P-band PolInSAR has the potential to map forest height and biomass at the global scale with the upcoming BIOMASS mission. However, because of the strong penetration of P-band and temporal decorrelation of repeat-pass observations, volume temporal decorrelation (γVT), ground temporal decorrelation (γGT), and residual ground scattering (mmin) largely influence forest height inversion. By integrating the random volume over ground (RVoG) model and Sum of Kronecker Products (SKP) decomposition, this study proposed a multi-baseline forest height inversion method to remove the joint influence of these problems. The theoretical simulation and empirical experiments using airborne P-band PolInSAR data in tropical forests, Nouragues, explicitly explored how each of the three problems affects the inversion. γVTandmmingenerally affect forest height inversion more than γGT, and the concurrence of them brought severe overestimation to RVoG inversions (RMSE ranges from 7.6 to 18.8 m). Stepwise comparisons show that compensating for each of γGT, γVT, andmmincould improve the inversion accuracy further. The proposed multi-baseline inversion simultaneously solved all three problems and produced the best accuracy (RMSE of 3.4 m), and it has a stable performance for another two more different multi-baseline datasets, producing similar inversion accuracies (RMSE of 3.3 and 3.6 m). The additional experiment using BIOSAR 2007 datasets with varied temporal baselines of 0 day, 30 days, and 56 days demonstrated that the proposed method has stability against temporal decorrelation. Consequently, the proposed method improved both the accuracy and robustness of forest height inversion.

Forest biomass is a complex parameter that is a function of multiple forest structure parameters, such as forest height, DBH, and woody density. At present, forest height is most likely to be estimated over the globe from spaceborne remote sensing, particularly with the upcoming BIOMASS mission. This study inverted forest height using P-band repeat-pass multi-baseline PolInSAR data and evaluated the robustness across three different forest sites. Afterward, the PolInSAR height was converted into forest AGB. Results show that the method produced a more accurate forest height in tropic forests than that in boreal forests, but there is little bias for forest height estimates across three sites, with$\mathrm{R}^{2}$of 0.86 and RMSE of 3.1m in contrast to LiDAR forest height. One power equation was found able to convert PolInSAR forest height into forest AGB across three sites, producing$\mathrm{R}^{2}$of 0.85 and RMSE of 51.8 tons/ha using LOO cross-validation, which is at a similar level to the AGB accuracy estimated using LiDAR-derived forest height$(\mathrm{R}^{2}$of 0.87 and RMSE of 47.9 tons/ha).

Temporal decorrelation is a critical issue to be addressed for forest height inversion using repeat-pass Polarimetric SAR Interferometry (PolInSAR) data. Temporal decorrelation plus the residual ground contribution will bring a 2-D ambiguity to volume-only coherence, which makes the inversion underdetermined. This study utilizes the overlapped area of the ambiguities from different baselines to reduce the ambiguous region and to more accurately retrieve the volume-only coherence and forest height. The method was validated and compared with two commonly used single-baseline inversions, moreover, its robustness was evaluated across two tropic forest sites, Paracou and Nouragues, French Guiana. Results show that the presented multi-baseline inversion scheme successfully reduced both the influence of temporal decorrelation and ground contribution, and performed better than any of the single baseline used. For Paracou, R2 was improved from 0.35 to 0.42, RMSE decreased from 4.7 to 2.7m, and ubRMSE decreased from 3.0 to 2.7m; for Nouragues, R2 was improved from 0.43 to 0.56, RMSE decreased from 5.8 to 5.7m, and ubRMSE decreased from 5.6 to 5.0m, at a fine validation scale of ca. 20×30m.

