A Remote Sensing Technique for Monitoring Temporal Dynamics of a Forest Located at a Desert Fringe
p. 217-226
Résumé
Drought years are a very frequent phenomenon in Israel. Between the years 1994/5 and 2001/2, Israel experienced four (non-consecutive) years of drought. Consequently the Yatir forest, a pine forest located in the desert fringe, suffered from a notable water shortage. The aim of this research is to detect and assess seasonal/phenological changes and inter-annual changes in the forest trees with respect to the drought effect. The use of a spectral vegetation index, namely the Normalized Difference Vegetation Index (NDVI) to detect stress conditions was implemented by using eight Landsat TM and ETM+ images. In addition, the change detection NDVI Image Differencing technique was applied for assessing seasonal and inter-annual variations in vegetation. Considerable NVDI decline was observed between 1995 and 2000 due to the drought events during these years, enabling assessment of the spatial and temporal effects of such a disaster. NDVI image differencing has proven to be a useful and accurate method for tracing physiological changes in the Yatir forest, which serves as a case study for a manmade forest in the desert fringe.
Entrées d’index
Keywords : remote sensing, landsat, ndvi, change detection, drought, forest, desert fringe, Israel
Texte intégral
Introduction
1The recurrence of severe droughts in the Sahel and other regions around the world has led to extensive discussions on the effect of droughts on the life of people and plants, particularly in the arid and semi-arid climatologic zones. Forest planting on the desert fringe represents an attempt to combat desertification and rehabilitate of drylands. In Israel, the Yatir forest is such a manmade pine forest situated in the transition between the semi-arid and sub-humid zones (275 mm mean annual rainfall). In order to explore changes and the effects of drought in the forest, the Normalized Difference Vegetation Index (NDVI) was used. The NDVI is the most widely used vegetation index, and it is based on the ratio between the maximum absorption of radiation in the red (R) spectral band vs. the maximum reflection of radiation in the near infrared (NIR) spectral band. Lacking the plants’ absorption/reflectance mechanisms, soil spectra typically do not show such a dramatic spectral difference. NDVI is formulated as:
2where R is the reflectance value in the indicated spectral bands. NDVI values range between -1.0 to +1.0 but are usually positive for soil and vegetation. Denser and/or healthier vegetation have higher values.
3NDVI values of vegetation usually offer an efficient and objective mean for evaluating phenological characteristics (e.g., Justice et al.,1985; Reed et al., 1994; Running et al., 1995) and have long been used to monitor vegetation conditions and changes in vegetation cover (e.g., Lyon et al., 1998; Mas, 1999; Woodcock et al., 2001).
4Change detection has become a major application of remotely sensed data because of repetitive coverage at short intervals and consistent image quality (Mas, 1999). Two categories are recognized for the change detection assessment (Yuan et al., 1998). The first is conversion from one land cover type (class) into another and the other is transformations within a given land cover type. The latter can be used for examining the effect of water shortage on the land use of a single renewable natural resource. Several methods for detecting land cover changes were reviewed by Yuan et al. (1998) and Mas (1999). The Vegetation Index Differencing method, and particularly the NDVI Image Differencing, was found to be suitable for the current investigation. NDVI Image Differencing (Δ NDVI) is a change detection technique that has been used for several applications such as studying the effect of extensive flooding on forest ecosystems (Michener and Houhoulis, 1997), monitoring the impact of urban development (Fung and Siu, 2000), and monitoring the regeneration of Mediterranean shrubland (Svoray et al., 2003). The following equation is applied:
5where the subscripts t1 and t2 are NDVI images from dates 1 and 2, respectively.
6The results of this operation correspond to an increase or decrease in vegetation state or cover.
7Nelson (1983) showed that using the Δ NDVI technique has stronger relationship to the phenomena of interest in the scene than any single spectral band alone.
8The objective of this research is to apply remote sensing and geographic information system (GIS) techniques to monitor changes in the forest on two temporal scales - seasonal and inter-annual changes. The hypothesis is that the NDVI provides a suitable tool to assess changes in the Yatir forest that are related to drought periods due to decrease in vegetation cover and consequent increase of soil background. Spatial data of the forest, coupled into a GIS, can provide a better understanding of the areal changes within the forest during the same hydrological year and patterns of change between the years.
Material and methods
Study area
9The forest area to be studied is located between the Mediterranean and Dead Seas, approximately 31° 20’ N 35° 00’ E and 650 m above mean sea level (Fig. 1). This southern part of the mountain chains in Israel is situated between two climate zones: the dry desert with less than 200 mm rainfall per annum and the semi-arid desert that receives between 200 mm and 300 mm rainfall per annum. In addition to its location on the desert fringe, its relative high elevation plays an important role in defining the climatic characteristics of the forest (Fig. 2). The mean annual temperature is about 17.6°C (ranging between 12.8 and 22.4°C).
10The mean winter temperature (November–March) ranges from 9.1 to 12.7°C, while in summer (June-September) the temperatures range from 23.2 to 24.3°C (Eshel, 2000). The long-term mean annual rainfall in this region (275 mm) is limited to the winter months
11(October – April) and characterized by high annual fluctuation, unequal distribution of the events within the rainy season, and above all its general scarcity. The current research covers a 7-year period (Fig. 3). The hydrological year 1994/5 is characterized by much more rainfall than the annual mean (360 mm). The next two years, 1995/6 and 1996/7 were drought years with 158 and 232 mm of rain, respectively. 1997/8 was an average year with 274 mm of rainfall. The following two years, 1998/9 and 1999/2000 were again drought years with 138 and 157 mm, respectively. Lastly, 2000/1 was a wet year with 297 mm. In summary, during five years (1994/5 – 1999/2000 the forest suffered four drought years and one average year. The trees in the forest were planted during four decades: 1960s (28% of the forest); 1970s (38%); 1980s (13.5%); and 1990s (20%).
