Spatial-statistical modeling of deforestation from an ecogeomorphic perspective in typical Hyrcanian forests, northern Iran

doi:10.21203/rs.3.rs-4918455/v1

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Spatial-statistical modeling of deforestation from an ecogeomorphic perspective in typical Hyrcanian forests, northern Iran

https://doi.org/10.21203/rs.3.rs-4918455/v1

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The spread of disruptive and negative environmental changes in the watersheds has led to the adopting of new interdisciplinary approaches with awareness of the manifold interactions between biotic and abiotic components, to organize and restore watersheds. This study aimed to discover and identify the interaction between geomorphological variables and deforestation by adopting an interdisciplinary approach of ecogeomorphology and relying on statistical-spatial analysis, to model forest loss deforestation. Talesh catchments in northern Iran have been selected as the target area due to their good forest ecosystems and many environmental issues in recent years. Independent geomorphometry variables were: altitude, slope, topographic position index (TPI), northness, esatness, plan curvature, profile curvature, length of slope factor (LS), slope length, topographic wetness index (TPI), contributing area, distance to stream, terrain ruggedness index. The assessment of negative changes in forest cover from 1991 to 2022 showed that about 90 km² (4.5% of the total area of catchments) has been exposed to deforestation. The results of logistics regression analysis of the relationships between geomorphological variables and deforestation showed that the high probability of forest loss in low altitude and valleys, low slopes, divergent flow points, convex surface, downstream section, flat areas with homogeneous, dry zones with low moisture. Therefore, we noticed the "ecogeomorphic sensitivity" in such areas is more than in other environments. What is important for regional, urban, and rural policymakers and planners are the areas that were identified with high ecogeomorphic sensitivity. These hotspots require more care and protection, and any human intervention in these areas must be done consciously and in accordance with environmental sustainability.

Deforestation

Geomorphometry

Modeling

Logistic Regression

Talesh

Land use/land cover change (LULCc) is a global environmental challenge that has a considerable impact on the sustainability of land and water resources (Malede et al. 2023). Nowadays, widespread and rapid land use/cover changes at planetary, regional, and local scales, leading to challenging consequences such as land degradation, reduced agricultural productivity, loss of biodiversity, and disruption of ecosystem functions. Identification of various aspects of LULCc and its drivers in space and time supply sustainable natural resources (Twisa and Buchroithner, 2019). The relationship between LULCc and regional climate (Salazar et al. 2015; Nayak et al. 2021), hydrology (Alvarenga et al. 2016; Kumar et al. 2022), runoff and flood (Chen et al. 2020; Anh Tu et al. 2023), groundwater (Elmahdy et al. 2020; Salem et al. 2023), soil erosion (Wang et al. 2016; Kogo et al. 2020), landslides (Shu et al. 2019; Tyagi et al. 2023), and ecosystem services (Huang et al. al. 2019; Petroni et al. 2022), clearly reveals the significance of LULCc. Forest cover changes are a significant concern in the world. Forests serve as renewable resources within the Earth system, providing enormous ecosystem services, including provisioning, regulating, supporting, and cultural services for humans since ancient times (Bera et al. 2022). The presence of forests, especially upstream of the catchments, is considered as a moderator of hazardous hydrogeomorphic processes and environmental disturbances such as erosion, floods, and mass movements (landslide, fall, avalanche, etc.). Since the 1960s, human interventions in the natural environment have increased through activities such as mining, urban development, and farming. These human needs regarding forest cover have appeared in the form of deforestation (Ramachandran et al. 2018) which has been associated with several hazards. There have been worrying reports of flood hydrological changes due to deforestation in various watersheds (Sriwongsitanon and Taesombat 2011). Flooding is often correlated with deforestation or forest loss (Ramadhan et al., 2023). In this regard, researchers have pointed to the effect of deforestation and forest conversion on increased streamflow and flooding (e.g., Salazar et al. 2015; Alvarenga et al. 2016; Ewane and Lee 2020). Given these consequences, monitoring and predicting negative changes in forest cover (deforestation) is crucial for the sustainability of water and soil resources at the catchment scale. This issue holds particular significance in countries located in arid and semi-arid regions like Iran, which encounter numerous challenges in maintaining healthy and sustainable water and soil resources. Due to its location in the dry belt of the world, Iran is poor in forest cover and it ranks last among the countries in the world in terms of forest cover (Velayati and Kadivar 2010). The Talesh region, located in Gilan province, N.W. Iran is a unique geographical area in the west of the Caspian Sea characterized by a predominantly mountainous and forest-covered landscape. The plains are stretched as a narrow and continuous strip at the confluence with the Caspian Sea, where the majority of the region's population resides. The favorable climatic conditions, fertile soils, diverse vegetation, and abundant surface and groundwater resources in the Talesh region provide suitable conditions for agriculture and livestock production (Riahi and Javan 2017). However, these activities have also given rise to numerous environmental problems. The unsystematic and excessive expansion of these activities in the foothills and slopes has caused the problem of deforestation. Similarly, LULC alteration of rural areas adjacent to forested valleys to urban and artificial land cover has resulted in the destruction of natural land cover. The consequences of these actions include erosion, flooding, and the deterioration of the ecological conditions in the region. Notable examples of these issues can be observed in the catchments of Navarood (Fatolahzadeh and Sarvati 2012), Shafarood (Nikooy et al. 2010), and Lisar (Panahandeh 2018). Protecting, conserving, and restoring forest areas in this region requires a comprehensive understanding of the interactions between environmental factors and the spatio-temporal dynamics of forests.

