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This study aimed to propose a new convolutional neural network (CNN)-based model for spatial resolution enhancement of Landsat images without finer resolution bands for training the CNN-based model. Its performance was assessed using Landsat 8 images from eight different regions that were dominated by various topographic features, and land use and land cover types by comparison with bicubic (BIC), nearest neighbor (NN) and Lanczos (LZ) resampling. It was found that for spatial resolution enhancement of 30 m resolution images to 15 m, 10 m and 7.5 m, the proposed model increased the average peak-signal-to-noise-ratio (PSNR) values by 1.7% to 11.5% compared with the compared methods. The PSNR increases were statistically significantly different from zero at the significant level of.05, but the improvement decreased as the spatial resolution of the input images became finer. Moreover, the deeper the CNN model, the better the performance, but after nine layers, the gain of performance slowed down. This indicates that the proposed algorithm is promising for spatial resolution enhancement of optical images without input of finer spatial resolution images. [ABSTRACT FROM AUTHOR]

Developing an individual tree diameter increment (ΔDBH) model is the basis of near-natural management of mixed, uneven-aged oak forests. This analysis used remeasurement data (2009–2014) comprising 6154 observations from 112 permanent plots in central China to develop and compare an indicator variable model (IVM) and a mixed-effect model (MEM) to estimate ΔDBH. First, a basic model was estimated using 12 potential explanatory variables. Geographical regions (GR), competition intensities (CI) and species compositions (SC) were introduced into the basal model as indicator variables or mixed effects, step by step, and then the prediction accuracy of IVM and MEM was compared. The results showed that (1) the independent variables significantly affecting ΔDBH included the reciprocal of DBH, basal area, altitude, and mean annual rainfall; (2) the introducing GR could not improve the accuracy of estimating ΔDBH, but the CI and SC could. (3) Compared with the basic model and IVM, the percentage mean absolute deviation of MEM decreased by 2.07% and 1.11%, while the root mean square error decreased by 0.06 and 0.04, respectively. The MEM including CI and SC as a random effect showed the best predictive performance and can be applied to improve the prediction of individual oak trees ΔDBH. [ABSTRACT FROM AUTHOR]

Moso bamboo is an evergreen plant that extensively distributes in subtropical regions. Comparing to other forest types, Moso bamboo forest has some unique characteristics: high growth rate, short harvesting rotation, and on/off-year phenomenon. Plant phenology plays an important role in regulating carbon sequestration of the bamboo forest ecosystem. However, it is a challenge task to capture the phenological features of Moso bamboo forests on a regional scale due to frequent change of canopy structures and lack of high spatiotemporal remotely sensed data. The Vegetation and Environment monitoring on a New Micro-Satellite (VENµS) data with high spatiotemporal resolution provide the potential to examine the seasonal change of Moso bamboo forests. This research employs the VENµS time-series data (from January 2018 to December 2019) to analyse the spectral characteristics of on-year/off-year Moso bamboo forests and other two evergreen forest types (i.e., broadleaf forest and coniferous forest). The optimal spectral ranges for examining the seasonal variation of bamboo forests were determined. Three red-edge-based vegetation indices were reconstructed using the Harmonic analysis of time series (Hants) and compared. Red-edge position index (REPI) was selected to identify different phenological periods of Moso bamboo forests and other evergreen forest types. The results show that the spectral range of 730–920 nm in the VENμS data is sensitive to seasonal variation of Moso bamboo forests. The REPI can more effectively identify the two-year growing cycle of the bamboo forests than other vegetation indices, especially the bamboo shoots period. The start of the growing season of the off-year bamboo forest is approximately 50 to 60 days earlier than on-year bamboo forest. The results provided time-series phenological datasets of on-year and off-year Moso bamboo forests, which is valuable for local governments to conduct better ecological management and decision-making. [ABSTRACT FROM AUTHOR]

As a cross-disciplinary scientific domain, a great deal of research on landscape geochemistry has been conducted since its establishment. However, the used methods lack the ability of revealing the relationships of landscape geochemistry with other sciences, and the obtained knowledge is thus fragmented and isolated. In this study, the state-of-the-art regarding the applications of relatively new geospatial technologies including fractal theory, geographic information system and remote sensing to landscape geochemistry was reviewed and analyzed to provide deep insights of current research and a roadmap for furthering the development of landscape geochemistry as a cross-disciplinary discipline. The results showed that substantial research on the applications of fractal theory, GIS and RS technologies for analyzing the processes and data of landscape geochemistry has been conducted by using the advantages of the geospatial technologies. However, the great challenges still exist when the geospatial technologies were individually utilized due to the limitations of the technologies themselves and the complexity of landscape geochemistry. In the end, opportunities and challenges for advancing the further studies of landscape geochemistry were discussed in detail and new directions of studying landscape geochemistry using multidisciplinary or integrated approaches to enhance understanding the relationships among the relevant disciplines were suggested. [ABSTRACT FROM AUTHOR]

