Quazi K. Hassan

Spatial mapping of growing degree days: an application of MODIS-based surface temperatures and enhanced vegetation index

Growing degree days (GDD) is a simple temperature-based index of biological development. In this paper we evaluated the potential of using 2003-2005 MODIS-based 8-day and 16-day composites of daytime surface temperature (TS) and enhanced vegetation index (EVI) values at 250 m resolution for mapping GDD. The work was applied to the Canadian Atlantic Maritime Ecozone as a demonstration of the methodology. The work proceeded by establishing an empirical relationship between mean tower-based estimates of TS for the MODIS-acquisition period of 10:30 am-12:00 pm and the daily mean TS calculated from half-hourly emitted infrared/longwave radiation measurements taken from four flux sites in southern commercial forests of Canada. The relationship revealed a strong correlation between variables (r2=98.4%) and was central to the calculation of daily mean TS from MODIS-based estimates of TS. Since seasonally-based estimates of GDD and EVI were strongly correlated (r2=87%), data fusion techniques were applied to enhance the GDD map originally produced at 1 km resolution (from infrared emission band data), to 250 m. In general, the MODIS-derived map of GDD showed a positive constant offset of about 511 degree days from calculated long-term averages (1971 2000) based on temperatures collected at 101 Environment Canada climate stations.

A Wetness Index Using Terrain-Corrected Surface Temperature and Normalized Difference Vegetation Index Derived from Standard MODIS Products: An Evaluation of Its Use in a Humid Forest-Dominated Region of Eastern Canada

In this paper we develop a method to estimate land-surface water content in a mostly forest-dominated (humid) and topographically-varied region of eastern Canada. The approach is centered on a temperature-vegetation wetness index (TVWI) that uses standard 8-day MODIS-based image composites of land surface temperature (TS) and surface reflectance as primary input. In an attempt to improve estimates of TVWI in high elevation areas, terrain-induced variations in TS are removed by applying grid, digital elevation model-based calculations of vertical atmospheric pressure to calculations of surface potential temperature (θS). Here, θS corrects TS to the temperature value to what it would be at mean sea level (i.e., ∼101.3 kPa) in a neutral atmosphere. The vegetation component of the TVWI uses 8-day composites of surface reflectance in the calculation of normalized difference vegetation index (NDVI) values. TVWI and corresponding wet and dry edges are based on an interpretation of scatterplots generated by plotting θS as a function of NDVI. A comparison of spatially-averaged field measurements of volumetric soil water content (VSWC) and TVWI for the 2003-2005 period revealed that variation with time to both was similar in magnitudes. Growing season, point mean measurements of VSWC and TVWI were 31.0% and 28.8% for 2003, 28.6% and 29.4% for 2004, and 40.0% and 38.4% for 2005, respectively. An evaluation of the long-term spatial distribution of land-surface wetness generated with the new θS-NDVI function and a process-based model of soil water content showed a strong relationship (i.e., r2 = 95.7%).

Potential Species Distribution of Balsam Fir Based on the Integration of Biophysical Variables Derived with Remote Sensing and Process-Based Methods

Abstract: In this paper we present a framework for modelling potential species distribution (PSD) of balsam fir [bF; Abies balsamea (L.) Mill.] as a function of landscape-level descriptions of: (i) growing degree days (GDD: a temperature related index), (ii) land-surface wetness, (iii) incident photosynthetically active radiation (PAR), and (iv) tree habitat suitability. GDD and land-surface wetness are derived primarily from remote sensing data acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite. PAR is calculated with an existing spatial model of solar radiation. Raster-based calculations of habitat suitability and PSD are obtained by multiplying normalized values of species environmental-response functions (one for each environmental variable) parameterized for balsam fir. As a demonstration of the procedure, we apply the calculations to a high bF-content area in northwest New Brunswick, Canada, at 250-m resolution. Location of medium-to-high habitat suitability values (i.e., >0.50) and actual forests, with >50% bF, matched on average 92% of the time.