It is evident that the physical environment is the basis and template for vegetation cover changes and the establishment and persistence of dynamic human activities are also contingent upon this environment. Land use and anthropogenic disturbances systematically vary across a given region with topography. Anthropogenic activities and interventions vary depending on different features of the earth's surface. For example, agriculture is preferably common in flat lands, and slopes and hollows are not suitable due to low crop yields (Detto et al. 2013). Geomorphic components and elements are prominent representations of the physical environment and are employed to explain the spatio-temporal dynamics of forests. The dynamic interactive relationship between form and process profoundly influences the geographical distribution of forests in space and time through biogeochemical cycles and the provision of water, nutrients, and sediment. Engelhardt et al. (2011) highlighted the refuge of forests in rough terrain. Fox et al. (2012) acknowledged that landforms have played a key role in forest cover dynamics in all studies regarding land cover change in the Mediterranean region. Bebi et al. (2016) found that the extension of Alpine forests since the 19th century has primarily occurred on steep slopes. Various researchers have used geomorphological variables in analyzing and modeling the spatial and temporal variations of forest cover (e.g. Siles 2009; Pir Bavagar 2015; Bonilla-Bedoya et al. 2018; Saha et al. 2020; Bera et al. 2022).

A review of studies investigating the spatial relationships between geomorphic factors and deforestation reveals that most researchers have not paid attention to the interaction between ecology and geomorphology under the name of "Ecogeomorphology" in explaining environmental changes. In addition, most of the researchers have focused on utilizing only three geomorphic variables, including elevation, slope, and aspect, to explain forest cover variations. However, the use of various geomorphometry parameters under the topic of "Terrain Analysis ", which has become very important in geoscience, presents an opportunity to reveal the real and special power of geomorphology in modeling the dynamics of ecological patterns. "Geomorphology, the study of landscape change, thus stands in the center of a newly emerging science of the Earth's surface, where strong couplings link human dynamics, biology, biochemistry, geochemistry, geology, hydrology, geomorphology, and atmospheric dynamics, including climate change. Geomorphologists today are employing a rapidly expanding, interdisciplinary set of tools that are revolutionizing how we understand Earth-surface processes'' (Murray et al. 2009). This has been greatly facilitated by the availability of extensive and readily accessible remote sensing (RS) data and the development of spatial analysis tools such as geographic information systems (GIS), IDRISI, SAGA, etc. The combination of RS/GIS with geomorphological and ecological data has made it possible to reveal the interrelationships between environmental variables (García-Aguirre et al. 2010) and in this way, it is possible to model and predict the spatio-temporal variation of the forest cover in relation to geomorphological characteristics. Given the approach, this study aimed to 1- detection of negative changes in forest cover (deforestation) and 2- determination and modeling of the relationship between geomorphic variables and deforestation in the Talesh catchments. In this context, employing a multitude of terrain variables, particularly those that represent the morphodynamic conditions of catchments, guiding us from form to process, can constitute an innovative aspect of the research and provide novel insights into ecogeomorphological studies. We can even introduce the term of "ecogeomorphic sensitivity" to sites with close relationships between geomorphic variables and deforestation occurrences. Identifying and monitoring negative forest cover changes in relation to geomorphic variables can, on the one hand, identify areas prone to natural forest degradation and, on the other hand, reveal areas conducive to forest sustainability. This knowledge can facilitate the prioritization of conservation, protection, and restoration efforts for forest cover at the catchment scale, paving the way for sustainable utilization of these valuable natural resources. Such an approach and spatial analysis have not been undertaken in the Talesh region to date and can serve as a guide for future researchers in modeling deforestation probability.

2.1. Study area

The Talesh region, with an area of 3,207 square kilometers, is located in the north of Iran (Fig. 1). In terms of political divisions, this region is located in the northern half of Gilan province and is bordered by Azerbaijan to the north, Ardabil province to the east, and the Caspian Sea to the west. The major population centers of the region are the cities of Astara, Lisar, Hashtpar, and Rezvanshahr, which are located on the plain and at an elevation of 100 meters below sea level. The studied catchments include 12 catchments with a total area of 1,992 square kilometers. Topographically, most of the region belongs to the mountain unit, which stretches south-north to the border between Iran and Azerbaijan. The highest of these mountains is 3,236 meters above sea level. The Talesh plain, which includes the eastern part of the region, is covered by alluvial formations resulting from the erosional activity of rivers flowing into the Caspian Sea. This plain is narrow in the north, with a width of 2 to 3 kilometers, but gradually widens to the south, reaching a width of 6 to 8 kilometers. The lowest point in Talesh Plain is 59 meters below sea level.

