Moso bamboo forest extraction and aboveground carbon storage estimation based on multi-source remotely sensed images

Using a combination of moso bamboo forest thematic maps derived from Landsat Thematic Mapper (TM) images, field inventory data, and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) images, moso bamboo forest was extracted using the matched filtering (MF) technique and its aboveground carbon storage (AGC) was then estimated. This study presents a feasible method for extracting large-scale moso bamboo forests and for estimating moso bamboo forest AGC based on low-spatial resolution MODIS images. The results showed that moso bamboo forests in the majority of counties can be accurately estimated between actual area and estimates, with an R ² of 0.8453. The fitted accuracy of the AGC model was high (R ² = 0.491). The prediction accuracy of the AGC model was also evaluated using validation samples collected from Lin'an City, with an R ² and root mean square error prediction of 0.4778 and 3.06 Mg C ha^?1, respectively. The AGC in the majority of counties or cities in Zhejiang Province was between 0 and 15 Mg C ha^?1, and to a certain extent the predicted AGC estimates were close to observed ground truth data and representative of the study area.     

Classification of SPOT HRV imagery and texture features

Abstract

Spatial co-occurrence matrices were computed for a SPOT HRV multispectral image for a moderate-relief environment in eastern Canada. The texture features entropy and inverse difference moment were used with the spectral data in landcover classification, and substantive increases in accuracy were noted. These range from 10 per cent for exposed bedrock to over 40 per cent in forest and wetland classes. The average classification accuracies were increased from 511 per cent (spectral data alone) to 86.7 per cent (spectral data plus entropy measured in band 2 and inverse difference moment in band 3). Classes that are homogeneous on the ground were characterized adequately by spectral tone alone, but classes containing mixed vegetation patterns or strongly related to structure were characterized more accurately by using a mixture of spectral tone and texture.     

Determining the rate of forest conversion in Mato Grosso,Brazil, using Landsat MSS and AVHRR data

Abstract

AVHRR-LAC thermal data and Landsat MSS and TM spectral data were used to estimate the rate of forest clearing in Mato Grosso, Brazil, between 1981 and 1984. The Brazilian state was stratified into forest and non-forest. A list sampling procedure was used in the forest stratum to select Landsat MSS scenes for processing based on estimates of fire activity in the scenes. Fire activity in 1984 was estimated using AVHRR-LAC thermal data. Slate-wide estimates of forest conversion indicate that between 1981 and 1984, 353966 ha ±77 000 ha (0·4 percent of the state area) were converted per year. No evidence of reforestation was found in this digital sample. The relationship between forest clearing rate (based on MSS-TM analysis)and fire activity (estimated using AVHRR data)was noisy (R²= 0·41). The results suggest that AVHRR data may be put to better use as a stratification tool rather than as a subsidiary variable in list sampling.     

TM digital processing of a tropical forest region in southeastern Mexico

A humid forest in the neotropical area of Los Tuxtlas, in southeastern Mexico has been used as a test area (900km²) for classification of landscape and vegetation by means of Landsat Thematic Mapper (TM) data, aerial photography and 103 ground samples. The area presents altitudinal variations from sea level to 1640m, providing a wide variety of vegetation types. A hybrid (supervised/unsupervised) classification approach was used, defining spectral signatures for 14 clustering areas with data from the reflective bands of the TM. The selected clustering areas ranged from vegetation of the highlands and the rain forest to grassland, barren soil, crops and secondary vegetation. The digital classification compared favourably with results from aerial photography and with those from a multivariate analysis of the 103 ground data. The statistical evaluation (error matrix) of the classified image indicated an overall 84·4 per cent accuracy with a kappa coefficient of agreement of 0·83. A geographical information system (GIS) was used to compile a land unit and a vegetation map. The TM data allowed for delineation of boundaries in the land unit map, and for a finer differentiation of vegetation types than those identified during field work. Digital value patterns of several information classes are shown and discussed as an indirect guide of the spectral behaviour of vegetation of highlands, rain forest, secondary vegetation and crops. The method is considered applicable to the inventory of other forested areas, especially those with significant variations in vegetation.     

