave infrared bands has a good potential in applications of monitoring crop residue coverage for wheat, and the relevant technology and method can also provide a useful reference for CRC estimates of other crops.

Exploring the potential of Chinese GF-6 images for crop mapping in regions with complex agricultural landscapes

Accurate and timely crop mapping is crucial for environment assessment, food security and agricultural production. However, for the areas with high landscape heterogeneity and frequent cloudy and rainy weather, the insufficient high-quality satellite images limit the accuracy of crop classification. The recently launched Chinese GF-6 wide field-of-view camera (WFV) with a revisit cycle of 4-day and spatial resolution of 16-meter shows great potential for agricultural monitoring. In this study, Qianjiang City characterized by complex agricultural landscapes was selected as the research area to assess the potential of GF-6 data in identifying crop types. Firstly, the pairwise and global separability were calculated to analyze the effect of different spectral-temporal features of GF-6 images on crop classification. A total of 255 spectral-temporal features derived from 15 GF-6 tiles were then used to perform random forest classification. Furthermore, the classification results were evaluated based on 671 field samples and then compared the accuracy between GF-6 data and Sentinel-2 or Landsat-8 data. In addition, the earliest identifiable time of crop types was also determined by iteratively using all available GF-6 data during each time period. The results suggested that the overall accuracy (OA) of all available GF-6 images was 91.55%, which was significantly higher than that of Landsat-8 data (OA = 85.97%) and was slightly lower than that of Sentinel-2 data (OA = 93.10%). The newly added red-edge bands (0.69 ∼ 0.73 μm, 0.73 ∼ 0.77 μm) and their derivative vegetation indices were important spectral features, and the period from mid-March to early-April was the best temporal window for crop identification in our research area. Moreover, late July was the earliest crop identifiable time with overall accuracy of 90% for the first time of the year. These results indicated the great potential of GF-6 images for classifying crop types in the areas with complex cropping system and fragmented agricultural landscapes, particularly when integrating other satellite data with comparable spatial resolution (e.g. Chinese GF-1 data and Sentinel-2 data).

Extraction of Landslide Information Based on Object-Oriented Approach and Cause Analysis in Shuicheng, China

In China, landslides are abundant, widespread, and regular, destroying villages and agriculture and sometimes posing a threat to people’s lives. The question of how to rapidly detect and attain landslide data is a significant topic of research, yet traditional measurement using medium-resolution remote sensing data is problematic. Object-oriented categorization is utilized in this research to extract landside data from high-resolution GF-1 and Sentinel-2 data. Data preprocessing begins with orthophoto correction, image matching, and data fusion, followed by band enhancement, which comprises band synthesis, principal component analysis, and filtering, and finally landside extraction using an object-oriented technique. The impact of geology, lithology, rainfall, and human activities on the occurrence of landslides in the study area is explored utilizing DEM data, visualization tools, remote sensing interpretation map, and other associated data. The studies are conducted in Shuicheng County, Guizhou Province, China, with a segmentation scale of 25 pixels and 14 classification feature parameters. Following that, the landslide mass is extracted and categorization findings of nearby characteristics are acquired. Finally, the destructiveness of the landslide is determined by comparing the results of object-oriented classification before and after the landslide. With a Kappa coefficient of 0.76 and a landslide extraction accuracy of 79.8%, the overall classification accuracy is 87%. Combined with the geological structure, rock lithology, spatial location, landslide occurrence process, elevation of the study area, precipitation and the impact of human activities, the causes of the landslide are discussed and analyzed. The early warning of other unknown landslides can be obtained by analyzing the features of the aforementioned components.