Distance to the Caspian Sea and the distribution and extent of the mountains are important and determining factors in the climate of this region. Significant altitudinal variation over a short distance creates a high climatic diversity, and the proximity to the Caspian Sea as a source of moisture moderates the climatic conditions (Sari Saraf et al. 2009). Given the 500 to 1500 mm isohyet lines with a west-east increasing trend indicate the high rainfall in this region. These prevailing topoclimatic conditions in the region, through biophysical and biochemical weathering, have provided the necessary conditions for the development of thick and fertile soils and, consequently, the formation of dense and semi-dense forests. These forests, which start from the edge of the Talesh plain and extend to elevations of 2,000-2200 meters, play an important role in preventing the occurrence of rapid runoff and erosive processes and maintaining the environmental equilibrium of the region. The structural zone of Talesh is characterized by broken and folded folds, and rivers flow as the main erosion factor in deep and winding valleys. River discharges reach a peak in late autumn and winter and decrease in late spring and early summer. The high erosive and flood power of the rivers, induced by the topoclimatic and morphotectonic conditions, indicates the sensitivity of the catchments to anthropogenic interventions and changes in their ecogeomorphic conditions.

2.2. Data used

To conduct this research, various databases were used, including Landsat satellite images, Google Earth, and Digital Elevation Model (DEM). The characteristics of the data used are presented in Table 1.

Table 1

Characteristics of data used
Satellite	Sensor	Spatial Resolution	Year	Derived Variables	Source
Landsat 5	TM	30	1991	Forest Cover	https://earthexplorer.usgs.gov
Landsat 8	OLI	30	2022	Forest Cover	https://earthexplorer.usgs.gov
Shuttle Radar Topographic Mission	SRTM	30	2000	Geomorphometry	https://earthexplorer.usgs.gov

2.3. Methodology

The present study was based on spatial-statistical analysis. Satellite image processing and spatial analysis fulfilled by GIS 10.8, SAGA 7.0, and IDRISI 17.0 software. The spatio-temporal modeling and prediction of deforestation probability using geomorphologic variables were carried out in 3 main steps (Fig. 2) as follows:

2.3.1. Detection of negative forest cover changes

Landsat data (TM, ETM, OLI) has been widely used for land cover classification and change detection due to their spatial resolution (30 m), revisit period (16 days), and wide spatial coverage (185 * 185 km) (Gomez et al. 2016). In this study, two Landsat images from 1991 and 2022 were used to detect forest cover changes over 32 years. In selecting appropriate satellite images, an effort was made to ensure the images were free of cloud cover, phonological limitations, and scanline gaps. Additionally, considering the peak vegetation greenness in the region during June and July (Shahzeidi et al. 2014), the images were selected from these months. Since both selected images were Landsat Level-2, geometric and radiometric corrections were not required. To determine the extent, situation, and spatial pattern of forest cover changes in the study area, the "post-classification" method was employed. This method is more prevalent than other change detection techniques (see Were et al. 2013; McRoberts 2014; Scharsich et al. 2017). In this method, two forest/non-forest classifications performed based on two sets of forest/non-forest training data, and then, two classified maps are compared to change detection (McRoberts 2014). Several important aspects must be considered when employing the post-classification method for change detection: 1- harmony and consistency among maps (Sexton et al., 2013) which is possible by utilizing the same training samples for both classifications (Almutairi and Warner 2010), 2- data acquisition timing and seasonality: The data used for change detection should be acquired during similar periods (season and month) and in seasons that are suitable for the phenomenon. This ensures that the spectral reflectance of the phenomena being studied is comparable and consistent across both images. (Ranjbarnejad et al. 2013). In this study, the post-classification comparison method was implemented using the SAGA 7.0.0. Firstly, a false-color composite (RGB) was prepared using Landsat bands 4, 3, and 2. Secondly, the greenness layer was extracted from the Landsat images using the Normalized Difference Vegetation Index (NDVI). NDVI is a widely used indicator of vegetation health and can effectively distinguish between forested and non-forested areas. Thirdly, the composite layer was combined with the NDVI layer. This combined image serves as the input for the subsequent classification step. We used the Maximum Likelihood Classification (MLC) algorithm which is known for its high accuracy. This method classifies each pixel in the image into one of two classes: forest or non-forest. In this regard, training samples were carefully prepared using Google Earth images and in accordance with the region's topographic maps and NDVI. These samples represent the characteristics of both forest and non-forest areas and serve as the basis for the classification algorithm. A 5 km x 5 km grid was used to ensure a representative distribution of training samples across the study area (Fig. 3). We tried to collect one sample per cell, encompassing both forest and non-forest areas. The satellite image classification was performed using 300 training points (100 points for the forest class and 200 points for the non-forest class). During the preparation and selection of training samples, it was ensured that the samples were located in the center of continuous forest masses and not within narrow strips of forest cover or along road margins. This helps to minimize the impact of edge effects and ensures the accuracy of the classification.

