Estimation of fire severity by use of Landsat TM images and its relevance to vegetation and topography in the 2000 Samcheok forest fire

The severity of the 2000 Samcheok forest fire was classified by using Landsat TM images, and the effects of vegetation structures and topographic conditions on fire severity were analyzed. The estimated normalized difference vegetation index differences between the pre and post-fire Landsat TM images were used as the criteria in determining the levels of fire severity–low, moderate, and extreme. According to the results from fire severity estimation, of the 10,600 ha forest stands, 28% was severely damaged by crown fires, 38% was moderately damaged, and the remaining 34% was damaged slightly by surface fires. The overall accuracy of the fire severity classification was 83% (Kappa coefficient = 0.76). The results of χ ²-tests showed that fire severity differed significantly with the vegetation and topographic conditions as follows. The coniferous stands, compared with the mixed and broad-leaved, were more vulnerable to fire damage; the higher the slope of fire sites, the greater the fire damage; the south was the most vulnerable aspect; fire severity of coniferous forest stands increased with increasing elevation. However, in the study area it was found that fire severity of broad-leaved forest stands were negatively related to the elevation of the corresponding fire sites and affected more by vegetation conditions rather than by topographic conditions.     

Land cover mapping of deciduous forest regions using ETM+ data: a case study of Azerbaijan Province,Iran

Up to date information about the existing land cover patterns and changes in land cover over time is one of the prime prerequisites for the preparation of an integrated development plan and economic development program of a region. By using ETM+ image data from 2002, we provided a land cover map of deciduous forest regions in Azerbaijan Province, Iran. Initial qualitative evaluation of the data showed no significant radiometric errors. Image classification was carried out using a maximum likelihood-based supervised classification method. In the end, we determined five major land cover classes, i.e., grass lands, deciduous broad-leaf forest, cultivated land, river and land without vegetation cover. Accuracy, estimated by the use of criteria such as overall accuracy from a confusion matrix of classification was 86% with a 0.88 Kappa coefficient. Such high accuracy results demonstrate that the combined use of spectral and textural characteristics increased the number of classes in the field classification, also with excellent accuracy. The availability and use of time series of remote sensing data permit the detection and quantification of land cover changes and improve our understanding of the past and present status of forest ecosystems.     

Estimation of forest stem volume using multispectral optical satellite and tree height data in combination

Abstract

The accuracy of forest stem volume estimation at stand level was investigated using multispectral optical satellite and tree height data in combination. The stem volumes for the investigated coniferous stands, located in southern Sweden, were in the range of 15–585 m³ ha^?1 with an average stem volume of 266 m³ ha^?1. The results from regression analysis showed a substantial improvement for the combined stem volume estimates compared with using satellite data only. The accuracy in terms of root mean square error (RMSE) was calculated to 11.2% of the average stem volume using SPOT-4 data and tree height data in combination compared with 23.9% using SPOT-4 data only. By replacing SPOT-4 data with Landsat TM data the RMSE was improved from 25.2% to 12.2%. In addition, a sensitivity analysis was performed on the combined stem volume estimates by adding random errors, normally distributed with zero expectations, with standard deviations of 1, 1.5 and 2 m to tree height data. The results showed that the RMSE increased with increasing random tree height error to 15.4%, 18.0% and 19.9% using SPOT-4 data and 16.3%, 19.2% and 21.2% using Landsat TM data. The results imply that multispectral optical satellite data in combination with accurate tree height data could be used for standwise stem volume estimation in forestry applications.     

