Image-based reflectance conversion of ASTER and IKONOS imagery as precursor to structural assessment of plantation forests in KwaZulu-Natal,South Africa

Reflectance-converted imagery is a requirement for establishing temporally robust remote sensing algorithms, given the reduction of time-specific atmospheric effects. Thus, in this study image-based atmospheric correction methods for ASTER and IKONOS imagery for retrieving surface reflectance of plantation forests in KwaZulu-Natal, South Africa were evaluated. This effort formed part of a larger initiative that focused on retrieval of forest structural attributes from resultant reflectance imagery. Atmospheric correction methods in this study included the apparent reflectance model (AR), dark object subtraction model (DOS), and the cosine approximation model (COST). Spectral signatures derived from different image-based models for ASTER and IKONOS were inspected visually as first departure. This was followed by comparison of the total accuracy and Kappa index computed from supervised classification of images that were derived from different image-based atmospheric correction of ASTER and IKONOS imagery. The classification accuracy of DOS images derived from ASTER and IKONOS imagery exhibited percentages of 93.3% and 94.7%, respectively. Classification accuracies for images from AR and COST, on the other hand, resulted in lower accuracy values of 87.9% and 83.6% for ASTER and 90.5% and 92.8% for IKONOS, respectively. We concluded that the image-based DOS model was better suited to atmospheric correction for ASTER and IKONOS imagery in this study area and for the purpose of forest structural assessment. This has important implications for the operational use of similar imagery types for forest inventory approaches.     

Satellite-based stand-wise forest cover type mapping using a spatially adaptive classification approach

Due to high variation in forest communities, forest structure and the fragmentation of the forested area in Central Europe, satellite-based forest inventory methods have to meet particularly high-quality requirements. This study presents an innovative method to combine official forest inventory information at stand level with multidate satellite imagery using a spatially adaptive classification approach for producing wall-to-wall forest cover maps of important tree species and management classes across multiple ownership regions in a heterogeneous low mountain range in Germany. The classification approach was applied to a 5,200-km² area (about 2,080?km² of forest land, mostly mixed forests) located in the Eifel mountain range in Central Europe. In comparison with conventional classifiers, our results demonstrate a significant increase in classification accuracy in the order of 12%. The method was tested with ASTER images but holds the potential to be used for regular state forest inventories based on standard and novel earth observation data supplied for instance from the SPOT-5 and RapidEye sensors.     

Estimation of premature forests in Georgia (USA) using U.S. Forest Service FIA data and Landsat imagery

We used geographic information system applications and statistical analyses to classify young, premature forest areas in southeastern Georgia using combined data from Landsat TM 5 satellite imagery and ground inventory data. We defined premature stands as forests with trees up to 15 years old. We estimated the premature forest areas using three methods: maximum likelihood classification (MLC), regression analysis, and k-nearest neighbor (kNN) modeling. Overall accuracy (OA) of classifying the premature forest using MLC was 82% and the Kappa coefficient of agreement was 0.63, which was the highest among the methods that we have tested. The kNN approach ranked second in accuracy with OA of 61% and a Kappa coefficient of agreement of 0.22. Regression analysis yielded an OA of 57% and a Kappa coefficient of 0.14. We conclude that Landsat imagery can be effectively used for estimating premature forest areas in combination with image processing classifiers such as MLC.     

The inventory of carbon stock in New Zealand’s post-1989 planted forest for reporting under the Kyoto protocol