Since no classification is complete until its accuracy is assessed, half of the training points were used to perform the classification and the other half were used to evaluate its accuracy. The accuracy assessment of the classification was carried out using the overall accuracy and kappa coefficient metrics. The overall accuracy is calculated as follows (Rwanga and Ndambuki. 2017):

The overall classification accuracy = number of correct points/total number of points (1)

Certainly, the High values of this criteria are indicative of high accuracy. The KAPPA statistic is representative of the discrepancy between the real agreement between reference data and automated classification and the chance agreement between the reference data and the random classification as shown in Eq. (2) (Lillesand and Kiefer et al. 2004).

$$K=\frac{{N\sum\limits_{{i=1}}^{r} {{x_i}_{i} - N\sum\limits_{{i=1}}^{r} {({x_i}+X{x_{+1}})} } }}{{{N^2} - \sum\limits_{{i=1}}^{r} {({x_{ii}}X{x_{+1}})} }}$$

Where; r = number of rows and columns in error matrix, N = total number of observations (pixels), X_ii = observation in row i and column I, Xi + = marginal total of row i, and X + i = marginal total of column I (Rwanga and Ndambuki. 2017). The rate of the accuracy of the classified map is based on the kappa coefficient specified according to Table 2.

Table 2

Rating criteria of Kappa statistics (Richards 2022)
Kappa Coefficient	Classification can be regarded as
Below 0.4	Poor
0.41–0.60	Moderate
0.61–0.75	Good
0.76–0.80	Excellent
0.81 and above	Almost Perfect

After classification and ensuring their accuracy, the resulting images were entered into the IDRISI 17.0 environment for comparison, and the negative changes that occurred in the forest area were determined through the crosstab tool.

2.3.2. Extraction and Preparation of Geomorphological Variables

Remote sensing and GIS techniques are applied to measure diverse terrain variables and morphometry of drainage basins, as they supply a flexible environment and capable tools for manipulating and analyzing spatial information (Aparna et al. 2015). In the present study, the capabilities of GIS 10.8 and SAGA 7.0 software were utilized for geomorphometry analysis. A total of 15 geomorphological variables were extracted using the DEM (Table 3). Due to the varying scales and ranges of the independent variables, it was necessary to normalize them before incorporating them into logistic regression analyses. This was fulfilled by the following equation, which rescales the variables to a range of zero to one:

$${X_{normalized}}=\frac{{x - \hbox{min} (x)}}{{\hbox{max} (x) - min(x)}}$$

Where, x: the initial value of the variable; min (x): the minimum of the variable; and max (x): the maximum of the variable.

Table 3

Geomorphological variables involved in the modeling of deforestation
Variable	Symbol	Description	Reference
Altitude	Alt	-	-
Slope	S	The rate of change of elevation in the direction of the steepest descent	Wilson and Gallant (2000)
Topographic Position Index	TPI	TPI compares the elevation of a cell to the mean elevation of the surrounding cells in a specified area. Positive values represent ridges 10 and negative TPI values represent valleys while flat areas have a value near zero.	Agren et al. 2014
Northness	N	Linear transformation of aspect to 2 section: North (value = 1), South (value= -1) as follows:$northness=\cos (aspect)$	Rodriguez-Moreno and Bullock (2014)
Eastness	E	Linear transformation of aspect to 2 section: East (value = 1), West (value= -1) as follows:$eastness=\sin (aspect)$	Rodriguez-Moreno and Bullock (2014)
Planform Curvature	PLC	The curvature along the line of intersection between the surface and the XY plane. Plan curvatures are set to positive when the curvature is convex. Negative values indicate concave curvature. This value is undefined when the slope is exactly 0	Jenness (2012)
Profile Curvature	PRC	Positive values is indicative of concave surface (when water would decelerate as it flows over this point). Negative values indicate convex surface (where stream flow would accelerate).	Jenness (2012)
Convergence Index	CI	This index measures the rate of convergence/divergence of flow in a pixel. Negative and positive values indicate convergence and divergence, respectively.	Olaya (2004)
Slope Length Factor	LS	LS is the length-slope factor that accounts for the effects of topography on erosion. This variable is calculated based on 2 parameters: 1- Specific Area (A_s) 2- Slope (β) as follows:${L_s}=(n+1){\left( {\frac{{{A_s}}}{{22.13}}} \right)^n}{\left( {\frac{{\sin \beta }}{{0.0896}}} \right)^m}$ n = 0.4 و m = 1.3	Moor et al. (1991)
Slope Length	SL	Slope length from highlands to lowlands along the flow direction.	Hickey (2000)
Topographic Wetness Index	TWI	This Index is calculated based on two parameters: Specific Area (A_s) - Slope (β) as follows:$TWI=\ln ({A_s}/tan(\beta ))$ TWI is mainly used to characterize the long-time soil moisture status at each point. High values show wetlands, but low values indicate arid lands.	Ma et al. (2010)
Contributing Area	CA	A matrix where each cell is assigned a value equal to the number of cells that flow into it.	Temimi et al. (2010)
Stream Density	Str-Dens	A measure of the length of stream channel per unit area of drainage basin	https://esdac.jrc.ec.europa.eu/
Distance to Stream	Str-Dist	Calculates, for each cell, the Euclidean distance to the closest stream	ArcGIS Tutorial
Terrain Ruggedness Index	TRI	The sum of elevation variations between one pixel and 8 neighbors surrounding it	Riley et al, 1999