Assessing rates of forest change and fragmentation in Alabama,USA, using the vegetation change tracker model

Forest change is of great concern for land use decision makers and conservation communities. Quantitative and spatial forest change information is critical for addressing many pressing issues, including global climate change, carbon budgets, and sustainability. In this study, our analysis focuses on the differences in geospatial patterns and their changes between federal forests and nonfederal forests in Alabama over the time period 1987–2005, by interpreting 163 Landsat Thematic Mapper (TM) scenes using a vegetation change tracker (VCT) model. Our analysis revealed that for the most part of 1990 s and between 2000 and 2005, Alabama lost about 2% of its forest on an annual basis due to disturbances, but much of the losses were balanced by forest regeneration from previous disturbances. The disturbance maps revealed that federal forests were reasonably well protected, with the fragmentation remaining relatively stable over time. In contrast, nonfederal forests, which are predominant in area share (about 95%), were heavily disturbed, clearly demonstrating decreasing levels of fragmentation during the time period 1987–1993 giving way to a subsequent accelerating fragmentation during the time period 1994–2005. Additionally, the identification of the statistical relationships between forest fragmentation status and forest loss rate and forest net change rate in relation to land ownership implied the distinct differences in forest cutting rate and cutting patterns between federal forests and nonfederal forests. The forest spatial change information derived from the model has provided valuable insights regarding regional forest management practices and disturbance regimes, which are closely associated with regional economics and environmental concerns.     

Assessing impact of climate change on forest cover type shifts in Western Himalayan Eco-region

Climate is a critical factor affecting forest ecosystems and their capacity to produce goods and services. Effects of climate change on forests depend on ecosystem-specific factors including dimensions of climate (temperature, precipitation, drought, wind etc.). Available information is not sufficient to support a quantitative assessment of the ecological, social and economic consequences. The present study assessed shifts in forest cover types of Western Himalayan Eco-region (700?4500 m). 100 randomly selected samples (75 for training and 25 for testing the model), genetic algorithm of rule set parameters and climatic envelopes were used to assess the distribution of five prominent forest cover types (Temperate evergreen, Tropical semi-evergreen, Temperate conifer, Subtropical conifer, and Tropical moist deciduous forests). Modelling was conducted for four different scenarios, current scenario, changed precipitation (8% increase), changed temperature (1.07°C increase), and both changed temperature and precipitation. On increasing precipitation a downward shift in the temperate evergreen and tropical semi-evergreen was observed, while sub-tropical conifer and tropical moist-deciduous forests showed a slight upward shift and temperate conifer showed no shift. On increasing temperature, an upward shift in all forest types was observed except sub-tropical conifer forests without significant changes. When both temperature and precipitation were changed, the actual distribution was maintained and slight upward shift was observed in all the forest types except sub-tropical conifer. It is important to understand the likely impacts of the projected climate change on the forest ecosystems, so that better management and conservation strategies can be adopted for the biodiversity and forest dependent community. Knowledge of impact mechanisms also enables identification and mitigation of some of the conditions that increase vulnerability to climate change in the forest sector.     

Surface temperature change of spruce forest as a result of bark beetle attack: remote sensing and GIS approach

In the 1990s, a bark beetle (Ips typographus [L.]) infection caused the decay of spruce forest (Picea abies [L.] Karst.) in the central part of the Šumava Mountains, the Czech Republic, bordering the Bavarian Forest National Park, Germany, where the bark beetle infection started in the late 1980s. Some areas were left without human intervention and, consequently, the trees around these areas were removed to stop further bark beetle outbreak. The objective of our study was the assessment of surface temperature (ST) change in spruce forest decayed under bark beetle and following clear-cutting. The change detection of ST is based on the comparison of modelled values and thermal satellite data. For this purpose, Landsat scenes from July 11th, 1987 and July 28th, 2002 were used. The models describe the dependence of ST of living spruce forest on topography. The topography effect is based on the Altitude and Hillshade index, which expresses the influence of Aspect and Slope on the relief illumination. Then the modelled ST values were extrapolated for decayed spruce forest and clear-cut areas. In order to increase model accuracy, the forest edge zones (90 m wide) were removed because of their different energy balance; then explained variability value (R ²) increased from 0.37 to 0.55. The results of comparing modelled values with satellite ST in the decayed spruce forest and clear-cut areas show an average increase of ST by 5.2 and 3.5°C, respectively. The thermal satellite data from 1987 were used for model validation. This showed that the accuracy of ST modelling using topography was sufficient, because the difference between the modelled ST with and without decayed spruce forest and clear-cut areas was at most only 0.4°C.     

Inclusion of forest harvest legacies, forest type, and regeneration spatial patterns in updated forest maps: A comparison of mapping results

In Maine and other heavily forested states, existing land cover maps quickly become dated due to forest harvesting and land use conversion; therefore, these maps may not adequately reflect landscape properties and patterns relevant to current resource management and ecosystem studies. By updating an older land cover product (the 1993 Maine GAP map) using Landsat imagery and established forest change detection techniques, we demonstrate a practical and accurate means of providing contemporary, spatially explicit forest cover data needed to quantify landscape change. For a 1.8 million hectares study area in northern Maine, we quantify the accuracy of forest harvest classes and compare mapped harvest and regeneration area between the 2004 GAP update product and the 2004 Maine Landcover Dataset (MeLCD), a map recently developed in coordination with the 2001 National Land-Cover Database (NLCD). For the period 1995–2004, the overall harvest/non-harvest accuracy of the GAP update map is 87.5%, compared to 62.1% for the MeLCD. Producer and user accuracy for harvest detection is 92.4% and 89.7%, respectively for the GAP update, and 48.8% and 92.5% for the MeLCD. Mapped harvest area differs considerably, reflecting a systematic under-representation of recent harvest activity on the part of the MeLCD. By integrating older land cover data, the GAP update retains the forest disturbance legacies of the late 1970s through the early 1990s while simultaneously depicting 2004 forest composition for harvested and regenerating stands. In contrast, the MeLCD (and 2001 NLCD) over-represents the area and connectivity of older forest (undisturbed since the late 1970s), and provides no forest composition information for mapped forest regeneration. Systematic misclassification of forest age classes and harvest history has serious implications for studies focused on wildlife habitat modeling, forest inventory, and biomass or carbon stock estimation. We recommend the integration of older land cover data and time-series forest change detection for retention of harvest or disturbance classes when creating new forest and land cover maps.     

Composition,structure and diversity characterization of dry tropical forest of Chhattisgarh using satellite data

The purpose of this study was to characterize the land use, vegetation structure, and diversity in the Barnowpara Sanctuary, Raipur district, Chhattisgarh, India through the use of satellite remote sensing and GIS. Land cover and vegetation were spatially analyzed by digitally classifying IRS 1D LISS III satellite data using a maximum likelihood algorithm. Later, the variations in structure and diversity in different forest types and classes were quantified by adopting quadratic sampling procedures. Nine land-cover types were delineated: teak forest, dense mixed forest, degraded mixed forest, Sal mixed forest, open mixed forest, young teak plantation, grasslands, agriculture, habitation, and water bodies. The classification accuracy for different land-use classes ranged from 71.23% to 100%. The highest accuracy was observed in water bodies and grassland, followed by habitation and agriculture, teak forest, degraded mixed forest, and dense mixed forest. The accuracy was lower in open mixed forest, and sal mixed forest. Results revealed that density of different forest types varied from 324 to 733 trees ha-1, basal area from 8.13 to 28.87 m2·ha-1 and number of species from 20 to 40. Similarly, the diversity ranged from 1.36 to 2.98, concentration of dominance from 0.06 to 0.49, species richness from 3.88 to 6.86, and beta diversity from 1.29 to 2.21. The sal mixed forest type recorded the highest basal area, diversity was highest in the dense mixed forest, and the teak forest recorded maximum density, which was poor in degraded mixed forests. The study also showed that Normalized Difference Vegetation Index(NDVI) was strongly correlated to with the Shannon Index and species richness.     