Quickly and accurately mapping soil properties is critical for agricultural, forestry and environmental management. In this study, a new hyperspectral remote sensing method of soil property prediction was developed and validated in Stipa purpurea dominated alpine grasslands located in Shenzha County of the Qiangtang Plateau, northwestern Qinghai-Tibet Plateau. Hyperspectral data were collected in a total of 67 sample points. At the same time, soil samples were obtained at the locations and soil properties including organic carbon, total nitrogen, total potassium and total phosphorus were measured. The correlations of the soil properties with original bands and enhanced spectral variables derived from both field and satellite hyperspectral data were analyzed. Regression models that explained the relationships were further developed to map the soil properties. The results showed that the stepwise regression models based on the satellite hyperspectral image derived enhanced spectral variables produced reasonable spatial distributions of the soil properties and the relative RMSE values of 68.9, 46.3, 31.4 and 45.5% for soil organic carbon, total nitrogen, total phosphorus and total potassium, respectively. Thus, this study implied that the hyperspectral data based method provided great potential to predict the soil properties. [ABSTRACT FROM AUTHOR]

To increase the accuracy of predicting net primary productivity (NPP), in this study, Carnegie–Ames–Stanford Approach (CASA) model was modified by developing new methods to estimate absorbed photosynthetically active radiation or fraction of photosynthetically active radiation (FPAR) and water stress coefficient (WSC). In the modified model, FPAR was derived based on its non-linear relationship with leaf area index. Moreover, WSC was estimated using leaf water potential from soil moisture instead of a traditional evapotranspiration-based method. This study was conducted in Baiyun District area of Guangzhou, China, using Gaofen-1 (GF-1), Landsat 7, and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The predictions from the original and three modified CASA models and MODIS NPP product MOD17A3 were compared with field observations. The results showed that all the CASA-based models led to similar spatial distributions of forest aboveground NPP estimates. Overall, the estimates increased with elevation because the valley bottoms were dominated by developed or urbanized areas whereas the hillslopes and hilltops were largely vegetated. Based on root mean square error (RMSE) and relative RMSE between the observed and predicted values, the CASA model that integrated the modifications of both FPAR and WSC increased the estimation accuracy of NPP by 8.1% over the original one. The increase in accuracy was mainly contributed by the modification of FPAR. This suggested that the modification of FPAR provided greater potential than that of WSC for improving the predictions of CASA model. Compared to the CASA models, MOD17A3 had lower accuracy of aboveground NPP estimates. This study also showed that the fine spatial resolution GF-1 image provided a new source of data used to estimate NPP of forest ecosystems. [ABSTRACT FROM PUBLISHER]

Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling. [ABSTRACT FROM PUBLISHER]

Remotely sensed data have been widely used for classification of land-use and land-cover (LULC) types. However, classifying different forest types in Northeast China using satellite images is still a great challenge because of the similar spectral reflectances of different tree species. The differences of vegetation phenological characteristics provide the potential of classifying the types using time series of spectral variables derived from images. In this study, time series of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) images obtained in 2012 for Northeast China were used to calculate various phenological metrics and to further derive amplitude and phase information of harmonic components using Fourier transforms. The separability of eight vegetation cover types plus water and built-up areas was then analysed using phenological metrics, and amplitude and phase of harmonic components. Moreover, a phenology-based decision tree classifier was developed to classify the types in this area. Out of 4900 national forest inventory plots, 3700 plots were used to train the decision tree classifier and 1200 plots to assess the accuracy of classification by combining the plots’ observations with the values of a published LULC map that had a higher spatial resolution and accuracy of classification using a window majority rule. In addition, three data sets from different temporal resolution MODIS NDVI and EVI time series and two similarity measures were compared for separability and classification of the types. The results showed that (1) Fourier transforms of NDVI and EVI time series led to the first four harmonic components (including component 0, average annual NDVI, and EVI) that captured the phenological characteristics of the cover types; (2) compared to those using only NDVI, the separability values of the classes using NDVI, amplitude, and phase increased from 1.71 to 1.95, implying the potential improvement of classification; (3) the data set from 10-day NDVI time series had higher classification accuracy than those from 16-day NDVI and EVI time series, although the EVI time series performed slightly better than the NDVI time series at the same temporal resolution; and (4) a classification accuracy of 83.8% with a kappa coefficientof 0.79 was finally obtained. This study implied that this method is applicable for classification of vegetation cover types for large areas. [ABSTRACT FROM AUTHOR]