2.3.3. Modeling Negative Change of Forest Cover (Deforestation)

Spatial regression models are a useful method for establishing the relationship between vegetation maps and maps of environmental variables, all of which are combined into a geographic information system (GIS) (Bax et al. 2016). "These models address three key research questions: (1) where? – identifying areas affected by change and areas that are most likely to undergo some change in the future; (2) why? – linking potential causal factors with change; and (3) when? – measuring rates of change" (Rutherford et al. 2008). Common parametric models such as binary logistic regression have found extensive application in this field. Logistic regression is a statistical method belonging to generalized linear models that predicts the probability of a phenomenon occurring using independent variables. The key aspect of logistic regression is that the dependent variable is binary, meaning it can only take values of 0 (indicating non-occurrence) or 1 (indicating occurrence). In this study, deforestation and non-deforestation data are considered as the dependent variables. The dependent variable is binary, 0 and 1 which are representative of no deforestation and deforestation, respectively (Saha et al. 2020). The independent variables are the geomorphological variables in this study that are introduced into the model to explain the occurrence of deforestation. The logistic regression prediction equation is as follows:

$$Log(P/1+P))={\beta _0}+{\beta _1}{x_1}+{\beta _2}{x_2}+...+{\beta _k}{x_k}$$

Where βi represents the parameter for each variable estimated by the model, and xi is the factor included in the model (i = 1, 2, · · ·, k); finally, P is the probability that a non-deforested pixel will become deforested (Plata-Rocha et al. 2021). The resulting model is evaluated using a percentage of sample points randomly selected from the initial deforestation map using systematic or stratified random sampling methods. Given statistics for evaluation involve “Pseudo R²" and "Relative Operating Characteristic (ROC)". The pseudo-R² index, based on the likelihood ratio principle, tests the goodness of fit in logistic regression and is calculated as follows:

$$Pseudo - {R^2}=1 - (log(likelihood)/log({L_0}))$$

Where Likelihood = the value of the probability function in the case that the model fits perfectly; Lo = the value of the probability function in the case that all the coefficients except the "a" (y-intercept) are zero. Unlike R² of ordinary regression, Pseudo-R² does not represent the ratio of variance explained by the model, but this index shows the degree of correlation between the experimental data and the output of the regression model, hence its value is generally lower than R² (Arekhi et al. 2013). Pseudo R² = 1 indicates a perfect fit, whereas pseudo R² = 0 indicates no relationship (IDRISI Help). ROC is an excellent statistic for measuring the goodness of fit of logistic regression. The ROC value ranges from 0 to 1, where 1 represents a perfect fit and 0.5 means a random fit. The rates above 0.7 are considered as accurate models, whereas values above 0.9 are considered as highly accurate models (Franklin and Miller 2010).

Modeling and predicting the deforestation was fulfilled by IDISSI 17.0 in this study. In this regard, the dependent variable layer (negative change in forest cover) which was obtained through the crosstab operation, was masked using the catchments layer to restrict modeling to the target catchments.

3.1. Detection of Negative Changes in Forest cover (Deforestation)

The classification of Landsat 5 and 8 images resulted in two maps showing the forest/non-forest areas (Fig. 4). As the classification of satellite imagery was carried out in two well-defined classes (forest and non-forest) and training samples were selected carefully, it was expected that the accuracy of the classification would be acceptable. The results also indicated high classification accuracy for the two images (Table 4).

Table 4

Results of accuracy assessment of classification of satellite images
Landsat 8-2022		Landsat 5-1991
Kappa Coefficient	Overall Accuracy	Kappa Coefficient	Overall Accuracy
0/94	95/61%	0/91	93/14%

The map of negative changes in forest cover (Fig. 5), indicating areas with deforestation, was generated by intersecting the two classification maps. Before dealing with the details of negative changes in forest cover, it is important to note that the studied catchments are located in the mountainous region and are relatively far from the anthropogenic interventions of urban centers situated in the plains in the eastern part of the region. As a result, these catchments can reflect the relationship between physical factors and deforestation. In these areas, human interventions are mainly in the form of agriculture and livestock breeding. Although a comparison of the forest/non-forest classification maps reveals fairly the expansion of deforestation from the plains towards the mountains, the deforestation map shows more clearly this fact. This map shows that deforestation is not only occurring along the main valleys towards the headwaters but that forest destruction is also traceable in different altitude zones and landforms, indicating a hazardous ecogeomorphic event. The rate of forest loss in the catchments is shown in Table 5. Considering that the catchments are listed in this table from north to south, it is determined that the percentage of deforestation in the northern catchments is relatively higher than in the southern ones, even though these catchments are smaller. Out of the total area of catchments, which is equal to 1993 km², 174 km² (8.7%) has been exposed to deforestation during 32 years. The Havigh catchment has the highest percentage of deforestation (7.7%) among the catchments. The spread of deforestation in the main valley of this catchment towards the headwaters is visible, indicating the degradation of the riparian zone. The lowest percentage of deforestation (1.8%) belongs to the Dinachal catchment.

