Hongli Ge

Pixel-based Minnaert Correction Method for Reducing Topographic Effects on a Landsat 7 ETM+ Image

The topographic effect on land surface reflectance is an important factor affecting quantitative analysis of remotely sensed data in mountainous regions. Different approaches have been developed to reduce topographical effects. Of the many methods, the Minnaert correction method is most frequently used for topographic correction, but a single global Minnaert value used in previous research cannot effectively reduce topographic effects on the remotely sensed data, especially in the areas with steep slopes. This paper explores the method to develop a pixel-based Minnaert coefficient image based on the established relationship between Minnaert coefficients and topographic slopes. A texture measure based on homogeneity is used to eva-luate the topographic correction result. This study has demonstrated promising in

Spatial heterogeneity and carbon contribution of aboveground biomass of moso bamboo by using geostatistical theory

Moso bamboo extensively distributes in southeast and south Asia, and plays an important role in global carbon budget. However, its spatial distribution and heterogeneity are poorly understood. This research uses geostatistics theory to examine the spatial heterogeneity of aboveground biomass (AGB) of moso bamboo, and uses a point kriging interpolation method to estimate and map its spatial distribution. Results showed that (1) spatial heterogeneity and spatial pattern of moso bamboo’s AGB can be revealed by an exponential semivariance model. The analysis of the model structure indicating that the AGB spatial heterogeneity is mainly composed of spatial autocorrelation components, and spatial autocorrelation range is from 360 to 41,220xa0m; (2) kriging standard deviation map showing the level of the model errors indicates that the AGB spatial distribution by point kriging interpolation method is reliable; (3) the average AGB of moso bamboo in Anji County is 44.228xa0Mgxa0hm−2, and carbon density is 20.297xa0Mgxa0Cxa0hm−2. The total AGB of moso bamboo accounts for 16.97% of the total forest-stand biomass in Zhejiang province. The total carbon storage of moso bamboo in China is 68.3993xa0Tgxa0C, accounting for 1.6286% of the total forest carbon storage. This implies the important contribution of moso bamboo in regional or national carbon budget.

Estimation of aboveground carbon stock of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest with a Landsat Thematic Mapper image

The extensive distribution of bamboo forests in South and Southeast Asia plays an important role in the global carbon budget. It is an urgent task to accurately and in good time estimate carbon stock within these areas. In this study, linear regression, partial least-squares (PLS) regression and backpropagation artificial neural network (BP-ANN) with a Gaussian error function as the activation function of the hidden layers (Erf-BP) were used to estimate aboveground carbon (AGC) stock of Moso bamboo in Anji, Zhejiang Province, China. Based on the combined use of Landsat Thematic Mapper (TM) and field measurements, the results indicate that the Erf-BP model provided the best estimation performance, and the linear regression model performed the poorest. This study indicates that remote sensing is an effective way of estimating AGC of Moso bamboo in a large area.

Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique

This article explores a non-linear partial least square (NLPLS) regression method for bamboo forest carbon stock estimation based on Landsat Thematic Mapper (TM) data. Two schemes, leave-one-out (LOO) cross validation (scheme 1) and split sample validation (scheme 2), are used to build models. For each scheme, the NLPLS model is compared to a linear partial least square (LPLS) regression model and multivariant linear model based on ordinary least square (LOLS). This research indicates that an optimized NLPLS regression mode can substantially improve the estimation accuracy of Moso bamboo (Phyllostachys heterocycla var. pubescens) carbon stock, and it provides a new method for estimating biophysical variables by using remotely sensed data.

Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping

This study proposed a multi-scale, object-based classification analysis of SPOT-5 imagery to map Moso bamboo forest. A three-level hierarchical network of image objects was developed through multi-scale segmentation. By combining spectral and textural properties, both the classification tree and nearest neighbour classifiers were used to classify the image objects at Level 2 in the three-level object hierarchy. The feature selection results showed that most of the object features were related to the spectral properties for both the classification tree and nearest neighbour classifiers. Contextual information characterized by the composition of classified image objects using the class-related features assisted the detection of shadow areas at Levels 1 and 3. Better classification results were achieved using the nearest neighbour algorithm, with both the producer’s and user’s accuracy higher than 90% for Moso bamboo and an overall accuracy of over 85%. The object-based approach toward incorporating textural and contextual information in classification sequence at various scales shows promise in the analysis of forest ecosystems of a complex nature.