The United Nations Framework Convention on Climate Change (UNFCCC) requires reporting net carbon stock changes and anthropogenic greenhouse gas emissions, including those related to forests. This paper describes the design and implementation of a nation-wide forest inventory of New Zealand’s planted post-1989 forests that arose from Land Use, Land-Use Change and Forestry activities (LULUCF) under Article 3.3 of the Kyoto Protocol. The majority of these forests are planted with Pinus radiata, with the remainder made up of other species exotic to New Zealand. At the start of the project there was no on-going national forest inventory that could be used as a basis for calculating carbon stocks and meet Good Practice Guidelines.A network of ground-based permanent sample plots was installed with airborne LiDAR (Light Detection and Ranging) for double sampling using regression estimators to predict carbon in each of the four carbon pools of above- and below-ground live biomass, dead wood and litter. Measurement, data acquisition and quality assurance/control protocols were developed specifically for the inventory, carried out in 2007 and 2008. Plots were located at the intersection of a forest with a 4 km square grid, coincident with an equivalent 8 km square grid established over the indigenous forest and “grassland with woody biomass” (Other Wooded Land). Planted tree carbon within a ground plot was calculated by an integrated system of growth, wood density and compartment allocation models utilising the data from measurements of trees and shrubs on the plots. This system, called the Forest Carbon Predictor, predicts past and future carbon in a stand and is conditioned so that the calculated basal area and mean top height equals that obtained by conventional mensuration methods at the time of the plot measurement. Mean per hectare carbon stocks were then multiplied by an estimate of the total area of post 1989 forests obtained from wall to wall mapping using a combination of satellite imagery and ortho-photography.The network of permanent samples plots and LiDAR double sampling methodology was designed to be simple and robust to change over time. In the future, using LiDAR should achieve sampling efficiencies over using ground plots alone and reduces any problems regarding restricted access on the ground. The network is to be remeasured at the end of commitment period 1, 2012, and the carbon stocks re-estimated in order to calculate change.     

Estimating forest biomass increment based on permanent sample plots in relation to woodfuel consumption: a case study in Kampong Thom Province,Cambodia

To assess the sustainability of forest use for woodfuel, above ground biomass increment must be examined against woodfuel consumption. However, reliable data on the biomass increment of tropical forests are very limited. In this study, we estimated above ground forest biomass increment in Kampong Thom Province, Cambodia, using two consecutive measurements of 32 permanent sample plots in 1998 and 2000, and forest inventory data of 540 plots collected in 1997. The permanent sampling plot data were used to determine the relationship between initial biomass and subsequent biomass increment over a 2-year period. This relationship was applied to the inventory data to obtain a robust estimate of biomass increment across the major forest types for the entire province. The weighted average annual above ground biomass increment for the whole province was 4.77Mg/ha, or 2.3% of biomass. Woodfuel consumption was estimated to be about 2% of biomass increment for the province, suggesting that deficiency of woodfuel may not occur in this province. However, localized variation needs to be taken into account and there is a need to examine the effects of stand age and factors such as soil type, microtopography, and species composition on biomass increment and to consider woodfuel collection rate in specific forest areas with respect to accessibility for firewood collection.     

遥感影像分类技术在森林景观分类评价中的应用研究

以吉林省旺清林业局金沟岭林场为例,在地面调查数据和Landsat TM多光谱卫星遥感数据的基础上,以ERDAS遥感影像处理系统和MapInfo地理信息系统支持,利用基于二类调查数据取证遥感分类技术,对其森林景观进行分类和评价,得到金沟岭林场森林景观空间分布图及空间格局分析结果。本研究提出的一套实用技术方法可为宏观上快速提取森林景观要素及空间格局状态提供技术参考,结果可为进一步森林景观规划和设计提供依据。     

Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass

The spatial distribution of forest biomass is closely related with carbon cycle, climate change, forest productivity, and biodiversity. Efficient quantification of biomass provides important information about forest quality and health. With the rising awareness of sustainable development, the ecological benefits of forest biomass attract more attention compared to traditional wood supply function. In this study, two nonparametric modeling approaches, random forest (RF) and support vector machine were adopted to estimate above ground biomass (AGB) using widely used Landsat imagery in the region, especially within the ecological forest of Fuyang District in Zhejiang Province, China. Correlation analysis was accomplished and model parameters were optimized during the modeling process. As a result, the best performance modeling method RF was implemented to produce an AGB estimation map. The predicted map of AGB in the study area showed obvious spatial variability and demonstrated that within the current ecological forest zone, as well as the protected areas, the average of AGB were higher than the ordinary forest. The quantification of AGB was proven to have a close relationship with the local forest policy and management pattern, which indicated that combining remote-sensing imagery and forest biophysical property would provide considerable guidance for making beneficial decisions.     

