Linear mixture model applied to Amazonian vegetation classification

Many research projects require accurate delineation of different secondary succession (SS) stages over large regions/subregions of the Amazon basin. However, the complexity of vegetation stand structure, abundant vegetation species, and the smooth transition between different SS stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier (MLC). Most of the time, classification distinguishes only between forest/non-forest. It has been difficult to accurately distinguish stages of SS. In this paper, a linear mixture model (LMM) approach is applied to classify successional and mature forests using Thematic Mapper (TM) imagery in the Rondônia region of the Brazilian Amazon. Three endmembers (i.e., shade, soil, and green vegetation or GV) were identified based on the image itself and a constrained least-squares solution was used to unmix the image. This study indicates that the LMM approach is a promising method for distinguishing successional and mature forests in the Amazon basin using TM data. It improved vegetation classification accuracy over that of the MLC. Initial, intermediate, and advanced successional and mature forests were classified with overall accuracy of 78.2% using a threshold method on the ratio of shade to GV fractions, a 7.4% increase over the MLC. The GV and shade fractions are sensitive to the change of vegetation stand structures and better capture biophysical structure information.     

A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t-test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s⁻¹), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m² m⁻²), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use.     

Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing

Floodplain roughness parameterization is one of the key elements of hydrodynamic modeling of river flow, which is directly linked to exceedance levels of the embankments of lowland fluvial areas. The present way of roughness mapping is based on manually delineated floodplain vegetation types, schematized as cylindrical elements of which the height (m) and the vertical density (the projected plant area in the direction of the flow per unit volume, m^− 1) have to be assigned using a lookup table. This paper presents a novel method of automated roughness parameterization. It delivers a spatially distributed roughness parameterization in an entire floodplain by fusion of CASI multispectral data with airborne laser scanning (ALS) data. The method consists of three stages: (1) pre-processing of the raw data, (2) image segmentation of the fused data set and classification into the dominant land cover classes (KHAT = 0.78), (3) determination of hydrodynamic roughness characteristics for each land cover class separately. In stage three, a lookup table provides numerical values that enable roughness calculation for the classes water, sand, paved area, meadows and built-up area. For forest and herbaceous vegetation, ALS data enable spatially detailed analysis of vegetation height and density. The hydrodynamic vegetation density of forest is mapped using a calibrated regression model. Herbaceous vegetation cover is further subdivided in single trees and non-woody vegetation. Single trees were delineated using a novel iterative cluster merging method, and their height is predicted (R² = 0.41, rse = 0.84 m). The vegetation density of single trees was determined in an identical way as for forest. Vegetation height and density of non-woody herbaceous vegetation were also determined using calibrated regression models. A 2D hydrodynamic model was applied with the results of this novel method, and compared with a traditional roughness parameterization approach. The modeling results showed that the new method is well able to provide accurate output data. The new method provides a faster, repeatable, and more accurate way of obtaining floodplain roughness, which enables regular updating of river flow models.     

利用植被覆盖度-地表温度特征空间提取厦门市土壤湿度信息

土壤湿度是水文学、气象学以及农业科学研究领域的一个重要参数。利用1989年和1996年两个时相Landsat TM遥感卫星影像来获取厦门的植被覆盖度和地表温度,并由此构建了植被覆盖度和地表温度组成的特征空间,最后利用该特征空间反演了土壤湿度,并分析湿度在两个时相中的变化情况。结果显示,在所研究的时间段内,厦门西南老城区的湿度得到增加。但是在东北部城市建成区扩展的地区,土壤湿度则明显降低。     

Spatial and temporal variability of macrophyte cover and productivity in the eastern Amazon floodplain: A remote sensing approach

Herbaceous aquatic macrophytes cover extensive areas on the floodplains of the Amazon basin and are an important habitat and input of organic carbon. These communities have large intra- and inter-annual variability, and characterization of this variability is necessary to quantify the role of macrophytes in the ecology and biogeochemistry of the floodplain. A novel approach for mapping the temporal evolution of aquatic vegetation in the Amazon floodplain, which could be adapted to other spatially and temporally changing environments, is presented. Macrophyte cover varied seasonally and inter-annually, ranging between 104 and 198 km² for the floodplain examined (total area, 984 km²). The observed evolution of plant distribution indicated a spatial and temporal partition of macrophyte communities into short-lived and annual groups. A simulation of macrophyte net primary production (NPP) based on the mapping results indicated that at least 3% of NPP could be attributed to the short-lived communities. The present results suggest that significant changes in the macrophyte's contribution to carbon cycling in the Amazon floodplain could occur as a result of the predicted increase in frequency of drought years for the Amazon system due to climate change.     

基于HJ-1高光谱数据的植被覆盖度估测方法研究

植被覆盖度是衡量地表植被状况的一个重要参数,在水文、生态等方面有重要意义,同时,也是影响土壤侵蚀与水土流失的主要因子,是评价土地荒漠化最有效的指标。以环境一号(HJ-1)小卫星上搭载的新型传感器HSI获取的高光谱数据为数据源,通过选择合适的植被指数建立了植被覆盖度反演模型——像元二分模型。然后运用该模型提取了新疆石河子地区的植被覆盖度信息。通过与地面样方数据进行交互比较,对HJ-1/HSI数据反演植被覆盖度的精度进行了评价。研究结果表明,HJ-1/HSI数据能够得到较高精度的植被覆盖度反演结果,在植被动态及全球变化研究领域具有潜在应用价值。     

Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions.Throughout the observation period (2001-2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3 months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Niño winter of 2002-2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area.     

Effects of soil composition and mineralogy on remote sensing of crop residue cover

The management of crop residues (non-photosynthetic vegetation) in agricultural fields influences soil erosion and soil carbon sequestration. Remote sensing methods can efficiently assess crop residue cover and related tillage intensity over many fields in a region. Although the reflectance spectra of soils and crop residues are often similar in the visible, near infrared, and the lower part of the shortwave infrared (400-1900 nm) wavelength region, specific diagnostic chemical absorption features are evident in the upper shortwave infrared (1900-2500 nm) region. Two reflectance band height indices used for estimating residue cover are the Cellulose Absorption Index (CAI) and the Lignin-Cellulose Absorption (LCA) index, both of which use reflectances in the upper shortwave infrared (SWIR). Soil mineralogy and composition will affect soil spectral properties and may limit the usefulness of these spectral indices in certain areas. Our objectives were to (1) identify minerals and soil components with absorption features in the 2000 nm to 2400 nm wavelength region that would affect CAI and LCA and (2) assess their potential impact on remote sensing estimates of crop residue cover. Most common soil minerals had CAI values ≤ 0.5, whereas crop residues were always > 0.5, allowing for good contrast between soils and residues. However, a number of common soil minerals had LCA values > 0.5, and, in some cases, the mineral LCA values were greater than those of the crop residues, which could limit the effectiveness of LCA for residue cover estimation. The LCA of some dry residues and live corn canopies were similar in value, unlike CAI. Thus, the Normalized Difference Vegetation Index (NDVI) or similar method should be used to separate out green vegetation pixels. Mineral groups, such as garnets and chlorites, often have wide ranges of CAI and LCA values, and thus, mineralogical analyses often do not identify individual mineral species required for precise CAI estimation. However, these methods are still useful for identifying mineral soils requiring additional scrutiny. Future advanced multi- and hyperspectral remote sensing platforms should include CAI bands to allow for crop residue cover estimation.     

Characterizing 3D vegetation structure from space: Mission requirements

Human and natural forces are rapidly modifying the global distribution and structure of terrestrial ecosystems on which all of life depends, altering the global carbon cycle, affecting our climate now and for the foreseeable future, causing steep reductions in species diversity, and endangering Earth's sustainability.To understand changes and trends in terrestrial ecosystems and their functioning as carbon sources and sinks, and to characterize the impact of their changes on climate, habitat and biodiversity, new space assets are urgently needed to produce high spatial resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass above ground, the carbon stored within and the implications for atmospheric green house gas concentrations and climate. These needs were articulated in a 2007 National Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives of this paper are to articulate the importance of these new, multi-year, 3D vegetation structure and biomass measurements, to briefly review the feasibility of radar and lidar remote sensing technology to meet these requirements, to define the data products and measurement requirements, and to consider implications of mission durations. The paper addresses these objectives by synthesizing research results and other input from a broad community of terrestrial ecology, carbon cycle, and remote sensing scientists and working groups. We conclude that:

(1)

Current global biomass and 3-D vegetation structure information is unsuitable for both science and management and policy. The only existing global datasets of biomass are approximations based on combining land cover type and representative carbon values, instead of measurements of actual biomass. Current measurement attempts based on radar and multispectral data have low explanatory power outside low biomass areas. There is no current capability for repeatable disturbance and regrowth estimates.

(2)

The science and policy needs for information on vegetation 3D structure can be successfully addressed by a mission capable of producing (i) a first global inventory of forest biomass with a spatial resolution 1 km or finer and unprecedented accuracy (ii) annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass accumulation rates at resolutions of 1 km or finer, and (iii) transects of vertical and horizontal forest structure with 30 m along-transect measurements globally at 25 m spatial resolution, essential for habitat characterization.

We also show from the literature that lidar profile samples together with wall-to-wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together to satisfy these vegetation 3D structure and biomass measurement requirements. Finally we argue that the technology readiness levels of combined pol-SAR and lidar instruments are adequate for space flight. Remaining to be worked out, are the particulars of a lidar/pol-SAR mission design that is feasible and at a minimum satisfies the information and measurement requirement articulated herein.