Forest classification by principal component analyses of TM data

Abstract

Differential insolation generally strongly influences the spectral responses of woody areas distributed over hills and mountains, making them difficult to discriminate by means of remote sensing techniques. In this paper, we examine the possibilities of a new method of variance decomposition of Thematic Mapper (TM) data using principal component analyses applied to this problem. The first principal components found for every class of ground truth are assumed as expressing the portions of spectral variance related to different insolation and are therefore not considered in discriminating the forested surfaces of two TM scenes by a minimum distance classifier. The low quality and quantity of the ground information used in the research have forced us to employ them with some restrictions. This leads to overestimation of the classification accuracy evaluated by a final confusion matrix; for this reason, the obtained results cannot be considered definitive.     

Subpixel measurement of tropical forest cover using AVHRR data

Abstract

Tropical forest assessment using data from the Advanced Very High Resolution Radiometer (AVHRR) may lead to inaccurate estimates of forest cover in regions of small subpixel forest or non-forest patches and in regions where the pattern of clearance is particularly convoluted. Test sites typifying these two patterns were chosen in Ghana and Rondonia, respectively. To capture the subpixel proportions of forest cover, a linear mixture model was applied to two AVHRR test images over the test sites. The model produced image outputs in which pixel intensities indicated the proporton of forest cover per km². For comparison, supervised maximum likelihood classifications were also performed. The outputs were assessed against classified Landsat TM scenes, converted to proportions maps and coregistered to the AVHRR images. An empirical method was applied for determining the critical forest cover per km² needed for an AVHRR pixel to be classified as forest. The critical values exceeded 50 per cent, indicating a tendency for AVHRR classification to underestimate forest cover. This was confirmed by comparing estimates of total forest cover obtained from the AVHRR and TM classifications. In the case of Ghana, a more accurate estimate of forest cover was obtained from the AVHRR mixture model than from the classification. Both mixture model outputs were found to be well correlated with those from Landsat TM. Further work should test the robustness of the approach adopted here when applied to much larger areas.     

Estimation of tropical forest biomass using Landsat TM imagery and permanent plot data in Xishuangbanna,China

Because of its complexity, it is very difficult to obtain information about distribution of biomass in tropical forests. This article describes the estimation of tropical forest biomass by using Landsat TM and forest plot data in Xishuangbanna, PR China. The method includes several steps. First, the biomass for each forest permanent plot is calculated by using field inventory data. Second, Landsat TM images are geometrically corrected by using topographic maps. Third, a map of the tropical forest is obtained by using data from a variety of sources such as Landsat TM, digital elevation model (DEM), temperature and precipitation layers and expert knowledge. Finally, the biomass and carbon storage of each forest vegetation type in the forest map is calculated by using the tropical forest map and the forest plot biomass GIS database. In the study area, forest area accounts for 57% of the total 1.7?×?10⁶ hectares. The total forest biomass is 2.0?×?10⁸ tonne. It is shown that the forest vegetation map, the forest biomass and the forest carbon storage can be obtained by effectively integrating Landsat TM, ancillary data including DEM, temperature and precipitation, forest permanent plots and knowledge using the method proposed here.     

Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data

In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. Estimates of AGB are relevant for sustainable forest management, monitoring global change, and carbon accounting. This is particularly true for the Qilian Mountains, which are a water resource protection zone. We combined forest inventory data from 133 plots with TM images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) V2 products (GDEM) in order to analyse the influence of the sun-canopy-sensor plus C (SCS+C) topographic correction on estimations of forest AGB using the stepwise multiple linear regression (SMLR) and k-nearest neighbour (k-NN) methods. For both methods, our results indicated that the SCS+C correction was necessary for getting more reliable forest AGB estimates within this complex terrain. Remotely sensed AGB estimates were validated against forest inventory data using the leave-one-out (LOO) method. An optimized k-NN method was designed by varying both mathematical formulation of the algorithm and remote-sensing data input, which resulted in 3000 different model configurations. Following topographic correction, performance of the optimized k-NN method was compared to that of the regression method. The optimized k-NN method (R² = 0.59, root mean square error (RMSE) = 24.92 tonnes ha^–1) was found to perform much better than the regression method (R² = 0.42, RMSE = 29.74 tonnes ha^–1) for forest AGB retrieval over this montane area. Our results indicated that the optimized k-NN method is capable of operational application to forest AGB estimates in regions where few inventory data are available.     