西藏自治区森林碳密度及分布规律研究

利用森林资源连续清查实测样地及样木数据,结合相对树高曲线,构建生物量-蓄积量模型,解决了模型与各类森林资源调查数据的衔接问题,可应用于西藏自治区森林资源连续清查的目测与遥感样地生物量估算及森林资源规划设计调查小班生物量估算等。根据计算的森林资源连续清查各样地生物量密度,结合树种面积数据及含碳率,估算全区森林碳密度,并初步探讨了森林碳库地带性分布规律。     

Effect of cluster configuration and auxiliary variables on the efficiency of local pivotal method for national forest inventory

ABSTRACT

Planning a forest inventory comprises making decisions related to the sampling strategy: cluster configuration, sample size and sample allocation within the survey area. Cluster configuration includes deciding on the number of sample plots within the cluster and distances between them. Available resources set the limit for field work in terms of man-days. If the time consumption for measurements is known, the sample size can be determined under the constraint. In this study, we simulated the second phase of inventory sampling with fixed time resources by replicating sample selection with a spatially balanced sampling utilizing local pivotal method (LPM) for different cluster configurations to find the most efficient. As a result, the temporary cluster configuration was changed from 9 to 5-sample plot configuration in a pilot inventory. Further, the sample selection was performed with LPM having total growing stock volume and broadleaf volume proportion as auxiliary information. The pilot results were aligned with the time series in respect to forest area and total growing stock volume, but in tree species groups deviations were observed in growing stock volume. A more comprehensive optimization should include the travelling routes, the plot-to-plot distances and the plot design. In any case, the result is region specific.     

Spatiotemporal pattern of urban forest leaf area index in response to rapid urbanization and urban greening

Rapid urbanization and urban greening have caused great changes to urban forests in China. Understanding spatiotemporal patterns of urban forest leaf area index(LAI) under rapid urbanization and urban greening is important for urban forest planning and management. We evaluated the potential for estimating urban forest LAI spatiotemporally by using Landsat TM imagery. We collected three scenes of Landsat TM(thematic mapper)images acquired in 1997, 2004 and 2010 and conducted a field survey to collect urban forest LAI. Finally, spatiotemporal maps of the urban forest LAI were created using a NDVI-based urban forest LAI predictive model.Our results show that normalized differential vegetation index(NDVI) could be used as a predictor for urban forest LAI similar to natural forests. Both rapid urbanization and urban greening contribute to the changing process of urban forest LAI. The urban forest has changed considerably from 1997 to 2010. Urban vegetated pixels decreased gradually from 1997 to 2010 due to intensive urbanization.Leaf area for the study area was 216.4, 145.2 and173.7 km~2 in the years 1997, 2004 and 2010, respectively.Urban forest LAI decreased sharply from 1997 to 2004 and increased slightly from 2004 to 2010 because of numerous greening policies. The urban forest LAI class distributions were skewed toward low values in 1997 and 2004. Moreover, the LAI presented a decreasing trend from suburban to downtown areas. We demonstrate the usefulness of TM remote-sensing in understanding spatiotemporal changing patterns of urban forest LAI under rapid urbanization and urban greening.     

Comparison of relascope and fixed-radius plots for the estimation of forest stand variables in northeast Spain: an inventory simulation approach

The Bitterlich relascope is a multiple use dendrometer widely used in forest inventory. Although it is most commonly used to estimate basal area, the relascope can also estimate other stand variables, including density and diameter distribution. However, forest stand inventories in Spain rarely use relascope plots to estimate these variables due to the belief that they lead to higher errors than fixed-radius plots due to the heterogeneity of many Mediterranean forests. This study compared the accuracy of the estimated averages of three main stand variables (basal area, stand density, and diameter class distribution) in forest stand inventories performed with relascope plots and with conventional fixed-radius circular plots, both measuring a similar number of trees (15–20). A forest stand inventory simulator (DOMO) was used (1) to generate simulated forest stands corresponding to the nine most common types in the Mediterranean region of Catalonia (NE Spain), including even-aged and uneven-aged stands, and (2) to estimate and compare the average values of these variables at the forest stand level resulting from both plot types. In general, we did not find significant accuracy differences between the inventory systems for most of the stand variables and forest types studied, as expected by established angle-count sampling theory. However, the results show that for stands with multiple strata and open structures, the Bitterlich relascope provides a more accurate estimate for basal area than for density, while the reverse occurs for fixed-radius plots.     

Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.     