Retrieval of Canopy Closure and LAI of Moso Bamboo Forest Using Spectral Mixture Analysis Based on Real Scenario Simulation

This paper investigates the retrievals of the canopy closure and leaf area index (LAI) of the Moso bamboo forest from the Landsat Thematic Mapper data using a constrained linear spectral unmixing method. A new approach for endmember collection based on the real scenario simulation of the Moso bamboo forest is developed. Four fraction images (i.e., sunlit canopy, shaded canopy, sunlit background, and shaded background) are calculated and used to develop the canopy closure and LAI. The results show that the predicted crown closure, which was inverted from the sunlit and shaded canopies, has a good agreement with the observed crown closure (R2 = 0.725). The accuracy assessment indicates that the root mean square error (rmse) and the relative root mean square error (rmse_r) are 10% and 13.37% for the predicted crown closure, respectively. The LAI has the highest correlation coefficient with the shaded background, and it can be fitted by an exponential model (R2 = 0.497). The linear relationship between the predicted and observed LAI values is significant at a level of 99% (P <; 0.01 and R2 = 0.459), and the LAI can be predicted by the exponential model.

Exploring the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based approach

This study presents a new method for the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based classification approach. This new method can integrate an object with its super-objects’ metrics. The entire classification involves two object hierarchies: (1) a five-level object hierarchy to extract object metrics at five scales, and (2) a three-level object hierarchy for the classification process. A five-level object hierarchy was developed through multi-scale segmentation to calculate and extract both spectral and textural metrics. Layers representing the hierarchy at each of the five scales were then intersected by using the overlay tool, an intersected layer was created with metrics from all five scales, and the same geometric elements were retained as those of the objects of the lowest level. A decision tree analysis was then used to build rules for the classification of the intersected layer, which subsequently served as the thematic layer in a three-level object hierarchy to identify shadow regions and produce the final map. The use of multi-scale object metrics yielded improved classification results compared with single-scale metrics, which indicates that multi-scale object metrics provide valuable spatial information. This method can fully utilize metrics at multiple scales and shows promise for use in object-based classification approaches.

Integration of Remote Sensing and GIS for Evaluating Soil Erosion Risk in Northwestern Zhejiang, China

Estimation of soil loss or evaluation of soil erosion risk has been an active research topic and has had much attention in the past three decades. The application of Revised Universal Soil Loss Equation (RUSLE) in large areas is a challenge because of data availability and quality. The RUSLE model was used in this article to evaluate soil erosion risk based on soil samples, a soil type map, digital elevation model (DEM) data, and Landsat Thematic Mapper (TM) images. Multiple regression analysis was used in order to identify major factors that influence the risks of soil erosion. A regression model based on DEM-derived slope gradient and TM-derived fractional soil and vegetation images was developed to map soil erosion risk distribution in a forest ecosystem in Zhejiang, China. The method that was developed has the potential to quickly examine the spatial distribution of the risk of soil erosion. This article provides a new insight for the evaluation of soil erosion risks in forest ecosystems with the integration of remote sensing and geographic information systems (GIS).

Detection of Drought-Induced Hickory Disturbances in Western Lin An County, China, Using Multitemporal Landsat Imagery

Hickory plantations play an important role in improving local farmers’ economic conditions, but extreme drought in July–August 2013 seriously influenced hickory nut production. It is necessary to understand the extent and magnitude of this drought-induced hickory disturbance through mapping its spatial distribution using remote sensing data. This paper proposes a new approach to examine hickory disturbance based on multitemporal Landsat imagery. Ratios of green vegetation to soil fractions were calculated, in which the green vegetation and soil fractions were extracted from Landsat multispectral imagery using the linear spectral mixture analysis approach. We used the differences between before-drought and after-drought ratios to detect hickory disturbances. Four disturbance levels—non-disturbance, light, medium, and severe—were grouped according to the field survey data. The spatial distribution of these four levels was developed using the ratio-based approach. The result indicates that this approach is effective to detect drought-induced hickory disturbance and may be transferred to detect other kinds of disturbances, such as forest disease and selective logging. Cautions should be taken to properly select image acquisition dates and the change detection period, in addition to the approach itself.

Synergistic use of Landsat TM and SPOT5 imagery for object-based forest classification

Abstract This study evaluated the synergistic use of Landsat5 TM and SPOT5 images for improving forest classification using an object-based image analysis approach. Three image segmentation schemes were examined: (1) segmentation based on both SPOT5 and Landsat5 TM; (2) segmentation based solely on SPOT5; and (3) segmentation based solely on Landsat5 TM. The optimal scale parameters based on TM/SPOT5 and SPOT5 were determined by measuring the topological similarity between segmented objects and reference objects at 10 different scales. Mean and standard deviation of the pixels within each object in each input layer were the classification metrics. Nearest neighbor classifier was performed for the three segmentation schemes. The results showed that (1) the optimal scales of TM/SPOT5, SPOT5, and TM were 70, 100, and 0.8, respectively and (2) classification results with medium spatial resolution images were not desirable, with overall accuracy of only 72.35%, while synergistic use of Landsat5 TM and SPOT5 greatly improved forest classification accuracy, with overall accuracy of 82.94%.