Table 5

The rate of deforestation in Talesh catchments over 32 years (1991–2022)
Catchment	Area (km²)	Deforestation (km²)	Deforestation (%)
Lavandvil	37.44	1/4	3/7
Cheloond	61.60	3/7	6
Lamir	51.59	3/2	6/2
Choobar	61.89	4/5	7/2
Havigh	126.78	9/8	7/7
Shirabad	85.76	2/6	3
Lisar	174.86	5/3	3/1
Korghanrood	528.08	27/2	5/1
Navrood	264.83	8/7	3/3
Khalehsara	49.22	1/2	2/4
Dinachal	202.27	3/7	1/8
Shafarood	348.59	18/6	5/3

Although riparian forests and shrubs in small and rugged catchments are more abundant than in large and less rugged catchments (Engelhardt et al. 2011), it should also be noted that small catchments are more sensitive to land degradation and soil erosion compared to large catchments and have a faster hydrological response. Important studies by Milliman et al. (1999) in the East Indies and Vanmaercke et al. (2011) in the European catchments refer to this high response of small catchments and their sensitivity to floods and landslides. Such catchments characterized by the narrow valley and bedrock dominance, are typically under the control of floods and may exhibit significant channel variations during high flows. As a result, these catchments are highly sensitive to both natural and human disturbances and require preventive land-use management practices such as grazing, roads, and recreation. Compared to small catchments, large catchments have an abundance of grasslands and meadows (Engelhardt et al. 2011) and have a slower hydrogeomorphic response due to the extensive river network and the large distance between the upstream and downstream. However, it is clear that the protective role of forests in protecting water and soil resources is greater than grasslands, and the lower forest cover of large catchments such as Korghanrood and Shafarood has made the risk of floods and erosion in these catchments serious. The issue of the treeline is important in this case. The tree line in the region is located at an altitude of 2000 to 2200 meters. As the area of the catchments increases by moving from north to south of the region, the catchment divides recede to the west and cross the tree line. From another perspective, as the area of the catchments increases, the percentage of bare land or rock outcrops in the upper reaches of the catchments increases. Therefore, it is clear that maintaining environmental balance in all catchments is essential and depends on the conservation and restoration of forests.

3.2. Relationship between geomorphologic variables and deforestation (Logistic Regression model)

The spatial logistic regression test resulted in a spatial predictive model of the probability of deforestation based on geomorphological variables (Eq. 5), which indicates the direction and intensity of the relationships between the dependent and independent variables. This can be understood by considering the b coefficients of the predictive regression model:

Logit (Deforestation) = -1.1803 - − 2.097567* alt − 0.292042*tpi − 2.819719*s − 0.000031*north − 0.000017*east + 0.000312*ls + 0.118545*sl − 1.921002*tri + 0.744707*plc − 0.170077*prc − 0.272819*ci − 0.510281*ca + 0.000751*str-dens − 0.000124*str-dist − 1.262366*twi (5)

Considering that the five variables including northness, eastness, erosion factor of slope length, river density, and distance to the river have had a negligible effect on deforestation, these variables can be removed from the above equation, so that the following final equation can be presented:

Logit (Deforestation) = -1.1803–2.097567* alt − 0.292042*tpi − 2.819719*s + 0.118545*sl − 1.921002*tri + 0.744707*plc − 0.170077*prc − 0.272819*ci − 0.510281*ca − 1.262366*twi (6)

The first geomorphological variable considered in explaining the spatiotemporal changes in vegetation cover is altitude. As elevation increases to a certain extent, precipitation usually increases, and if the maximum elevation of the region does not reach the snow line, favorable topoclimatic circumstances are generally provided for the growth and development of forests. As expected, a strong relationship is observed between altitude (Alt) and the probability of deforestation in the Talesh catchments. The negative relationship between the two variables of altitude and topographic position index (tpi) and the occurrence of deforestation indicates that deforestation has occurred at lower altitudes and in valleys (Fig. 6).