P-band PolInSAR is potential to map forest height and biomass at the global scale with the upcoming BIOMASS mission. However, there are three major problems in forest height inversion using P-band repeat-pass PolInSAR data, i.e. volume temporal decorrelation, residual ground scattering, and ground temporal decorrelation. Based on the random volume over ground (RVoG) model and the Sum of Kronecker Products (SKP) decomposition, this study developed a multi-baseline forest height inversion method to remove the joint influence of these three problems. Based on the TropiSAR 2009 datasets in Nouragues, results show that the concurrence of the three problems brought severe overestimation to three-stage inversion (RMSE of 7.6-18.8 m for different baselines). Stepwise comparisons show that each compensation of the three problems improved the inversion accuracy. The proposed multi-baseline inversion simultaneously removed all three problems and produced the best accuracy for both short (20 m) forest (RMSE 3.4 m), which is much better than the inversion accuracy of the used single baselines.

Most of the current optical-based forest AGB estimation methods only use spatial information of reflectance, making estimates susceptible to saturation and noise. We introduced dynamic time-series information on forest reflectance into AGB estimation and explored its ability to improve estimation accuracy. The experiments were conducted based on 10304 in situ AGB plots and corresponding Landsat 5/7/8 data from 1984 to 2019 in Australia, using a random forest algorithm for AGB estimation. Results show that time-series modeling of reflectance could remove most of the noise in observed reflectance, and improved the correlation between reflectance and AGB. After introducing the time-series parameters into AGB estimation, estimation accuracy was significantly improved, with R2 increased by 0.08 and RMSE reduced by 7.5 tons/ha.

PolInSAR technique has been proved effective in mapping forest height. However, for P band repeat-pass PolInSAR data, the influence of residual ground scattering contribution and temporal decorrelation are non-negligible for forest height inversion. To resolve these two problems, we developed a dual-baseline inversion method to reduce the influence of ground scattering contribution, and then cooperated it with Random-Motion-over-Ground (RMoG) model to compensate for the influence of temporal decorrelation. The Three-stage inversion showed a severe overestimation for the whole image, producing R2of ca. 0.4 and RMSE of ca. 7m. For near range (with$k_{z}$larger than 0.1rad/m), the overestimation was mainly caused by ground scattering contribution, which was effectively solved by using fixed extinction method. For far range (with$k_{z}$smaller than 0.06rad/m), the influence of ground scattering contribution seemed decreased because the fixed extinction method did not reduce the overestimation, and overestimation was mainly caused by volume temporal decorrelation. Besides, the influence of ground scattering contribution on forest height inversion seemed greater than that of temporal decorrelation. The proposed method successfully solved both of the two influences, and reduced the overestimation for both near and far range, producing the best accuracy with R2 of 0.62 and RMSE of 3.73m.

In this study, the burn severity of several wildfires ignited at northern Australian tropical savannas area were estimated using the Forest Reflectance and Transmittance (FRT) radiative transfer model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) satellite data. To alleviate the spectral confusion between severe (SV) and not-severe (NSV) burnt levels caused by sparse tree distribution, the MODIS Vegetation Continuous Fields (VCF) tree cover percentage data was used to constrain the inversion. The results showed that the accuracy of burn severity estimation significantly improves when considering the tree coverage, with overall accuracy for two study sites increasing from 65% to 81% and kappa coefficient from 0.35 to 0.55. Future work will focus on extending the methodology to other ecosystems.

Nonvolume decorrelation (γNonvol) united with the unknown ground contribution will bring a 2-D ambiguity to volume-only coherence, making the inversion underdetermined even when multiple baselines are available. In the context of random volume over ground (RVoG) model and three-stage algorithm, this paper theoretically presented the varied response of different baselines to both γNonvoland ground contribution, and then proposed a new multi-baseline inversion method to reduce the 2-D ambiguity. The proposed method includes two steps, calculating the common overlapped ambiguity from different baselines and fixing the extinction coefficient, to more accurately retrieve the volume-only coherence and forest height. It makes no assumptions on γNonvoland ground contribution. The method was validated and compared with three single-baseline inversions and two published multi-baseline inversions by using the airborne P-band polarimetric SAR interferometry (PolInSAR) data and the reference data of LiDAR canopy height model (CHM) over a dense rainforest site. Results showed that the developed multi-baseline method successfully reduced the combined influence of both γNonvoland ground contribution, and performed better than any single baseline, improving the R2from 0.60 to 0.77 and unbiased root-mean-square error (RMSE) from 1.32 to 1.04 m at the scale of ca. 100 × 140 m2. Moreover, the multi-baseline scheme is relatively robust among different baseline combinations.