Geo-referencing forest field plots by co-registration of terrestrial and airborne laser scanning data

Remote sensing plays an important role within the field of forest inventory. Airborne laser scanning (ALS) has become an effective tool for acquiring forest inventory data. In most ALS-based forest inventories, accurately positioned field plots are used in the process of relating ALS data to field-observed biophysical properties. The geo-referencing of these field plots is typically carried out by means of differential global navigation satellite systems (dGNSS), and often relies on logging times of 15–20 min to ensure adequate accuracy under different forest conditions. Terrestrial laser scanning (TLS) has been proposed as a possible tool for collection of field data in forest inventories and can facilitate rapid acquisition of these data. In the present study, a novel method for co-registration of TLS and ALS data by posterior analysis of remote-sensing data – rather than using dGNSS – was proposed and then tested on 71 plots in a boreal forest. The method relies on an initial position obtained with a recreational-grade GPS receiver, in addition to analysis of the ALS and TLS data. First, individual tree positions were derived from the remote-sensing data. A search algorithm was then used to find the best match for the TLS-derived trees among the ALS-derived trees within a search area, defined relative to the initial position. The accuracy of co-registration was assessed by comparison with an accurately measured reference position. With a search radius of 25 m and using low-density ALS data (0.7 points m^?2), 82% and 51% of the TLS scans were co-registered with positional errors within 1 m and 0.5 m, respectively. By using ALS data of medium density (7.5 points m^?2), 87% and 78% of the scans were co-registered with errors within 1 m and 0.5 m of the reference position, respectively. These results are promising and the method can facilitate rapid acquisition and geo-referencing of field data. Robust methods to identify and handle erroneous matches are, however, required before it is suitable for operational use.     

Landsat-TM data for estimating ground temperature and depth of subsurface coal fire in the Jharia coalfield,India

Coal fires are a ubiquitous problem in coal-mines, the world over. They burn our prime energy resource, lead to atmospheric pollution and render mining of coal hazardous. Processes leading to coal combustion and spread of subsurface fires are briefly examined in this paper and the role of remote sensing in surveillance of coal fires is presented.

The present study aims at developing a quick method for estimating the temperature of the ground surface directly above subsurface coal fires. Utility of TM6 and TM7-band data for temperature estimation is briefly reviewed. It is argued that temperature calculations of surface anomalies related to subsurface fires can only be done on the basis of 8–14 μm band data, due to the low temperatures involved.

In the Jharia coalfield, it is noted that subsurface fires in various coal-mines are associated with surface thermal anomalies, as has also been confirmed by ground checks. The pattern of TM6 data distribution and ground truth is used to isolate thermal anomalies, and the TM6 digital numbers are converted into kinetic temperature values. It is observed that for the Landsat-TM scene ( 28 November 1990) the kinetic temperatures range from 16·0°C to 31·6°C in the Jharia coalfield, with a threshold value of 25·6°C associated with the anomalies. Depth estimation of fire has been carried out using field structural geology data and pixel locations of thermal anomalies. It is inferred that the depth of subsurface coal fire ranges between 45–55 m, in most cases, which is in general agreement with the field data. Limitations of the method are indicated.     

Synergistic effects of combined Landsat-TM and SIR-B data for forest resources assessment

Abstract

Three different incidence angle data sets, obtained by the Shuttle Imaging Radar-B in October 1984 over a forested area in northern Florida, were combined with Landsat-5 Thematic Mapper data, to create a digitally registered 10 channel optical/microwave data set. The work discussed in this paper involved the analysis of the data obtained by the two sensors separately and in combination, to determine if there are synergistic effects obtained through the simultaneous use of data obtained from both the optical and microwave portion of the spectrum. The radar data were filtered with a low-pass filter to eliminate the speckle noise. Classifications of the TM, SIR-B and combined TM +SIR-B data sets were performed with both per-point and contextual classifiers. The results showed that filtered radar data can be used to classify accurately major cover types (i.e., pine forest, swamplands and radar smooth targets) and that the contextual classifier provided better classification performance. The combined TM and SIR-B data provided statistically improved classification performances compared to classifications from the three incidence angle SIR-B data, or the TM data alone. A four band subset (TM-2, TM-4, TM-5, and SIR-B 28°) of the 10 channels of the combined TM and SIR-B data set provided higher classification performances (91 per cent overall performance) than the 10 channel data set (86 per cent overall performance).     