Undoubtedly, the lack of easy access and unfavorable topoclimatic conditions for human activities in highlands have contributed to less deforestation in these areas. In contrast, the accessibility of forests located in lowlands has induced the deforestation process in these areas. The main valleys provide a permanent water reserve for livestock breeding, and in addition, these landforms accelerate the adjacency of roads to the dense forest (Vanacker et al. 2003). Therefore, it is clear that anthropogenic interventions and natural resource destruction have been more concentrated in river valleys. This inverse relationship between altitude and forest loss was widely mentioned by researchers (e.g. Arekhi et al. 2013; Kumar et al. 2014; Bonilla-Bedoya et al. 2018; Pujiono et al. 2019; Bera et al. 2022; Saha et al. 2022). Furthermore, this spatial-statistical relationship indicates that the riparian zone and coastal buffers of the catchments are under threat of degradation (Fig. 7).

“This transitional zone (ecotone) plays a prominent role in the equilibrium of the entire catchment and acts as a regulator of flowing and transportation of water, sediment, and nutrients between the river and the adjacent uplands” (Patten et al. 1998). So this feature is particularly sensitive to flooding in mountainous catchments. In addition, assuming the significance of the flood forest as a complete part of the food chain and the high scarcity of the remaining flood forest, the conservation of this forest should be a main conservation strategy (Lohani et al. 2020).

Changes in elevation across a land surface are represented by the slope, and landforms are formed by the interconnection of these slopes. Slope is an important variable that controls erosion, runoff, and soil development. As the slope increases, the rate of surface and subsurface drainage and soil erosion increases, and the occurrence of mass movements such as landslides and falls is directly related to the increase in slope. Therefore, the stabilization and sustainability of sediment, water, and nutrients may be limited on steep slopes, and subsequently, the establishment and stabilization of plant communities may become difficult. Despite this fact, from the perspective of human activities, the inaccessibility of steep slopes and their unsuitable conditions for the establishment and continuation of human activities, especially agriculture, can be a cause of the preservation of forest cover against deforestation. The inverse relationship between deforestation and slope in the above equation refers to this issue and the refuge of forests in steep areas. Gonzalez-Gonzalez et al. (2021) in their analysis of the spatiotemporal changes of deforestation in Colombia stated that low slope acts as a preventive factor to deforestation, whereas high slope acts as an attractor factor to deforestation. Most researchers, such as Fox et al. 2012; Arekhi et al. 2013; Kumar et al. 2014; Bonilla-Bedoya et al. 2018; Pujiono et al. 2019; Plata-Rocha et al. 2021; Saha et al. 2022, have found this inverse relationship between deforestation and slope.

In addition to topographic factors such as altitude and slope, some variables indicate the land roughness. Among these, two factors can be mentioned: slope length (SL) and terrain ruggedness index (TRI). Slope length (SL) is a geomorphological variable that is measured along the maximum slope, and its high values are observed in large and main valleys. The slope length variable reflects the altitudinal changes along the longitudinal extent of the catchment. The positive relationship between slope length and deforestation indicates that deforestation has occurred in the downstream areas and main valleys (Fig. 8). In contrast, forests in the upstream and first-order tributaries have been less affected by deforestation. Although the volume of floods and sediments increases in the downstream part of the catchment, and this factor may be effective in the destruction of forests due to the removal of plants or the burial of neonate shrubs and seeds under sediments, this fact is less observed in the Talesh catchments. Goebel et al. (2012) believe that the occurrence of floods leads to the generation and conservation of habitats for various plant species due to increasing erosion and sediment transport. So, it can be said that the same factor of easy access and the special attractions of the downstream coastal areas have led to the tendency and concentration of human activities (agriculture, tourism, etc.) in these areas which in turn, have caused forest destruction. Meanwhile, the expansion of the river channel and the formation of a floodplain downstream have led to the extended use of water resources and suitable soil in this section of the catchment, resulting in deforestation there. In addition to the SL factor, which is to some extent a reflection of hydrogeomorphic processes across the catchments, the terrain ruggedness index (tri) is a reflection of the morphodynamic conditions of the slopes and morphogenetic strength. This index is also a rate of topographic heterogeneity (Riley et al. 1999) so the high values of this index indicate low homogeneity of landforms in a specific area. The negative relationship between TRI and deforestation indicates that uneven and topographically heterogeneous areas haven't been exposed to deforestation. Although the adaptation and balance of vegetation cover is greater in homogeneous environments, the ecological diversity of heterogeneous environments is high. A review of the literature also indicates that researchers have acknowledged and emphasized the effects of the heterogeneity of geomorphological conditions on the abundance and diversity of vegetation cover (e.g. Hoersch et al. 2002; Stallins 2006; Reinhardt et al. 2010). In addition, high values of the TRI indicate vigorous altitude gradients that provide an unfavorable environment for human activities and limit the uniform spread of human works.