Understanding the dynamics of vegetation activity and its interaction with climate factors becomes an important issue in global change research. In this study, using satellite-derived normalized difference vegetation index (NDVI) data, we analyzed changes in vegetation growth at the region scale from 2000 to 2013 in Sichuan Province, China. And then explored the relationship between NDVI trends and the temperature and precipitation. The results showed: (1) The NDVI of the vegetated area in Sichuan was decreasing with a trend about −2.3 ×10−3NDVI.yr−1. About 2/3 of the vegetated area showed a negative trend of annual mean NDVI. (2) In larger areas vegetation growth was controlled by temperature. (3) Monthly NDVI was significantly correlated with the preceding month's temperature and precipitation. (4) The spatial pattern of relationship between NDVI and climatic factors was likely correlated with terrain and vegetation type.

Grassland, which has a great significance to human society, is susceptible to the drought. An efficient way to monitor the drought scale of grassland is imperative. In this paper, we proposed a new drought index named Grassland Drought Index (GDI) to monitor the drought conditions of the grassland ecosystem. The GDI was constructed by comprehensively integrating the soil moisture content, canopy water content (CWC) and precipitation together with different weights. The soil moisture with 1km spatial resolution was estimated by downscaling the AMSR-E soil moisture from 25km to 1km. The CWC was retrieved by the 1km MODIS products based on the PROSAIL model. And the precipitation was acquired by CRU data. Besides, the global distribution product of GDI in July 2010 was produced. By validation, the correlation coefficient R between the scaled GDI and the Standard Precipitation Index (SPI) was 0.6109, and the root-mean-square error (RMSE) was 0.7774.

Under the indoor building environment, when the fires and other accidents occur, how to effectively organize the masses evacuation and fire rescue, is closely related to the safety of people's lives and property and has become a critical problem of public concern. This paper presents an improved ant colony algorithm (ACO) to solve the problem of how to optimize the evacuation route and rescue route when an accident occurs. According to the key factors affecting people emergency evacuation, such as indoor building environment, fire and its combustion products, problem of path's optimal selection, etc., we propose an emergency evacuation model, based on the model it can give an optimal evacuation route for the mass and an optimal rescue route for the firefighters. We also analyzes the search results, it shows that the search results is robust and reasonable.

Two-dimensional GIS are extensively applied to mineral potential mapping on a regional scale. However, these systems are unable to represent underground geological information in three spatial dimensions. The objective of this article is to overcome this defect and to develop a prototype system to qualitatively and quantitatively analyze underground mineral resources on the local scale, based on spatial data mining methods and three-dimensional GIS. The presented approach is based on 3D geological model established by the three-dimensional geological modeling software, such as Micromine, and is characterized by three-dimensional visualization, data management, and functionality for mineral resources prospectivity and quantitative assessment integrating weights-of-evidence model and case-based reasoning. The resulting system enables geologists to more accurately analyze the prospectivity and reserves of unknown mineral resource on the local scale.

The retrieval of parameters through a physical mechanism model is promising for its generality but is challenged by the ill-posed inversion problem. This study focused on the use of multiple priori information to alleviate the ill-posed inversion problem. The priori information included the products of satellite images, the correlations among model free parameters, field survey, and the achievements of previous studies. However, the priori information was of uncertainty, which was described by multi-variables probability distribution in this study. A Bayesian network algorithm was used to retrieve the canopy water content (CWC) by calculating the posterior probability distribution of CWC based on the priori information, the HJ-1B product, and the PROSAIL model. The retrieval results showed that the R2 = 0.83 and RMSE = 0.18 compared to the field measured CWC, which confirmed the feasibility to alleviate the ill-posed inversion problem by the multiple priori information.