Crop Mapping with Combined Use of European and Chinese Satellite Data

Agricultural landscapes are characterized by diversity and complexity, which makes crop mapping at a regional scale a top priority for different purposes such as administrative decisions and farming management. Project 32194 of the Dragon 4 Program was implemented to meet the requirements of crop mapping, with the specific objective to develop suitable approaches for precise crop mapping with combined uses of European and Chinese high- and medium-resolution satellite images. Two sub-projects were involved in the project. The first was to focus on the use of time series high-resolution satellite data, including Sentinel-2 (S2, European satellite data) and Gaofen-1 (GF-1, Chinese satellite data), due to their similar spectral bands for Earth observation, while the second was to focus on medium-resolution data sources, i.e., the European Project for On-Board Autonomy–Vegetation (PROBA-V) and Chinese Fengyun-3 Medium Resolution Spectral Imager (FY-3 MERSI) satellite data, also due to their similar spectral channels. The approach of the European Space Agency (ESA) Sent2Agri project for crop mapping was adapted in the first sub-project and applied to the Yellow River irrigated district (YERID) of Ningxia in northwest China in order to assess its ability to accurately identify crop types in China. The goal of the second sub-project was to explore the potential of both European and Chinese medium-resolution satellite data for crop assessment in a large area. Methods to handle the data and retrieve the required information for the precise crop mapping were developed in the study, including the adaptation of the ESA approach to GF-1 data and the application of algorithms for classification. A scheme for the validation of the crop mapping was developed in the study. The results of implementing the scheme to the YERID in Ningxia indicated that the overall accuracies of crop mapping with S2 and GF-1 can be high, up to 94–97%, and the mapping had an accuracy of 88% with the PROBA-V and FY3B-MERSI data. The very high accuracy suggests the possibility of precise crop mapping with the combined use of time series high- and medium-resolution satellite data when suitable approaches are chosen to handle the data for the classification of crop types.

Risk Assessment of Urban Floods Based on a SWMM-MIKE21-Coupled Model Using GF-2 Data

Global climate change and rapid urbanization have caused increases in urban floods. Urban flood risk assessment is a vital method for preventing and controlling such disasters. This paper takes the central region of Cangzhou city in Hebei Province as an example. Detailed topographical information, such as the buildings and roads in the study area, was extracted from GF-2 data. By coupling the two models, the SWMM and MIKE21, the spatial distribution of the inundation region, and the water depth in the study area under different return periods, were simulated in detail. The results showed that, for the different return periods, the inundation region was generally consistent. However, there was a large increase in the mean inundation depth within a 10-to-30-year return period, and the increase in the maximum inundation depth and inundation area remained steady. The comprehensive runoff coefficient in all of the scenarios exceeded 0.8, indicating that the drainage system in the study area is insufficient and has a higher flood risk. The flood risk of the study area was evaluated based on the damage curve, which was obtained from field investigations. The results demonstrate that the loss per unit area was less than CNY 250/m2 in each return period in the majority of the damaged areas. Additionally, the total loss was mainly influenced by the damaged area, but, in commercial areas, the total loss was highly sensitive to the inundation depth.

Estimating the biomass of rice by combining GF-1 and RADARSAT-2 data

Estimating the biomass of rice by combining GF-1 and RADARSAT-2 data

A new satellite-derived dataset for marine aquaculture areas in China's coastal region

Abstract. China has witnessed extensive development of the marine aquaculture industry over recent years. However, such rapid and disordered expansion posed risks to coastal environment, economic development, and biodiversity protection. This study aimed to produce an accurate national-scale marine aquaculture map at a spatial resolution of 16 m, using a proposed model based on deep convolution neural networks (CNNs) and applied it to satellite data from China's GF-1 sensor in an end-to-end way. The analyses used homogeneous CNNs to extract high-dimensional features from the input imagery and preserve information at full resolution. Then, a hierarchical cascade architecture was followed to capture multi-scale features and contextual information. This hierarchical cascade homogeneous neural network (HCHNet) was found to achieve better classification performance than current state-of-the-art models (FCN-32s, Deeplab V2, U-Net, and HCNet). The resulting marine aquaculture area map has an overall classification accuracy > 95 % (95.2 %–96.4, 95 % confidence interval). And marine aquaculture was found to cover a total area of ∼ 1100 km2 (1096.8–1110.6 km2, 95 % confidence interval) in China, of which more than 85 % is marine plant culture areas, with 87 % found in the Fujian, Shandong, Liaoning, and Jiangsu provinces. The results confirm the applicability and effectiveness of HCHNet when applied to GF-1 data, identifying notable spatial distributions of different marine aquaculture areas and supporting the sustainable management and ecological assessments of coastal resources at a national scale. The national-scale marine aquaculture map at 16 m spatial resolution is published in the Google Maps kmz file format with georeferencing information at https://doi.org/10.5281/zenodo.3881612 (Fu et al., 2020).

Monitoring of Rice Irrigation Area Based on GF-1 Data and Spectral Matching Technology- A Case Study of Chuanhang Irrigation Area in Suqian City

China is the world’s largest rice producer and consumer. Accurate identification of rice planting and irrigation area is of great significance to China’s food security. Based on GF-1 multispectral data, spectral matching and OTSU algorithm were used to monitor the rice irrigation area in Chuanhang Irrigation Area from 2016 to 2020. The accuracy was verified by confusion matrix. The overall accuracy was 98.1691%, 91.8706%, 95.2451%, 93.0604%, 91.9697%, respectively. The Kappa coefficients were 0.9541, 0.7898, 0.8777, 0.8330 and 0.7824, respectively, which proved that this method was suitable for rice irrigation area extraction based on high spatial resolution remote sensing data, and could provide technical support for rice irrigation area monitoring in irrigated areas.

Consistency analysis of GF-1 and GF-6 satellite wide field view multi-spectral band reflectance

Chinese GF-1 and GF-6 satellites are currently operational for routine Earth observation, which can acquire multi-spectral data with moderate spatial resolution, high temporal resolution and wide coverage. There are slight differences between the wide field view (WFV) sensors onboard both satellites. Therefore, consistency analysis is very necessary for the networking of GF-1 and GF-6 WFV data to enhance the observation frequency. In this study, spectral band reflectance and vegetation indices in three regions dominated by urban, cropland and grassland, and forest were used to analyze the consistency of GF-1 and GF-6 WFV data. Consistency analysis results showed that the average coefficients of determination (R2) between GF-1 and GF-6 WFV data were 0.86, 0.88, 0.88 and 0.93 for blue, green, red and NIR bands, as well as 0.86, 0.82 and 0.6 for normalized difference vegetation index (NDVI), ratio vegetation index (RVI) and soil adjusted vegetation index (SAVI), respectively. Therefore, the subtle difference and high correlation of spectral band reflectance and vegetation indices indicated that the GF-1 and GF-6 WFV data had a degree of good consistency and had the potential of networking to monitor the rapid change of the Earth surface.

Surface biomass monitoring of rotational grazing in alpine grassland based on satellite Multi-spectral images

The surface of the biomass monitoring is blocked rotational grazing land prerequisite for high scores (GF - 1) satellite multispectral image and artificial biomass index test, using the ENVI software GF1 or GF6 data (radiation calibration atmospheric correction of RPC orthographical correction), and then calculate the vegetation index (NDVI), NDVI can detect vegetation growth status, vegetation coverage and eliminate part of the radiation error manual data collection and statistical analysis, then normalized processing, the conclusion is obtained.

Terrestrial aerosol retrieval over Beijing from Chinese GF-1 data based on the blue/red correlation.

ABSTRACT Wide field imager (WFV) cameras are Chinese high-resolution optical sensors in the GF-1 satellite, and the data from WFV cameras have been applied to environmental monitoring. In this paper, for wide swath observation of WFV, the blue/red correlation is more suitable for retrieving Aerosol Optical Depth (AOD). the blue/red correlation of surface reflectance was analysed by Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products for each pixel over the Beijing area. A significant linear relationship was found between red and blue bands in most of the area, corresponding to urban, farmland, forest, and water areas. Assisted by geographic blue/red correlation, the aerosol inversion method for WFV data was developed. After selecting the data between July 2016 and August 2017, the AOD was obtained using our method and was compared with the ground results from three Aerosol Robotic Network (AERONET) Beijing sites. The results showed that the correlation coefficient between the AOD of our method and that of the ground measurements was about 0.9, and approximately 53% of the points were within the expected error (0.05 ± 0.20τ), and the results are better than MODIS 10 km aerosol product.

Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data.

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.

Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

A New Quick View Retrieval Method for GF-6 Remote Sensing Images

As of November 3, 2019, all seven satellites for project of the high-resolution earth observation system have been launched. We have ushered in an era of massive remote sensing data. When we do large-scale forest land mapping, such as national and provincial scale, we need to retrieve images that meet the image quality requirements, are as cloudless as possible, cover the research area, and have as few images as possible. The conventional practice is that the application unit searches the web page provided by the remote sensing data provider for the remote sensing data it needs, and orders to obtain the data. It is often time-consuming and laborious to query data that meet the needs. This paper puts forward a new selection process method of GF-6, GF-1 and GF-2 satellite data. This is a step-by-step method. First, the cloud coverage, time and area are selected according to the metadatas provided by the data provider to meet the requirements. Then, according to the image quality and cloud amount set by the program, it is selected again. Finally, in this image set, the final selected image is given according to the requirements of maximum coverage of the research area and minimum overlap of the image itself. The method can greatly save the time for manually selecting images and improve the working efficiency. This paper designs a program, which can be used conveniently by users. The paper introduces the idea of program design in detail.