Maximum likelihood classification,optimal or problematic? A comparison with the nearest neighbour classification

Abstract

The maximum likelihood and the nearest neighbour classification algorithms are reviewed, particularly from the point of view of user/analyst requirements. The two algorithms were put to use for the classification or Landsat TM data of agricultural scenes and accuracy with respect to ‘ground truth’ was evaluated using different parametric settings. Results show that within the maximum likelihood classification, accuracies and errors can vary to a considerable degree depending on the formation of the statistical classes from the training data. More interestingly, it was found that the nearest neighbour algorithm produced higher accuracies and was judged to be more robust, but it has computer implementation problems with high data dimensionality.     

A technique for extrapolating and validating forest cover across large regions Calibrating AVHRR data with TM data

Abstract

An approach to extending high-resolution forest cover information across large regions is presented and validated. Landsat Thematic Mapper (TM) data were classified into forest and nonforest for a portion of Jackson County, Illinois. The classified TM image was then used to determine the relationship between forest cover and the spectral signature of Advanced Very High Resolution Radiometer (AVHRR) pixels covering the same location. Regression analysis was used to develop an empirical relationship between AVHRR spectral signatures and forest cover. The regression equation developed from data from the single county calibration area in southern Illinois was then applied to the entire AVHRR scene, which covered all or parts of ten states, to produce a regional map of forest cover. This map was used to derive estimates of forest cover, within a geographical information system (GIS), for each of the 428 counties located within the boundaries of the original AVHRR scene. The validity of the overall regional map was tested by comparing the AVHRR/TM-derived estimates of county forest cover with independent estimates of county forest cover developed by the U.S. Forest Service (USFS). The overall correlation coefficient of the AVHRR/TM and USFS county forest cover estimates was r=0-89 (n=428 counties). Not surpris0ingly, some individual states and the areas nearer to the southern Illinois calibration centre had higher correlation coefficients. Absolute estimates of forest cover percentages were also significantly well predicted. With the future inclusion of multiple calibration centres representing a number of physiographic regions, the method shows promise for predicting continental and global estimates of forest cover.     

Object-based satellite image analysis applied for crop residue estimating using Landsat OLI imagery

ABSTRACT

The main objective of this study is to apply an object-based image analysis (OBIA) approach to satellite image processing and determining crop residue cover (CRC) and tillage intensity. To achieve this goal, we collected ground truth data using line-transect method from 35 plots of farmlands with an area of 528 ha. Accordingly, Landsat Operational Land Imager (OLI) satellite image together with global positioning system (GPS)-based survey data set were considered for applying the OBIA methods and deriving CRC. To process the data, object-based image processing steps including segmentation and classification were applied to develop intelligent objects and establish classification using spectral and spatial characteristics of CRC. We developed three categories of rule sets including mean indices, tillage indices, and grey-level co-occurrence matrix (GLCM) texture features using the OBIA algorithms and assign class method. Results were validated against of ground control data set and were collected by GPS in field survey. Results of this study indicated that the brightness, normalised difference tillage index, and GLCM texture feature mean performed out as effective techniques. Overall accuracy and kappa coefficient (κ) were computed to be about 0.91 and 0.86; 0.93 and 0.90; 0.60 and 0.35, respectively, for the above-mentioned indices. The foregoing discussion has attempted to demonstrate that the remotely sensed data can be effective approach and substitute for ground methods, especially in large areas.     

Estimating forest structure in wetlands using multitemporal SAR

Data from 202 forest plots on the Roanoke River floodplain, North Carolina were used to assess the capabilities of multitemporal radar imagery for estimating biophysical characteristics of forested wetlands. The research was designed to determine the potential for using widely available data from the current set of satellite-borne synthetic aperture radar (SAR) sensors to study forests over broad geographic areas and complex environmental gradients. The SAR data set included 11 Radarsat scenes, 2 ERS-1 images, and 1 JERS-1 scene. Empirical analyses were stratified by flood status such that sites were compared only if they exhibited common flooding characteristics. In general, the results indicate that forest properties are more accurately estimated using data from flooded areas, probably because variations in surface conditions are minimized where there is a continuous surface of standing water. Estimations yielded root mean square errors (RMSEs) for validation data around 10 m²/ha for basal area (BA), and less than 3 m for canopy height. The r² values generally exceeded .65 for BA, with the best predictions coming from sample sites for which both nonflooded and flooded SAR scenes were available. The addition of early spring normalized difference vegetation index (NDVI) values from Landsat Thematic Mapper (Landsat TM) improved model predictions for BA in forests where BA levels were <55 m²/ha. Further analyses indicated a very limited sensitivity of the individual SAR scenes to differences in forest composition, although soil properties in nonflooded areas exerted a weak but nevertheless important influence on backscatter.     