Improving predictions of forest growth using the 3-PGS model with observations made by remote sensing

Measurements made by remote sensing can characterize the leaf area density and nitrogen/chlorophyll content of forest canopies, as well as maximum photosynthetic capacity and above-ground structure and biomass. Combining these with climate data estimated from relationships based on temperature measurements and using an appropriate process-based model, it is possible to calculate, with useful accuracy, carbon sequestration and wood production by different forest types covering large land areas. To broaden its application and reduce the need for detailed information on stand characteristics, a satellite-driven version of the model 3-PG, was developed. The 3-PGS model incorporates the major first-order physiological processes that determine forest growth, and the biophysical factors that affect and govern those processes. It incorporates remotely sensed estimates of seasonal variation in canopy light interception (fPAR) and includes physiological variables (stomatal conductance and canopy quantum efficiency) that can be estimated by remote-sensing measurements of factors that influence those variables. 3-PGS therefore provides a useful framework within which to evaluate how data from the array of airborne and satellite-borne sensors now available might be used to initialize, drive, and test process-based growth models across regions with diverse soils and climates. We address the question: to what extent might additional remote-sensing techniques improve 3-PGS predictions?Sensitivity analyses indicate that model accuracy would be most improved through better estimates of seasonal changes in canopy photosynthetic capacity (α) and canopy conductance (G_c). Canopy photosynthetic capacity depends on the amount of light absorbed by the canopy, estimated as a fraction of photosynthetically active radiation (fPAR), and on foliage nitrogen or chlorophyll content, which can be estimated using multi-spectral imagery. G_c depends on canopy leaf area index (L) and stomatal conductance of the foliage (g_s), which is affected by the vapor pressure deficit of the air and soil water content. The onset and effects of drought can be determined from changes in canopy reflectance and fPAR identified from sequential measurements; the same measurements, coupled with calculations of evapotranspiration using climatic data and standard formulae, provide estimates of total available water in forest root zones. Periodic surveys with Light Detection and Ranging (LiDAR) and interferometric RADAR may serve to validate model predictions of above-ground growth (NPP_A), while progressive reduction in light-use efficiency (NPP_A/APAR) may identify forests with declining vigor that are likely to succumb to attack from insects and pathogens.     

Effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery – Case study in Norway Spruce-dominated stands in Southern Finland

The effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Airborne thermal imagery, airborne scanning light detection and ranging (LiDAR) data and 92 field-measured sample plots were acquired at the area of interest. The surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and the logarithm of basal area at a resolution of 254?m² (9?m radius). LiDAR-derived metrics: the standard deviations of the canopy heights, canopy height (upper percentiles and maximum height) and canopy cover percentage were most strongly negatively correlated with the surface temperature. Although forest structure has an effect on the detected surface temperature, higher temperatures were detected in severely defoliated canopies and the difference was statistically significant. We also found that the surface temperature differences between the segmented canopy and the entire plot were greater in the defoliated plots, indicating that thermal images may also provide some additional information for classifying forests health status. Based on our results, the effects of forest structure on the surface temperature captured by airborne thermal imagery should be taken into account when developing forest health mapping applications using thermal imagery.     

基于CASIO fx-5800P计算器的森林资源规划设计调查蓄积量计算

基于CASIOfx-5800P计算器的可编程功能,以及其所具有的便携性、持久续航性和广泛应用性等特点,实现新一轮森林资源规划设计调查相关蓄积的自动化计算,蓄积计算类型包括标准地蓄积、控制样地蓄积和小班蓄积计算。本文分别给出三种蓄积计算的全部程序代码和使用说明。     

Characterizing rangeland vegetation using Landsat and 1-mm VLSA data in central Wyoming (USA)

As an alternative to ground-cover data collection by conventional and expensive sampling techniques, we compared measurements obtained from very large scale aerial (VLSA) imagery for calibrating moderate resolution Landsat data. Using a grid-based sampling scheme, 162 VLSA images were acquired at 100 m above ground level. The percent vegetation cover in each photo was derived using SamplePoint (a manual inventory method) and VegMeasure (a reflectance based, automated method). Approximately two-thirds of the VLSA images were used for calibrating Landsat data while the remainder was used for validation. Regression models with Landsat bands accounted for 55% of the VegMeasure-based measurements of vegetation, whereas models that included both Landsat bands and elevation data accounted for 67%. The relationship between the Landsat bands and the percent vegetation cover measured by SamplePoint was lower (R ² = 20%), highlighting the differences between the inventory and reflectance based protocols. Results from the model validation indicated that the model’s predictive power was lower when the vegetation cover was either <20% or >55%. Additional work is needed in these ecosystems to improve the calibration techniques for sites with low and high vegetation cover; however, these results demonstrate the VLSA imagery could be used for calibrating Landsat data and deriving rangeland vegetation cover. By adopting such methodologies the US Federal land management agencies can increase the efficiency of the monitoring programs in Wyoming and in other western states of the US. Mention of trade names is for information only and does not imply endorsement by USDA over comparable products or services.     