Dataset
12Assessment of drought effect was implemented by using eight Landsat TM and ETM+ images dating from: winter and spring 1994/5; fall 1995; winter and spring of 2000; and spring 2001. Fig. 3 shows the distribution of Landsat images with respect to monthly and cumulative yearly rainfall.
Image pre-processing
13The aim of the pre-processing operation was to bring all images to the same comparable format. Raw digital numbers of the images were converted to radiance values using the procedure published by EOSAT (1986). In order to perform atmospheric correction and to convert the radiance values to surface reflectance values, the Second Simulation of Satellite Signal in the Solar Spectrum (6S) (Vermote et al., 1997) was implemented. For this code aerosols and watervapor contents were acquired from a sunphotometer located at Sede-Boker Campus, about 50 km from the research site. All images were then registered to the New Israeli Grid using 20 ground control points with a root mean square error (RMSE) of less than one pixel. The area of interest (AOI), namely the Yatir forest, was extracted from the geometric corrected images. Finally, a masking of the areas with no trees was performed in order to provide NDVI images with a minimal effect of bare soil and/or annuals that grow in clear-cut plots. Landsat-derived NDVI, for selected dates in winter and spring 1995 and winter and spring 2000 is presented in Fig. 4.
Change detection
14The NDVI Change Detection method (Eq. 2) was selected for implementing the research goals. Changes within the same hydrological year were computed in order to assess the dynamics of NDVI during the phenological cycle of the trees. In addition, images from the same season in different years were computed to characterize the drought effect on vegetation cover due to differing rainfall regimes. Note that the 1994/5 hydrological year, which represents a wet year with above-mean annual rainfall, occurred after several wet years. Conversely, the 1999/2000 hydrological year represents a drought year, the fourth drought year in a five-year period. A common way to assess changes is based on determination of thresholds in terms of standard deviation levels from the mean Δ NDVI (NDVI Δ) (Fig. 5A). In this manner, one can distinguish between changed and unchanged pixels as well as between negative and positive changes (Jensen, 1986). In the current study, in the cases in which the entire forest changed in only one direction, the threshold is determined in the minimal NDVI value (≈0) and not in adjacency to the mean in order not to lose meaningful information (Fig. 5B). Steps of 0.5 standard deviation (STDV) from the NDVI Δ determined the magnitude of the change in both case studies described above.
Results and discussion
Inter-annual dynamics of NDVI
15Fig. 6 represents the NDVI transects across the forest (8.5 km) during the rainy year in 1994/5 (360 mm) and the drought year 1999/2000 (130 mm of rain after three non-consecutive years of drought). The curve of January 1995 shows the highest values of NDVI that are related to the photosynthetic activity of a healthy forest. The curve of June 1995 shows lower NDVI values, as expected due to closing of the stomata during the late spring and summer. Looking at the same months five years later reveals the effect of water shortage on the NDVI due to four years of drought. The January 2000 curve has higher NDVI values than May of the same year, however compared to January 1995 these values are significantly lower. Note that the NDVI values of January 2000 almost perfectly match the ones of June 1995. Also note that the effect of rainfall amounts, after four years of drought, is less profound than the effect of rainfall on the forest during a rainy year. The amount of rain required to recover the trees into the growing mode needs to be greater than the amount of rain needed in 1994/5.
Δ NDVI images
16Numerous change detection products were computed in order to observe the magnitude of the change along the phenological cycle of different years and in the same season but between years. According to the previous discussion, a threshold value was derived from the image as one standard deviation (SD) from the Δ NDVI mean in cases where the mean was between -0.1 and +0.1. Otherwise, when the mean was either smaller -0.1 or greater than +0.1, Δ NDVI was set to 0 as the reference point. Table 1 summarizes the mean and SD of the nine case studies. Positive mean indicates recovery of the forest while negative mean indicates degradation.
17Inter-annual changes are illustrated in Fig. 7. The largest negative change is observed between January 1995 and January 2000 due to the droughts in-between the two years (Fig. 7A, Table 1). The change between June 1995 and May 2000 was less pronounced since both images represent the season with less photosynthetic activity (Fig. 7B, Table 1). The moderate recovery of the forest as a result of a new wet year expressed as positive change, is demonstrated in Table 1. Note however that most of the pixels remain unchanged.
Conclusions
18The aim of this study was to monitor temporal changes in NDVI values in the Yatir forest drought conditions. Despite limitations caused by the effect of differences in bare soil reflectance and a relatively low number of images it can be concluded that changes of NDVI values during the growing season show that changes in forest physiology could be detected due to changes in photosynthetic activity. During the winter, high photosynthetic activity values are detected due to the relatively low temperature (in comparison with summer temperatures) and the high water availability. Conversely, during the summer, stomata close and photosynthetic activity decreases as a result of high temperature and absence of water. All those variables affect the state of the forest greenness and are reflected in the NDVI fluctuations along the year. This result shows the changes in phenotype in Pines trees with their immigration from Europe to Israel: a change from high photosynthetic activity in the summer months in Europe to high photosynthetic activity in the Israeli winter. In summary, NDVI image differencing has proven to be a useful and accurate method for tracing physiological changes in the Yatir forest, which serves as a case study for a manmade forest in the desert fringe.
Bibliographie
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Auteur
The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben Gurion
University of the Negev, Sede Boker Campus, 84990, Israel.
karnieli@bgu.ac.il
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