For the past three decades, ecologists and biogeographers have increasingly incorporated remote sensing and geographical information systems (GISs) to inventory and analyse spatially organized data. Although there are many studies exploring the effects of fine resolution on remote sensing and GIS mapping, there is still a gap on how to identify the most appropriate spatial resolutions for studying landscapes and their structures and dynamics. This study investigated the effects of landscape changes over a 64-year study period at different spatial resolutions using four resampling schemes. The study was conducted on a national wildlife refuge of five land-use and land-cover (LULC) categories using aerial photos recorded in three distant years (1938, 1971, and 2001). This refuge has undergone major landscape changes in the last 64 years. Among the five LULC categories studied, the one that lost the most surface is agriculture; the most gain was made in forest and water. In terms of net change and swapping, agriculture and forest were the most dynamic categories in the National Wildlife Refuge. Our findings showed considerable spatial variability in landscape dynamics at different scales. We specifically observed that hard-classified maps with spatial resolutions of 30 m or finer provided better analysis of landscape dynamics, whereas with soft-classified maps it could go up to 90 m or finer. This implies that there is a range of optimum resolution that could allow for the use of medium-resolution data, such as Landsat, for reliable land change analysis. These findings offer further insight on landscape change analysis at different spatial resolutions and advance our understanding and knowledge on the effect of scale on landscape ecology. [ABSTRACT FROM AUTHOR]

Quantification of forest biomass is important for practical forestry and for scientific purposes. It is fundamental to develop generic individual-tree biomass models suitable for large-scale forest biomass estimation. However, compatibility of forest biomass estimates at different scales may become a problem. We developed generic individual-tree biomass models using a mixed-effects modeling approach based on aboveground biomass data of Masson pine (Pinus massonianaLamb.) from nine provinces in southern China. Mixed-effects modeling could provide an effective approach to solving the compatibility of forest biomass estimates at different scales. A simple allometric function requiring diameter at breast height was used as a base model to construct generic individual-tree mixed-effects biomass models. Two factors of tree origin (natural and planted forests) and geographic region (nine provinces or three subregions) were included as random effect factors in the models. The results showed that the mixed-effects model not only provided more accurate estimates, but also possessed good universality compared with the population average model. We, therefore, recommend the mixed-effects model 17 to estimate national and regional-scale biomass for Masson pine in southern China. The mixed-effects modeling approach is versatile and can also be applied to construct generic individual-tree models for other tree species and variables. [ABSTRACT FROM AUTHOR]

Forest carbon sinks significantly contribute to mitigation of atmospheric concentrations of carbon dioxide. Thus, estimating forest carbon is becoming important to develop policies for mitigating climate change and trading carbon credits. However, a great challenge is how to quantify uncertainties in estimation of forest carbon. This study investigated uncertainties of mapping aboveground forest carbon due to location errors of sample plots for Lin-An County of China. National forest inventory plot data and Landsat TM images were combined using co-simulation algorithm. The findings show that randomly perturbing plot locations within 10 distance intervals statistically did not result in biased population mean predictions of aboveground forest carbon at a significant level of 0.05, but increased root mean square errors of the maps. The perturbations weakened spatial autocorrelation of aboveground forest carbon and its correlation with spectral variables. The perturbed distances of 800 m or less did not obviously change the spatial distribution of predicted values. However, when the perturbed distances were 1600 m or larger, the correlation coefficients of the predicted values from the perturbed locations with those from the true plot locations statistically did not significantly differ from zero at a level of 0.05 and the spatial distributions became random. [ABSTRACT FROM AUTHOR]

This study investigates applications and efficiencies of remotely sensed data and the sensitivity of grid spacing for the sampling and mapping of a ground and vegetation cover factor in a monitoring system of soil erosion dynamics by cokriging with Landsat Thematic Mapper (TM) imagery based on regionalized variable theory. The results show that using image data can greatly reduce the number of ground sample plots and sampling cost required for collection of data. Under the same precision requirement, the efficiency gain is significant as the ratio of ground to image data used varies from 1: 1 to 1: 16. Moreover, we proposed and discussed several modifications to the cokriging procedure with image data for sampling and mapping. First, directly using neighbouring pixels for image data in sampling design and mapping is more efficient at increasing the accuracy of maps than using sampled pixels. Although information among neighbouring pixels might be considered redundant, spatial cross-correlation of spectral variables with the cover factor can provide the basis for an increase in accuracy. Secondly, this procedure can be applied to investigate the appropriate spatial resolution of imagery, which, for sampling and mapping the cover factor, should be 90 m × 90 m - nearly consistent with the line transect size of 100 m used for the ground field survey. In addition, we recommend using the average of cokriging variance to determine the global grid spacing of samples, instead of the maximum cokriging variance. [ABSTRACT FROM AUTHOR]