Application of Landsat-7 ETM+ and MODIS products in mapping seasonal accumulation of growing degree days at an enhanced resolution

This paper describes a procedure for mapping long-term average, growing season-accumulated growing degree days at an enhanced spatial resolution of 28.5 m. GDD-product enhancement is based on augmenting a previously developed 1 km resolution map of GDD described in Hassan et al. [J. Applied Remote Sens., 1, 013511, 12p (2007)] using data from a series of scene- and date-specific Landsat-7 ETM+ images (at 28.5 m resolution) from the 1999-2002 data collection period and a chronological series of standard MODIS 16-day composites of enhanced vegetation index (EVI; at 250 m resolution) spanning the 2003-2005 growing periods (April-October). Surface reflectances from the Landsat-7 ETM+ images are used to derive fine-scale estimates of EVI, which are then transformed into long-term averages by taking into account growing-season specific, temporal trends in the series of MODIS-EVI images. As values from the 8-day accumulated GDD and 16-day composites of EVI have been shown to be strongly correlated, a new data-fusion method based on the mean and instantaneous values of fine-grain long-term average EVI is used to augment the resolution of the initial GDD map. As a demonstration, we apply the procedure to satellite and climate station data for the Canadian Province of Nova Scotia.

Estimation of daytime net ecosystem CO2 exchange over balsam fir forests in eastern Canada: combining averaged tower-based flux measurements with remotely sensed MODIS data

The focus of this paper is to develop a practical approach for estimating daytime net CO2 fluxes (i.e., daytime net ecosystem exchange or NEE) generated over balsam fir (Abies Balsamea (L.) Mill.) dominated forest ecosystems in the Atlantic Maritime ecozone of eastern Canada. The approach establishes empirical relationships between daytime NEE and absorbed photosynthetically active radiation (APAR) for the May–September period in 2004 and 2005 using flux measurements obtained at one of four flux towers in west-central New Brunswick, Canada. Our analysis reveals that the seasonally averaged daytime NEE and APAR values are strongly correlated. A linear regression fitted to the data explains more than 97% of the variation in the averaged daytime fluxes. Application of this linear relationship to data collected from a second New Brunswick flux site with higher measured NEE produces an equally high r2 value (~99%) when a linear fit is applied to the observed versus predicted values. Spatial calculations of APAR are obtained by multiplying the moderate resolution imaging spectroradiometer (MODIS) derived fraction of photosynthetically active radiation and digital elevation model corrected calculations of photosynthetically active radiation. This information and the relationship between daytime NEE and APAR provide the basis for the calculation of NEE across a balsam fir dominated region in northern New Brunswick, where it constitutes more than 50% of the forest cover.

Spatial Enhancement of MODIS-based Images of Leaf Area Index: Application to the Boreal Forest Region of Northern Alberta, Canada

Leaf area index (LAI) is one of the most commonly used ecological variables in describing forests. Since 2000, 1-km resolution Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composites of LAI have been operationally available from the National Aeronautics and Space Administration (NASA), USA, at no cost to the user. In this paper, we present a simple protocol to enhance the spatial resolution of NASA-produced LAI composites to 250-m resolution. This is done by fusing MODIS-based estimates of enhanced vegetation index (EVI), consisting of 16-day 250-m resolution composites (also from NASA), with estimates of LAI. We apply the protocol to derive 250-m resolution maps of LAI for the boreal forest region of northern Alberta, Canada. Data fusion was possible in this study because of the inherent linear correlation that exists between EVI and LAI for the April to October growing period of 2005-2008, producing r 2 -values of 0.85-0.95 and p-values < 0.0001. Comparison of MODIS-based LAI with field-based measurements using the Tracing Radiation and Architecture of Canopies (TRAC) sensor and LAI-2000 Plant Canopy Analyzer showed reasonable agreement across values; statistical comparison of LAI data points produced an r 2 -value of 0.71 and a p-value <0.0001. Seventy one percent of MODIS-based LAI were within ±20% of field estimates.

