Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data

Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed 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 pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests

Contrasting response mechanisms and ecological stress of net primary productivity in sub-humid to arid transition regions: a case study from the Loess Plateau, China

With the intensification of global change, the overall functions and structures of ecosystems in sub-humid to arid transition regions have changed to varying degrees. The Loess Plateau, as a typical case of such regions, plays a great role in the study of net primary productivity (NPP) for estimating the sustainability of the Earth’s carrying capacity in terrestrial ecosystem process monitoring. In the research on contrasting response mechanisms and ecological stress of NPP, the main innovations of this study are as follows. On the basis of the indicator system constructed from satellite imagery and meteorological data, we introduced deep multiple regressive models to reveal the relationship between NPP and the identified driving factors and then creatively proposed ecological stress (ES) evaluation models from the perspective of vegetation productivity. The findings are as follows: 1) From 2000 to 2019, the changes in driving factors presented a clear regional character, and the annual NPP maintained a fluctuating increasing trend (with a value of 4.57 g·m2·a−1). From the perspective of spatial distribution, the growth rate of NPP gradually increased from arid to sub-humid regions. 2) The effects of different driving factors on NPP changes and specific NPPs varied greatly across different regions. Arid and semi-arid regions were mainly controlled by precipitation (20.49%), temperature (15.21%), and other related factors, whereas sub-humid regions were mainly controlled by solar radiation, such as net surface solar radiation (NSSR) (8.71%) and surface effective radiation (SER) (7.93%). The main driving factors of NPP change varied under different soil conditions. 3) The spatio-temporal patterns of NPP approximated those of ES, but the effects of the latter significantly differed across ecological functional regions and land uses. This research on the Loess Plateau can serve as a valuable reference for future research on realizing ecosystem restoration and protection in sub-humid to arid transition regions

Analysis of Spatial-Temporal Variation of Agricultural Drought and Its Response to ENSO over the Past 30 Years in the Huang-Huai-Hai Region, China

This study constructed a time series of the seasonal Temperature Vegetation Dryness Index (TVDI) based on a remotely sensed dataset from the National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Earth Observing System/Moderate Resolution Imaging Spectroradiometer (EOS/MODIS). We examined the spatiotemporal variation in drought in the Huang-Huai-Hai region of China during the period from 1981 to 2011. Combined with the El Niño and southern oscillation (ENSO) indicator (i.e., the Sea Surface Temperature Anomaly, SSTA of the El Niño 3.4 area), the spatial and temporal relationship of agricultural drought in this region and ENSO was analyzed. The results showed that drought demonstrated a significant downward trend (95% confidence level) which covered 38.01 ~ 55.13% of the farmland in this region. In addition, the largest area of drought reducing appeared in winter. The significant decreasing tendency of agricultural drought started from the late 20th and early 21st centuries, whose variation cycles were mainly between 2.5 to 5 a (year). TVDI series were closely correlated to the ENSO index sequences at the 2.5 to 7 a cycle, and there was a delay from 0.16 to 1.40 a between them. However, the correlation between TVDI and ENSO index series was less. These findings show that there is a relationship between the spatiotemporal changes of agricultural drought in the Huang-Huai-Hai region of China and ENSO events over the recent 30 years

Estimation of total suspended solids concentration by hyperspectral remote sensing in Liaodong Bay

1137-1144Present study consists the potential of the satellite hyperspectral data — Hyperion image for mapping total suspended solids (TSS) concentration of coastal water in Liaodong Bay, China. After processing and atmospheric correction, the reflectance of water extracted from Hyperion image can be used to express the spectral characteristics of different TSS concentration. Estimated algorithms of TSS concentration based on water reflective spectra data collected in situ. The results indicated near infrared wavelength had better correlation with TSS concentration. Exponential algorithm was found to have better accuracy in estimate the concentration less than 200mg l-1 and linear algorithm was suited for the concentration range in 200–500 mg l-1 and logarithm algorithm can better describe the correlation between the reflectance and concentration range in 500–1000 mg l-1

A Feature Fusion Airport Detection Method Based on the Whole Scene Multispectral Remote Sensing Images