Post-1935 changes in forest vegetation of Grand Canyon National Park, Arizona, USA: Part 2—Mixed conifer, spruce-fir, and quaking aspen forests

This study examined changes in never-harvested mixed conifer (MCF), spruce-fir (SFF), and quaking aspen forests (QAF) in Grand Canyon National Park (GCNP), Arizona, USA based on repeat sampling of two sets of vegetation study plots, one originally sampled in 1935 and the other in 1984. The 1935 plots are the earliest-known, sample-intensive, quantitative documentation of forest vegetation over a Southwest USA landscape. Findings documented that previously described increases in densities and basal areas attributed to fire exclusion were followed by decreases in 1935-2004 and 1984-2005. Decreases in MCF were attributable primarily to quaking aspen (Populus tremuloides) and white fir (Abies concolor), but there were differences between dry-mesic and moist-mesic MCF subtypes. Decreases in SFF were attributable to quaking aspen, spruce (Picea engelmannii + Picea pungens), and subalpine fir (Abies lasiocarpa). Decreases in QAF resulted from the loss of quaking aspen during succession. Changes in ponderosa pine forest (PPF) are described in a parallel paper (Vankat, J.L., 2011. Post-1935 changes in forest vegetation of Grand Canyon National Park, Arizona, USA: part 1 - ponderosa pine forest. Forest Ecology and Management 261, 309-325). Graphical synthesis of historical and modern MCF data sets for GCNP indicated tree densities and basal areas increased from the late 19th to the mid 20th century and then decreased to the 21st century. Changes began earlier, occurred more rapidly, and/or were larger at higher elevation. Plot data showed that basal area decreased earlier and/or more rapidly than density and that decreases from 1935 to 2004 resulted in convergence among MCF, SFF, and PPF. If GCNP coniferous forests are trending toward conditions present before fire exclusion, this implies density and basal area were more similar among these forests in the late 19th century than in 1935. Changes in MCF and SFF can be placed in a general framework of forest accretion, inflection, and recession in which increases in tree density and basal area are followed by an inflection point and decreases. Accretion was triggered by the exogenous factor of fire exclusion, and inflection and recession apparently were driven by the endogenous factor of density-dependent mortality combined with exogenous factors such as climate. Although the decreases in density and basal area could be unique to GCNP, it is likely that the historical study plots provided a unique opportunity to quantitatively determine forest trends since 1935. This documentation of post-1935 decreases in MCF and SFF densities and basal areas indicates a shift in perspective on Southwestern forests is needed.     

塞罕坝国家级森林公园风景游憩林类型与特征研究

本研究基于多尺度、多因素植被类型分类法探讨了塞罕坝国家级森林公园风景游憩林资源的类型划分,通过典型风景游憩林林分样地调查、季相景观照片的获取、解析与视觉设计,对不同类型风景游憩林林分及景观特征进行了系统分析,得出以下结论和建议:(1)塞罕坝国家级森林公园风景游憩林资源丰富、类型多样,可从林分和景观两个尺度进行分类。(2)森林公园不同类型风景游憩林林分,其林相及林内景观特征各异。(3)塞罕坝森林公园处于森林和草原、山丘与高原的交接带,地理位置优势明显,景观类型丰富多样。(4)建议保护天然次生林景观,同时通过栽植乡土阔叶树种,合理营造针阔混交色彩层次丰富的风景游憩林。(5)对于大尺度的森林景观的人工采伐痕迹,应从视域角度予以调整,通过调节视域范围,适当遮挡屏蔽不良景观、展现优美景观。     

Post-1935 changes in forest vegetation of Grand Canyon National Park, Arizona, USA: Part 1 - ponderosa pine forest