辽东山区不同林龄落叶松林分林木各器官生物量分配特征

以辽东山区落叶松人工林为研究对象,采用样地调查和实测生物量等方法,测定落叶松幼龄林、中龄林和近熟林的生物量及其在一个年龄序列上的空间分配特征。结果表明:不同林龄落叶松林分生物量分布依次为中龄林(119.39t·hm~(-2))近熟林(94.69t·hm~(-2))幼龄林(31.44t·hm~(-2))。各器官生物量大小关系略有差异,中龄林和近熟林为树干树根树枝树叶;而幼龄林为树干树枝树根树皮树叶。落叶松人工林经营应定期采取抚育间伐,改善林木生长条件,提高落叶松人工林的生产力,以实现生态系统健康、稳定发展。     

Satellite remote sensing for forest inventory—experiences in the nordic countries

Satellite data were used in developing methods for forest inventory and mapping in a cooperation project involving Finland, Norway and Sweden. A goal was to improve the technical competence in forestry remote sensing in those countries. Landsat TM‐ and simulated SPOT‐imageries were classified using, e.g., filtered input data and contextual classifiers. The relative area distribution of usual forest classes was estimated at an acceptable accuracy. A two‐phase sampling scheme was introduced for compartmentwise (in‐place) inventories. The first phase involves analysis of satellite data, the second phase measurement of field plots. Correlations between satellite‐ and field‐measured values of various forest characteristics were relatively high, close to those obtained using aerial photo interpretation.     

Efficiency of inventory plot patterns in quantitative analysis of vegetation: a case study of tropical woodland and dense forest in Benin

The main issue in forest inventory is the reliability of data collected, which depends on the shape and size of inventoried plots. There is also a need for harmonisation of inventoried plot patterns in West Africa. This study focused on the impact of plot patterns on the quantitative analysis of two vegetation types of West Africa based on case studies from Benin. Twenty and fifteen plots of 1 ha each were demarcated in dense forest and woodland, respectively. Each 1 ha plot was divided into 100 quadrats of 100 m² each and diameter at breast height (dbh) of trees was recorded in each quadrat. The required time to measuring trees diameter in each 1 ha plot was also recorded to compute the mean inventory effort. From the 100 quadrats in each 1 ha plot, 14 subplots of different shapes and sizes were considered by grouping together adjacent quadrats. The basal area of each subplot was computed and the relationship between estimation bias of the basal area and the size of subplots was modeled using Smith's Law (Smith 1938). The mean absolute error of the shape parameter c of Weibull distribution was computed for each of the subplot shape, size and direction. The direction and shape of subplots did not influence significantly (P > 0.05) the precision of the quantitative analysis of vegetation. However, square subplots were suitable in practice. On the contrary, plot size was significantly (P < 0.05) and inversely correlated to estimation efficiency. The optimal plot size for quantitative analysis of vegetation was 1 800 and 2 000 m2 with an inventory effort of 0.51 and 0.85 man-days per subplot in woodland and dense forest, respectively. It is concluded that use of standard sample sizes will help to harmonise a forestry database and to carry out comparisons at regional level.     

基于遥感地学模型的辽宁省森林生物量和碳储量估测

通过对森林植被生物量估测遥感模型机理的综合分析,采用2005年辽宁省森林资源连续清查部分样地数据,基于遥感及其派生信息、气象信息、地学信息、林分信息建立了估测森林植被生物量的多元回归模型。得出如下结论:1)根据与样地生物量相关性的高低对MODIS数据植被指数(NVDI)的排序为植被指数的年平均值(NVDI_AVER)、植被生长季节的植被指数的平均值(NVDI_AMD)、植被指数的年内最大值(NVDI_MAX)、植被指数的年内最小值(NVDI_MIN)、植被指数的年内最大值与最小值之间的差值(NVDI_CHA);与生物量相关的气象因子分别为相对蒸散、平均气温和>10℃的积温,其相关系数分别为0.422,0.399和0.394;生物量与坡向、纬度相关性不显著。2)辽宁省森林总生物量为255.774×106t,森林碳储量为127.887×106t。3)从辽宁省森林碳密度空间分布来看,辽宁省森林碳密度呈现出东高西低的趋势,并且碳密度总体上不高,为50 t/hm2以下。