Being one of the most important infrastructures, airports play a vital role in both civil fields and military fields. However, detect airports directly based on the whole scene remote sensing images (RSIs) with complex background remains challenging. To address this issue, this article proposes a method that mainly combines spectral features and geometric features of airports with concrete runways to detect multiple airports simultaneously from a whole scene multispectral image with medium-high spatial resolution and with comparatively few bands (contains blue, green, red, and near-infrared bands). Specifically, a decision tree algorithm was developed based on the analysis of spectral features to extract main concrete areas within the whole RSI. Then, the geometric features are used to aim at extracting the point marks of candidate airports. The influence of different image spatial resolutions of the proposed method is explored and the detection effect and processing efficiency of proposed method is verified based on whole scene RSIs with complex background. The analysis of experimental results shows that Sentinel-2 images is more suitable for airport detection than Gaofen-6 and Landsat-8 images based on the proposed method. In addition, the proposed method provides high-accuracy detection of category Ⅳ airports based on Sentinel-2 images with different background complexity in experimental areas indicate the proposed method has a high robust and a good applicability. Finally, run-time test of the proposed method was conducted, and it demonstrates the proposed method has the higher processing efficiency when applying to regional airport detection

Deep Learning Application for Crop Classification via Multi-Temporal Remote Sensing Images

The combination of multi-temporal images and deep learning is an efficient way to obtain accurate crop distributions and so has drawn increasing attention. However, few studies have compared deep learning models with different architectures, so it remains unclear how a deep learning model should be selected for multi-temporal crop classification, and the best possible accuracy is. To address this issue, the present work compares and analyzes a crop classification application based on deep learning models and different time-series data to exploit the possibility of improving crop classification accuracy. Using Multi-temporal Sentinel-2 images as source data, time-series classification datasets are constructed based on vegetation indexes (VIs) and spectral stacking, respectively, following which we compare and evaluate the crop classification application based on time-series datasets and five deep learning architectures: (1) one-dimensional convolutional neural networks (1D-CNNs), (2) long short-term memory (LSTM), (3) two-dimensional-CNNs (2D-CNNs), (4) three-dimensional-CNNs (3D-CNNs), and (5) two-dimensional convolutional LSTM (ConvLSTM2D). The results show that the accuracy of both 1D-CNN (92.5%) and LSTM (93.25%) is higher than that of random forest (~ 91%) when using a single temporal feature as input. The 2D-CNN model integrates temporal and spatial information and is slightly more accurate (94.76%), but fails to fully utilize its multi-spectral features. The accuracy of 1D-CNN and LSTM models integrated with temporal and multi-spectral features is 96.94% and 96.84%, respectively. However, neither model can extract spatial information. The accuracy of 3D-CNN and ConvLSTM2D models is 97.43% and 97.25%, respectively. The experimental results show limited accuracy for crop classification based on single temporal features, whereas the combination of temporal features with multi-spectral or spatial information significantly improves classification accuracy. The 3D-CNN and ConvLSTM2D models are thus the best deep learning architectures for multi-temporal crop classification. However, the ConvLSTM architecture combining recurrent neural networks and CNNs should be further developed for multi-temporal image crop classification

AN IMPROVED ENDMEMBER EXTRACTION ALGORITHM BY INVERSING LINEAR MIXING MODEL

In hyperspectral imagery there are some cases when no pure pixels present due to the limitation of the sensors ’ space resolution and the complexity of the ground components, and then the endmembers extracted by traditional algorithms are usually mixing ones still. In order to solve this problem, this paper proposes an endmember extraction algorithm based on the re-analysis of preliminary endmembers extracted by volume calculating concept under the linear mixing model. After extracting the pixels which are most approximated to the pure pixels from the image, using the convex polyhedron’s geometric characters to search out the boundary pixels which are around the preliminary endmembers and on the edge of the convex polyhedron formed by the pure pixels. Calculating the abundance fractions of every endmember in these pixels by the laws of sins, thus, with these coefficients the endmembers could be obtained using the inversion of linear mixing model. Hyperspectral scenes are simulated by the real spectra to investigate the performance of the algorithm. Preliminary results indicate the effectiveness of the algorithm. Applying the algorithm to a real Hyperion scene it also gets a better result. 1

A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data

Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m3, respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm