AGRO 500 Special Topics in Agronomy Remote Sensing Use in Agriculture and Forestry Lecture 6 Biomass Estimation Junming Wang Department of Plant and Environmental Sciences Skeen Hall, NMSU, Las Cruces

Internet link This lecture: http://hydrology1.nmsu.edu/Teaching_Material/Agro500/Agro500Lecture6Biomass.htm

Major reference Lu, D. 2006.The potential and challenge of remote sensing-based biomass estimation. Journal of Remote Sensing. Vol. 27. No. 7: 10 April 2006, 1297-1328

Why need to estimate biomass Carbon cycles, soil nutrient allocations, fuel accumulation, and habitat environments Fire risks and damage Vegetation recovery large-area deforestation resulted in effects on climate change, biological diversity, hydrological cycle, soil erosion and degradation

Biomass, in general, includes the above-ground and below-ground living mass, such as trees, shrubs, vines, roots, and the dead mass of fine and coarse litter associated with the soil. Below ground biomass is difficult to measure Remote sensing measures above ground biomass

Ground measurements are labor and time consuming and can not get spatial and large-area data. GIS-based methods using ancillary data are also difficult because of of problems in obtaining good quality ancillary data.

The advantages of remote sensing biomass: repetitivety of data collection, a synoptic view, a digital format that allows fast processing of large quantities of data, and the high correlations between spectral bands and vegetation parameters

Fine resolution data IKonos (0.8 m resolution), Quickbird (0.6 m) Oil palm biomass, Thenkabail et al. (2005) These data can be used for medium and large-scale remote sensing validation

Disadvantages of fine resolution data the high spectral variation and shadows caused by canopy and topography may create difficulty in developing biomass estimation models. Another drawback is the lack of a shortwave: infrared image, which is often important for biomass estimation, Also, the need for large data storage and the time required for image processing prohibit its application in large, much more expensive.

medium spatial-resolution Landsat (30-90 m resolutions)

The canopy reflectance saturated when AGB approached about 15 kg/ m2 or vegetation age reached over 15 years in the tropical secondary successional forests in Manaus, Brazil. (Steininger, 2000)

Spectral signature or vegetation indices are often used for AGB estimation. However, not all vegetation indices are significantly correlated with AGB.

Research in the moist tropical forest in the Brazilian Amazon has indicated the image textures are more important than spectral responses for AGB estimation in the forest sites with complex vegetation stand structures (Lu 2005, Lu and Batistella 2005)

In the forest sites with relatively simple vegetation stand structure, spectral signatures play a more important role than image textures.

Coarse spatial-resolution data Resolution above 100 m NOAA Advanced Very High Resolution Radiometer (AVHRR), SPOT VEGETATION, and Moderate Resolution Imaging Spectroradiometer (MODIS]. often used at national, continental, and global scales

Baccin et al. (2004) used MODIS data in combination with precipitation Baccin et al. (2004) used MODIS data in combination with precipitation. Temperature , and elevation for mapping AGB in national forest lands in California. US.

Overall, the AGB estimation using coarse spatial-resolution satellite data is very limited because of the common occurrence of mixed pixels and the huge difference between the size of field-measurement data and pixel size in the image, resulting in difficulty in the integration of sample data and remote sensing-derived variables.

The disadvantages of optical sensors the frequent cloud conditions and night time conditions often restrain the acquisition of high-quality remotely sensed data by optical sensors. radar data become the only feasible way acquiring remotely sensed data

Radar applications Forest-cover identification and mapping, discrimination of forest compartments and forest types, and estimation of forest stand parameters

Radar constraints the data analyses involved in pre-processing, removal of noise, and image processing require more skills, knowledge, and specific software. most radar data were captured through airborne sensors, which may be much more expensive in data collection than spaceborne images for a large area. Most previous research using radar or lidar data is sill limited in the typical study areas, and has not been applied extensively to AGB estimation with regional and global scales because of the cost and labor constraints.

AGB model Multiple regression analysis Identifying suitable variables for developing a multiple regression model is often difficult and time consuming because many potential variables may be used.: such as canopy structure, tree density, and tree species composition.

Change in AGB is not directly shown in change of reflectance. The optical sensors mainly capture canopy information, thus the optical sensor data may be more suitable for estimation or canopy parameters such as crown density than AGB.

Four main AGB model categories: geometrical models turbid medium models, hybrid models, and computer simulation models suitable for canopies with smaller leaves, high leaf area index and high zenith angles.

geometric-optical models the canopy is assumed to be an array of opaque or translucent sub-canopies of prescribed geometrical shapes (Goel, 1988). applied to semi-arid woodland vegetation structure (Franklin and Strahler 1988. Franklin and Turner 1992), moderately closed coniferous forest canopy (Li and Strahler 1985), and wood biomass estimation.

Turbid models The canopy is treated as a horizontally uniform plane-parallel layer and canopy architecture (God 1985). These models are suitable for the dense and horizontally uniform vegetated covers such as crops.

Hybrid model presents a hybrid approach between two or more approaches

Computer-simulation models simulate the arrangement and orientation of vegetation elements on a computer. Canopy architecture can be treated in more detail and more realistically than with other models.

Accuracy assessment root-mean-squared error (RMSE). R2

based on different levels, such as per-pixel level, per-field level or polygon level, and the total amount for the study area. Fazakas et al. (1999) estimated AGB using Landsat TM data and assessed accuracy at a grid-cell level and an aggregation of cells. They found that the accuracy of estimated AGB at a grid-ell level was poor, but the accuracy increased when aggregations of cells were evaluated.

Accuracy Around 50%-80%.

Estimated accuracy on per pixel level Assessment of AGB estimation results at a per-pixel level is often difficult, and the accuracy may be misleading due to the registration errors between field collection data and the image.

Estimated accuracy on large area is also difficult because of the difficulty in collecting AGB data in a large area using traditional methods.

The suitable method may be based on the per-field or polygon level. At this level, the AGB reference data may be derived from different methods, such as: the estimated AGB results from fine spatial-resolution data, such as aerial photographs and IKONOS.

Model transferability It is difficult to directly transfer one model to different study areas because of the limitation of the model itself and the nature of remotely sensed data. Each model has its limitation and optical scale for implementation. For example, when using a regression model, attention should be given lo understanding the applicable scale implemented in the original models.

accurate atmospheric correction between multi-temporal or multi-scene image data and similar biophysical environments in the study areas are critical for the model transfer. The remote sensing spectral signatures, vegetation indices, and image textures are often dependent on the image scale and environmental conditions. Caution must also be taken to assure the consistence between the images used in scale.

Discussion of important issues influencing biomass estimation Economic condition may be the most important factor affecting the implementation of field work, purchase of different sources of image data, and the time and number of professionals e.g., collection of sufficient number or biomass sample plots

Solutions to the economic constraints Data sharing, including field measurements, ancillary data, and different sources of remotely sensed data, among different research trams can greatly reduce the cost in data collection.

Limitation of remotely sensed data & potential solutions radiometric and atmospheric correction is an important but difficult task due to complex atmospheric conditions in time and space. In rugged or mountainous regions, topographic factors such as slope and aspect can considerably affect vegetation reflectance, resulting in a low accuracy. Hence, removal of topographic effects on vegetation reflectance is necessary.

Topographic corrections linear transformations such as principal component analysis or regression models (Pouch and Campagna 1990) DEM and remotely sensed data (Walsh et al. 1990, Franklin et al. 1994)

The relatively coarse radiometric resolution (e. g The relatively coarse radiometric resolution (e.g. 8-bit for Landsat TM data) encourages digital number (DN) value saturation due to similar stand structures, impacts of canopy shadowing, and topographic factors, even if the AGB varies in different sites.

Selection of suitable variables Many remote sensing variables, including spectral signatures, vegetation indices, transformed images, and image textures, may become potential variables for AGB estimation. Selection of suitable variables is a critical step for developing an AGB estimation model, because some variables are weakly correlated with AGB or they have high correlation each other.

Modeling methods Regression models have been commonly used. There was no comparison for different models

Conclusions and perspectives Current models have low accuracy Remote sensing-based AGB estimation is a complex procedure in which many factors, such as atmospheric conditions, mixed pixels, data saturation, complex biophysical environments, insufficient sample data, extracted remote sensing variables, and the selected algorithms, may interactively affect AGB estimation performance.

Potential solutions (1) accurate atmospheric calibration to reduce the impacts of uncertainty caused by the different atmospheric conditions; (2) selection of suitable vegetation indices and image textures to reduce the impacts of environmental conditions and canopy shadows; (3) the integration of optical and radar data to reduce the data saturation in optical-sensor images;

(4) the integration of multi-source data

Assignment What are the major remote-sensing methods for estimating above-ground biomass? What is the accuracy of these methods? Due on next Monday. You can email me your anwers.