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data.

Sharpening the multispectral GF-2 imagery using the modified intensity-hue-saturation approach: the different spectral settings in comparison

The modified intensity-hue-saturation (mIHS) is one of the widely applied pansharpening approaches on remote sensing imagery. In this work, we try to compare the pansharpened images from the mIHS approach with the different spectral settings applied to sharpen GF-2 multispectral images. Visual inspection and quantitative assessments indicated that the mIHS with the SPOT 5 spectral settings was not suitable for sharpening the GF-2 multispectral imagery. There was no evidence to suggest that the pansharpened image with the GF-2 spectral settings performed over the other four kinds of spectral settings. The mIHS with IKONOS spectral settings was recommend for applications if there were not spectral settings for GF-2 data.

STUDY ON THE WATER BODY EXTRACTION USING GF-1 DATA BASED ON ADABOOST INTEGRATED LEARNING ALGORITHM

Abstract. Surface water system is an important part of global ecosystem, and the changes in surface water may lead to disasters, such as drought, waterlog, and water-borne diseases. The rapid development of remote sensing technology has supplied better strategies for water bodies extraction and further monitoring. In this study, AdaBoost and Random Forest (RF), two typical algorithms in integrated learning, were applied to extract water bodies in Chaozhou area (mainly located in Guangzhou Province, China) based on GF-1 data, and the Decision Tree (DT) was used for comparative tests to comprehensively evaluate the performance of classification algorithms listed above for surface water body extraction. The results showed that: (1) Compared with visual interpretation, AdaBoost performed better than RF in the extraction of several typical water bodies, such as rivers, lakes and ponds Moreover, the water extraction results of the strong classifiers using AdaBoost or RF were better than the weak basic classifiers. (2) For the quantitative accuracy statistics, the overall accuracy (96.5%) and kappa coefficient (93%) using AdaBoost exceeded those using RF (5.3% and 10.6%), respectively. The classification time of AdaBoost increased by 403 seconds and 918 seconds relative to RF and DT methods. However, in terms of visual interpretation, quantitative statistical accuracy and classification time, AdaBoost algorithm was more suitable for the water body extraction. (3) For the sample proportion comparison experiment of AdaBoost, four sampling proportions (0.1%, 0.2%, 1% and 2%) were chosen and 0.1% sampling proportion reached the optimum classification accuracy (93.9%) and kappa coefficient (87.8%).

Inversion of aboveground biomass of Pinus tabuliformis plantations based on GF-2 data

Pinus tabuliformis is an important afforestation species in the Loess Plateau. Quick and accurate estimation of aboveground biomass (AGB) of P. tabuliformis plantations plays an important role in monitoring regional forest resources. Here, we used multi-spectral remote sensing data of domestic satellite GF-2 and the field data to estimate the aboveground biomass of P. tabuliformis plantations in Shibao forest farm of Huanglong Mountain in Shaanxi Province. We calculated eight texture features and five vegetation indices, and then built models based four texture windows (3×3, 5×5, 7×7, 9×9) by using five regression methods including normal regression, stepwise regression, ridge regression, Lasso regression and principal component regression. We used the leave-one-out cross validation (LOOCV) to test the estimation accuracy of each model. We found serious multi-collinearity relationships between the extracted remote sensing factors. Most of the remote sensing factors had significant correlations with aboveground biomass of P. tabuliformis plantations. GF-2 data could achieve higher accuracy in the inversion of aboveground biomass of P. tabuliformis plantations in the Shibao forest farm. The best estimation result was the principal component regression model using 9×9 texture window, and the worst one was the normal regression model using 3×3 texture window. Inversion of aboveground biomass of P. tabuliformis plantation using domestic high-resolution satellite imagery could provide a scientific basis for forestry biomass monitoring, resource management, and sustainable management in the forestry departments of northwest China.