Vegetation Control in the Long-Term Self-Stabilization of the Liangzhou Oasis of the Upper Shiyang River Watershed of West-Central Gansu, Northwest China

Abstract This paper explores the relationship between vegetation in the Liangzhou Oasis in the Upper Shiyang River watershed (USRW) of west-central Gansu, China, and within-watershed precipitation, soil water storage, and oasis self-support. Oases along the base of the Qilian Mountains receive a significant portion of their water supply (over 90%) from surface and subsurface flow originating from the Qilian Mountains. Investigation of vegetation control on oasis water conditions in the USRW is based on an application of a process model of soil water hydrology. The model is used to simulate long-term soil water content (SWC) in the Liangzhou Oasis as a function of (i) monthly composites of Moderate Resolution Imaging Spectroradiometer (MODIS) images of land surface and mean air temperature, (ii) spatiotemporal calculations of monthly precipitation and relative humidity generated with the assistance of genetic algorithms (GAs), and (iii) a 80-m-resolution digital elevation model (DEM) of the area. Modeled r...

Evaluating Potential of MODIS-based Indices in Determining "Snow Gone" Stage over Forest-dominant Regions

Abstract: “Snow gone” (SGN) stage is one of the critical variables that describe the start of the official forest fire season in the Canadian Province of Alberta. In this paper, our objective is to evaluate the potential of MODIS-based indices for determining the SGN stage. Those included: (i) enhanced vegetation index (EVI), (ii) normalized difference water index (NDWI) using the shortwave infrared (SWIR) spectral bands centered at 1.64 µm (NDWI 1.64µm ) and at 2.13 µm (NDWI 2.13µm ), and (iii) normalized difference snow index (NDSI). These were calculated using the 500 m 8-day gridded MODIS-based composites of surface reflectance data ( i.e. , MOD09A1 v.005) for the period 2006–08. We performed a qualitative evaluation of these indices over two forest fire prone natural subregions in Alberta ( i.e. , central mixedwood and lower boreal highlands). In the process, we generated and compared the natural subregion-specific lookout tower sites average: (i) temporal trends for each of the indices, and (ii) SGN stage using the ground-based observations available from Alberta Sustainable Resource Development. The EVI-values were found to have large uncertainty at the onset of the spring and unable to predict the SGN stages precisely. In terms of NDSI, it showed earlier prediction capabilities. On the contrary, both of the NDWI’s showed distinct pattern (

Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data

Forest fires are a critical natural disturbance in most of the forested ecosystems around the globe, including the Canadian boreal forest where fires are recurrent. Here, our goal was to develop a new daily-scale forest fire danger forecasting system (FFDFS) using remote sensing data and implement it over the northern part of Canadian province of Alberta during 2009–2011 fire seasons. The daily-scale FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived four-input variables, i.e., 8-day composite of surface temperature (TS), normalized difference vegetation index (NDVI), and normalized multiband drought index (NMDI); and daily precipitable water (PW). The TS, NMDI, and NDVI variables were calculated during i period and PW during j day and then integrated to forecast fire danger conditions in five categories (i.e., extremely high, very high, high, moderate, and low) during j + 1 day. Our findings revealed that overall 95.51% of the fires fell under “extremely high” to “moderate” danger classes. Therefore, FFDFS has potential to supplement operational meteorological-based forecasting systems in between the observed meteorological stations and remote parts of the landscape.

Quantification of Local Warming Trend: A Remote Sensing-Based Approach

Understanding the warming trends at local level is critical; and, the development of relevant adaptation and mitigation policies at those levels are quite challenging. Here, our overall goal was to generate local warming trend map at 1 km spatial resolution by using: (i) Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composite surface temperature data; (ii) weather station-based yearly average air temperature data; and (iii) air temperature normal (i.e., 30 year average) data over the Canadian province of Alberta during the period 1961–2010. Thus, we analysed the station-based air temperature data in generating relationships between air temperature normal and yearly average air temperature in order to facilitate the selection of year-specific MODIS-based surface temperature data. These MODIS data in conjunction with weather station-based air temperature normal data were then used to model local warming trends. We observed that almost 88% areas of the province experienced warming trends (i.e., up to 1.5°C). The study concluded that remote sensing technology could be useful for delineating generic trends associated with local warming.