A ground truth based vanishing point detection algorithm

A Bayesian probability-based vanishing point detection algorithm is presented which introduces the use of multiple features and training with ground truth data to determine vanishing point locations. The vanishing points of 352 images were manually identified to create ground truth data. Each intersection is assigned a probability of being coincident with a ground truth vanishing point, based upon conditional probabilities of a number of features. The results of this algorithm are demonstrated to be superior to the results of a similar algorithm where each intersection is considered to be of equal importance. The advantage of this algorithm is that multiple features derived from ground truth training are used to determine vanishing point location.     

Tests of topographic mapping with Thematic Mapper images

Abstract

This paper reports on research sponsored by the European Space Agency on the potential of Thematic Mapper (TM) images for topographic mapping from the viewpoint both of accuracy and content. After a thorough test with Large Format Camera images taken as ground truth, the accuracy of point positioning was assessed as ～ 50m. Severe difficulties were encountered in updating or drawing new 1:500 000 maps due to the lack of the fundamental information contained in such a map in theTM images. The use of TM images for topographic mapping on that scale is therefore discouraged.     

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R ² > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R ² > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R ²?=?0.78, accuracy?=?73%, root mean square error (RMSE)?=?0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy?=?79%, RMSE?=?0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.     

Assessment of grassland degradation near Lake Qinghai,West China,using Landsat TM and in situ reflectance spectra data

The severity of grassland degradation near Lake Qinghai, West China was assessed from a Landsat Thematic Mapper (TM) image in conjunction with in situ samples of per cent grass cover and proportion (by weight) of unpalatable grasses (PUG) collected over 1?m² sampling plots. Spectral reflectance at each sampling plot was measured with a spectrometer and its location determined with a Global Positioning System (GPS) receiver. After radiometric calibration, the TM image was geometrically rectified. Ten vegetation indices were derived from TM bands 3 and 4, and from the spectral reflectance data at wavelengths corresponding most closely to those of TM3 and TM4. Regression analyses showed that NDVI and SAVI are the most reliable indicators of grass cover and PUG, respectively. Significant relationships between TM bands-derived indices and in situ sampled grass parameters were established only after the former had been calibrated with in situ reflectance spectra data. Through the established regression models the TM image was converted into maps of grass cover parameters. These maps were merged to form a degradation map at an accuracy of 91.7%. It was concluded that TM imagery, in conjunction with in situ grass samples and reflectance spectra data, enabled the efficient and accurate assessment of grassland degradation inside the study area.     

Fraction images derived from Terra Modis data for mapping burnt areas in Brazilian Amazonia

The objective of this paper is to present a method for mapping burnt areas in Brazilian Amazonia using Terra MODIS data. The proposed approach is based on image segmentation of the shade fraction images derived from MODIS, using a non‐supervised classification algorithm followed by an image editing procedure for minimizing misclassifications. Acre State, the focus of this study, is located in the western region of Brazilian Amazonia and undergoing tropical deforestation. The extended dry season in 2005 affected this region creating conditions for extensive forest fires in addition to fires associated with deforestation and land management. The high temporal resolution of MODIS provides information for studying the resulting burnt areas. Landsat 5 TM images and field observations were also used as ground data for supporting and validating the MODIS results. Multitemporal analysis with MODIS showed that about 6500 km² of land surface were burnt in Acre State. Of this, 3700 km² corresponded to the previously deforested areas and 2800 km² corresponded to areas of standing forests. This type of information and its timely availability are critical for regional and global environmental studies. The results showed that daily MODIS sensor data are useful sources of information for mapping burnt areas, and the proposed method can be used in an operational project in Brazilian Amazonia.