Vegetation plots originally sampled in Grand Canyon National Park (GCNP), Arizona, USA in 1935 are the earliest-known, sample-intensive, quantitative documentation of forest vegetation over a Southwest USA landscape. These historical plots were located as accurately as possible and resampled in 2004 to document multi-decadal changes in never-harvested Southwestern forests. Findings for ponderosa pine forest (PPF) differed among three forest subtypes (dry, mesic, and moist PPF), indicating that understanding the ecology of PPF subtypes is essential for development of ecologically based management practices. Dry PPF, which is transitional with pinyon-juniper vegetation at low elevation, exhibited no changes from 1935 to 2004. Mesic PPF, the core subtype of PPF, had increased densities of total trees, ponderosa pine (Pinus ponderosa), and white fir (Abies concolor) in the 10-29.9 cm diameter class from 1935 to 2004 that may have induced decreased densities of larger ponderosa pines and total tree and ponderosa pine basal areas. Moist PPF, which is transitional with mixed conifer forest at high elevation, was the most dynamic PPF subtype with decreases from 1935 to 2004 in total density and total basal area that are largely attributable to decreases in quaking aspen (Populus tremuloides). Graphical synthesis of datasets with historical and modern values for density and basal area indicates that overall PPF (all subtypes combined) increased in sapling density of all species combined and conifers with canopy potential and decreased in density of quaking aspen trees since the late 19th century. PPF of GCNP has passed through an accretion phase of forest development with increases in density and, depending on PPF subtype and variable being examined, is at or past the point of inflection to recession of density and basal area. Increases in small diameter ponderosa pine and white fir from 1935 to 2004 portend potential additional accretion, but decreases in total basal area, density and basal area of quaking aspen, basal area of ponderosa pine, and density of larger diameter ponderosa pine indicate PPF has passed the inflection point from accretion to recession. Uncertainties about 19th-century PPF structure and composition and about future ecological and societal environments lead to the conclusion that resource managers of GCNP and other natural areas should consider a change in focus from the objective of achieving desired future conditions to an objective of avoiding undesired future conditions.     

Evaluation of environmental functions of tropical forest in Kinabalu Park, Sabah, Malaysia using GIS and remote sensing techniques: Implications to forest conservation planning

Environmental functions of tropical forest can serve as criteria for forest conservation planning in the tropics. The objective of this study is to evaluate the environmental functions of tropical forest in Kinabalu Park, Sabah, Malaysia, using GIS and remote sensing techniques. Field data, statistical data, including weather data with geographic localities, maps and satellite image are collected. Linear regression models are developed for forests of different geological substrates, based on the relationships between altitude and biodiversity (Fisher’s alpha index). Biodiversity conservation function map is derived with the statistical models and a digital elevation model. Coupling with extensive literature review, an evaluation matrix for evaluating soil and water conservation functions including landslide prevention, flood prevention and drought prevention functions, is constructed. To evaluate the soil and water conservation functions, a weighted linear combination method is used with GIS layers of topography, geology, soil depth, rainfall and slope. Forest areas in Kinabalu Park are derived with land cover mapping using Landsat-TM image. Areas having high values of biodiversity conservation, flood and drought prevention functions are covered with mainly lowland rain forest. On the other hand, areas with high values of the landslide prevention function are covered with mainly subalpine forests. Using the environmental functions, a conservation index is computed to represent forests that are important to conservation. Based on theCI, the lowland rain forest receives highest priority in protection. In fact, it is located in the boundary areas of the park and thus exposed to illegal activities. This study would not be possibly accomplished without the map data as well as other data.     

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.     

Trends in land cover change and isolation of protected areas at the interface of the southern boreal mixedwood and aspen parkland in Alberta, Canada

The Beaver Hills region of central Alberta is located at the interface of the southern boreal mixedwood forest and the aspen parkland, an area now dominated by agriculture, urban and industrial development. Increasing anthropogenic land cover will eventually isolate remaining natural habitats currently protected in parks and reserves. This paper analyzes land cover and land cover change (LCC) in the Beaver Hills moraine and surrounding areas using a structured hierarchical satellite imagery classification applied to Landsat Multi Spectral Scanner and Thematic Mapper from 1977, 1987, and 1998. Our goal was to quantify deforestation and habitat fragmentation trends and then discuss how this information could be used to develop a conservation approach that will protect current areas against further habitat loss. We found that the rate of deforestation in the lands surrounding the moraine was similar to the broad trend at the southern periphery of the Canadian boreal forest region: annual rate of change in forest cover was −0.82%/year. However, in the Beaver Hills there was a net gain of +0.61%/year, due to regeneration of low quality agricultural lands. All fragmentation indices used indicated an increase in forest fragmentation. We designed a network of protected areas and remaining large forest patches, based on the UNESCO-MAB biosphere model. Our results underline concerns regarding the increasing isolation of national parks and biological reserves in Canada.     

