South China Tropical Forest Changes in Response to Economic Development and Protection Policies

The destruction of tropical forests continues to attract attention from the international community. China’s National Forest Administration has adopted protective measures for tropical forests, and efforts have been developed to balance forest protection and economic development in Hainan Island, China. However, the response of natural tropical forest to local economic development and the effectiveness of forest management and protection policies remain unclear because of complexity of tropical evergreen ecosystems. After comprehensive analysis of spectral characteristics, spatial distribution, patch shape, and other characteristics of main forests, we developed an information extraction method based on the decision tree method, combining digital elevation model (DEM) and forest planning maps, and established flowcharts and processes for sophisticated object-based information extraction. The accuracy of our method was 92%, and the method proved to be applicable and effective in the classification of complex surface features in a tropical evergreen ecosystem. Forces resulting in the change of these forests were explored by analyzing the relationships between economic development, protection policies, as well as environmental factors

Polarization Remote Sensing for Land Observation

In the real world, vegetation, liquid surfaces, rocks, buildings, snows, clouds, fogs, etc. can all be regarded as natural polarizers. In the process of reflecting, transmitting, and scattering of electromagnetic radiations, land surface objects can produce polarized features that are related to the nature of the materials. These polarized information can determine objects’ properties, and therefore, detecting the polarization information of objects becomes a new method of remote sensing. Polarization of reflected and scattered solar electromagnetic radiation adds a new dimension to the understanding of the Earth’s objects’ properties. The polarized bidirectional reflectance characteristics and polarized hyperspectral properties of land objects were methodically studied. The results of the polarized bidirectional reflectance characteristics can provide the theoretical basis for polarization remote sensing such as the detecting conditions, modeling and others. The polarized spectral property of the typical objects can be used as the spectral basis for polarization remote sensing. The atmospheric correction is a key problem when using polarization remote sensing method to detect land objects’ information, because scattered atmospheric particles exhibit stronger polarization phenomena than land objects do. A method of using atmospheric neutral point for the separation polarization effect between objects and atmosphere has been proposed

Laboratory Calibration of a Field Imaging Spectrometer System

A new Field Imaging Spectrometer System (FISS) based on a cooling area CCD was developed. This paper describes the imaging principle, structural design, and main parameters of the FISS sensor. The FISS was spectrally calibrated with a double grating monochromator to determine the center wavelength and FWHM of each band. Calibration results showed that the spectral range of the FISS system is 437–902 nm, the number of channels is 344 and the spectral resolution of each channel is better than 5 nm. An integrating sphere was used to achieve absolute radiometric calibration of the FISS with less than 5% calibration error for each band. There are 215 channels with signal to noise ratios (SNRs) greater than 500 (62.5% of the bands). The results demonstrated that the FISS has achieved high performance that assures the feasibility of its practical use in various fields

Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm

Evacuation routes are necessary to guide people to avoid hazardous and dangerous zones and to prevent the loss of human lives, especially in the event of volcanic eruptions. This article attempts to examine the evacuation routes of the sister village program by using the ant colony optimization (ACO) algorithm. Model simulations and calculations of the ACO algorithm were done by aggregation of the five determined parameters including distance, speed, hurdle, density, and secure point. The validation of the model was carried out by the examination of the five most prone disaster villages located in Mount Merapi that are interconnected as sister villages in Sleman Regency, Yogyakarta Province, and Indonesia. This research is important to ensure that the sister village evacuation system is effective in reducing the impact of the risks posed by the eruption of Mount Merapi. Based on the results, sister village evacuation systems are proven to be the fastest and safest routes by the ACO examinatio

An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China

Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales

An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China

Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales

Exploring the Potential of Spectral Classification in Estimation of Soil Contaminant Elements

Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security. Visible and near-infrared reflectance spectroscopy (VNIRS) has been widely used in soil science, due to its rapidity and convenience. With different spectrally active soil characteristics, soil reflectance spectra exhibit distinctive curve forms, which may limit the application of VNIRS in estimating contaminant elements in soil. Consequently, spectral clustering was applied to explore the potential of classification in estimating soil contaminant elements. Spectral clustering based on different distance measure methods and elements with different contamination levels were exploited. In this study, soil samples were collected from Hunan Province, China and 74 reflectance spectra of air-dried soil samples over 350–2500 nm were used to predict nickel (Ni) and zinc (Zn) concentrations. Spectral clustering was achieved by K-means clustering based on squared Euclidean distance and Cosine of spectral angle, respectively. The prediction model was calibrated with the combination of Genetic algorithm and partial least squares regression (GA-PLSR). The prediction accuracy shows that the prediction of Ni and Zn concentrations in soil was improved to different extents by the two clustering methods and the clustering based on squared Euclidean distance had better performance over the clustering relied on Cosine of the spectral angle. The result reveals the potential of spectral classification in predicting soil Ni and Zn concentrations. A selected subset of the 74 soil spectra was used to further explore the potential of spectral classification in estimating Zn concentrations. The prediction was dramatically improved by clustering based on squared Euclidean distance. Additionally, analysis on distance measure methods indicates that Euclidean distance is more suitable to describe the difference between the collected soil reflectance spectra, which brought the better performance of the clustering based on squared Euclidean distance

Radiometric Calibration of GF5-02 Advanced Hyperspectral Imager Based on RadCalNet Baotou Site

In this study, an on-orbit radiometric calibration campaign of the GF5-02 AHSI was performed at the RadCalNet Baotou site, based on the automated observation of reflectance and atmospheric parameters of a 300 m × 300 m homogeneous desert area. The consistency of the radiometric calibration coefficients was validated both at the Dunhuang calibration site and the Baotou site. The average relative difference between the calibrated top-of-atmospheric (TOA) radiance and the predicted TOA radiance were less than 7%. The R2 of these two TOA radiances were all higher than 0.99. These results showed that the accuracy of calibration coefficients could meet the requirements of hyperspectral quantification applications. The uncertainty of GF5-02 AHSI radiometric calibration was 6.18%. This study also demonstrated that automated observation data of the Baotou site were reliable for high-frequency radiometric calibration and radiometric performance monitoring of GF5-02 AHSI

Extraction of multiple cropping information at the Sub-pixel scale based on phenology and MODIS NDVI time-series: a case study in Henan Province, China

Understanding and mapping multiple cropping patterns(MCP) is important to meet global food demand. Due to the random crops in smallholder farming, it is challenging to obtain phenology and location of MCP. Phenology exhibits stable rhythmic variation, and vegetation index(VI) forms time-series profiles reflecting rhythmic variations. Since most crops have different growth cycles, time-series are used as a fingerprint to distinguish multiple crops in a specific area. Moderate Resolution Imaging Spectroradiometer(MODIS) normalized-VI(NDVI) data were used to construct time-series, but different land cover types were often mixed in a 250 m MODIS pixel. Time-series were explored as input of spectral unmixing algorithms that could capture the spatial distribution of MCP and subpixels at large areas. Time-series profiles of MCP were extracted based on N-finder algorithm(N-FINDR). Considering the mixed-pixel, fully constrained least-squares(FCLS) was used to extract the spatial distribution of MCP. The accuracy of the algorithm was verified using high spatial resolution images with an overall accuracy of 85.65%. The results show that the algorithm simultaneously extracts MCP and spatial distributions at large scales

Research and application of multi-angle polarization characteristics of water body mirror reflection

On the basis of the multi-angle polarized reflection spectrum of the water samples, the water body mirror reflection polarization characteristics and mechanism are described systematically. By altering such influential factors as the angle of incidence, detecting angle, detecting azimuth angle and polarization angle, ubiquitous laws for the multi-angle polarized reflection spectrum of the water samples are obtained. Combining multi-angle remote sensing with polarized light, the multi-angle polarized reflection method about eliminating the water body mirror reflection and the suitable time of the polarized remote sensing of the water body are proposed. This study provides technical references for the application of multi-angle polarization technology on water body remote sensing.Geosciences, MultidisciplinarySCI(E)EI0ARTICLE6946-9525