Evaluating the Impacts of Forest Management Policies and Community-Level Institutions in the Buffer Zone of Chitwan National Park,Nepal

A Master Plan for Nepal’s Forestry Sector (MPFS), enacted in 1989, and subsequent legislation laid the foundation for modern community-based forest management in Nepal. In 2014, the MPFS reached the end of its 25-yr lifespan, after successfully ushering in significant institutional changes that fundamentally transformed the management of Nepal’s forests, mostly through devolving management and benefits from the national level to local communities. Here, we use the 25-yr anniversary of the MPFS to explore forest cover trends in the buffer zone surrounding Chitwan National Park (CNP). Landsat imagery was used for the years 1989, 2005, and 2013 to compute a normalized difference vegetation index (NDVI) to analyze trends in forest cover for 36 buffer zone village development committees (VDCs). The analysis, covering approximately 1,267 km², found that since the MPFS was enacted, there was first a continued decrease in forest cover, followed by a significant recovery. These data offer insight into the success of modern community-based forest management policies and supporting institutions, and provide a model for other efforts to conserve forest resources in Nepal and elsewhere.     

孟加拉国Bhanugach西部保育林用地类型变化(英文)

应用遥感和地理信息系统调查了孟加拉国Sylhet林业分局所辖的Bhanugach西部保育林从1988年到2006年间用地类型的变化。应用了监督和非监督两种方法,用全球定位系统搜集了地面控制点。地理信息系统软件制成地图。结果表明,植被覆盖从1988年的1826hm2大幅地降低到1996年的1714.85hm2,随着协同管理措施的实施,植被覆盖自1996年后逐年增长到2006年的1847.83hm2。在没有其他类型的用地转变成裸地的情况下,裸地变化与植被覆盖间成反比。18年间水体面积从1988年的307.67hm2增加到1996年379.53hm2,96-97年间略有下降,1997年以后直到2006年基本保持不变。本文还提出了将遥感技术和地理信息系统应用于用地类型变化及地理信息系统数据库创建的一些建议。     

浙江天童国家森林公园常绿阔叶林主要组成树种材积与生物量相关关系探讨

以天童国家森林公园常绿阔叶林主要组成树种为研究对象,通过测定其各组分生物量,建立了实测生物量与材积之间的回归模型,结果表明:主要组成树种各组分生物量与其材积之间存在着密切的相关关系,各主要树种材积与生物量的回归模型存在差异;回归方程精度较高,用于根据地区常绿阔叶林主要树种材积推算其生物量是可行的。     

Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data

Conversion of tropical forests to oil palm plantations in Malaysia and Indonesia has resulted in large-scale environmental degradation, loss of biodiversity and significant carbon emissions. For both countries to participate in the United Nation’s REDD (Reduced Emission from Deforestation and Degradation) mechanism, assessment of forest carbon stocks, including the estimated loss in carbon from conversion to plantation, is needed. In this study, we use a combination of field and remote sensing data to quantify both the magnitude and the geographical distribution of carbon stock in forests and timber plantations, in Sabah, Malaysia, which has been the site of significant expansion of oil palm cultivation over the last two decades. Forest structure data from 129 ha of research and inventory plots were used at different spatial scales to discriminate forest biomass across degradation levels. Field data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) imagery to both discriminate oil palm plantation from forest stands, with an accuracy of 97.0% (κ = 0.64) and predict AGB using regression analysis of HV-polarized PALSAR data (R² = 0.63, p < .001). Direct estimation of AGB from simple regression models was sensitive to both environmental conditions and forest structure. Precipitation effect on the backscatter data changed the HV prediction of AGB significantly (R² = 0.21, p < .001), and scattering from large leaves of mature palm trees significantly impeded the use of a single HV-based model for predicting AGB in palm oil plantations. Multi-temporal SAR data and algorithms based on forest types are suggested to improve the ability of a sensor similar to ALOS PALSAR for accurately mapping and monitoring forest biomass, now that the ALOS PALSAR sensor is no longer operational.