Landscape curvature variables are useful measures for interpreting the significant water and sediment transport processes in a landscape. Plan curvature (PlC) represents the degree of divergence or convergence perpendicular to the flow direction, while profile curvature (PRC) represents convexity or concavity along the flow direction. The impact of these variables on spatial and temporal changes in vegetation cover (forest) occurs due to control of transport and deposition processes, as well as linear and point accumulation of materials such as water and sediment. The availability of the necessary moisture for soil-forming biophysical and biochemical processes in concave and depressed areas makes them fertile and productive, and concave areas have characteristics that make them more conducive to plant growth and net primary production (Gessler et al. 2000). The direct relationship between the PLC and the forest cover loss in the Talesh catchments indicates this matter. This relationship shows that deforestation has occurred on the convex surfaces of the slopes. This fact can be attributed to the dispersion and movement of sediments and water and the limitation of soil formation and development on convex surfaces, which constrains the stability and regeneration of forests. From an anthropogenic perspective, it appears that the existence of limited, relatively flat surfaces at the top of anticlines makes access to these areas easy, especially for livestock Breeders, leading to deforestation. Furthermore, the avoidance of humans from the shade and humidity of depressions and hollows can also be a reason for the lower probability of deforestation in these areas. The reverse relationship between the second curvature variable (PRC) could be in the completion of the former relations (PLC vs. Deforestation) indicating loss of forest cover on convex surfaces in the direction of the slope. In other words, areas with accelerated flow have been exposed to deforestation. In contrast, depressions and hollows located along the slope with decelerated flow have been less affected by deforestation.

In addition to land curvature variables, which are both indicators of landform and process, there is another variable called the convergence index (CI) that is not sensitive to absolute elevation changes and emphasizes more on the hydrogeomorphic process. This index indicates the convergence/divergence of flow, and its high values are observed on ridges and its low values are observed in river valleys. The resulting inverse relationship between this variable and the dependent variable indicates that the probability of deforestation is higher in the areas with flow convergence and valleys than in other parts of the catchment. Lohani et al. (2020) also found that deforestation is more prevalent in floodplains than upland areas. From an anthropogenic perspective, this fact refers to the attractiveness and easier access to floodplains, which was explained in relation to elevation variables. From a natural perspective, the occurrence of geomorphological disturbances such as landslides and floods can also be involved in this matter. Geomorphological processes such as floods and landslides are important factors in ecological disturbances (Rice et al. 2012). This is particularly important in large catchments such as Korghanrood and Shafarood, which have extensive floodplains (Fig. 9). Typically, the distributions of riparian tree species were limited to riverine corridors where floodplains are preserved from wearing floods (Shaw and Cooper 2008). However, the expansion of human activities such as agriculture and gardening, construction of roads, dams, villas, etc. is evident in the Talesh region from the plains (east) towards the mountains and across the catchments (west), resulting in deforestation.

Although the morphological and roughness variables described refer to the hydrologic conditions of the catchments, some variables are more indicative of the hydrological characteristics across the catchments. The variables of contributing area (CA) and topographic wetness index (TWI) are among them. CA indicates the potential of flow accumulation at a specific location. This location can be any point in the catchment that gathers upstream water flow. The inverse relationship between this variable and deforestation indicates that areas with high flow accumulation are less affected by deforestation. Conversely, areas with unfavorable hydrological conditions and scarce water resources have experienced more deforestation. This fact can be more clearly and completely traced in the relationship between the last geomorphological variable, the topographic wetness index (TWI), and deforestation. The involvement of slope and specific contributing area parameters in the calculation of the TWI makes this variable a composite variable that reflects both roughness and hydrological circumstances. Testing the relationships between TWI and the probability of deforestation in the study watersheds shows that there is an inverse relationship between them. This means that deforestation is more prevalent in areas with good drainage and low moisture than in areas with high surface and subsurface moisture. In other words, steep areas that are prone to rapid runoff and lack of moisture accumulation are more likely to experience deforestation. Areas that are representative of wetlands, where flow accumulation and sedimentation occur, naturally have more nutrients due to biogeochemical processes for the development and stabilization of forests. This allows for faster forest regeneration in these areas.

If we want to summarize the relationship between geomorphological variables and deforestation in the Talesh catchments, we can state that the effect of geomorphic form and process on deforestation is well-represented in the relationship between geomorphometry variables and the probability of deforestation. At the same time, the anthropogenic effects on changing environmental conditions through deforestation are implicit in these interactions. The results correspond to this fact: "Hydrology, geomorphology, and ecology of fluvial systems cannot be fully understood in isolation from one another" (Cadol and Wine 2017). This interweaved relationship and interdependency can be better studied in some geomorphic environments. The results indicate the dominance of deforestation in main and large valleys and the alteration of forest cover in the floodplain and riparian section. The available evidence also shows that the deforestation of these areas in recent years has caused significant damage to human settlements and facilities due to increased flooding. Although the clearing of riparian trees is sometimes associated with coastal erosion, the human effects on this event dominate the purely natural effects. In this regard, Bera et al. (2022) stated that “In high altitude zones, deforestation mainly occurs due to physical factors such as weathering, mass wasting, aeolian process, landslide, etc. whereas, in low altitude zones, deforestation mainly happens due to anthropogenic factors". This is because these areas have favorable geomorphic settings for the expansion of human activities. Humans impact on riverside ecosystems through the exploitation and management of land and water and the introduction or elimination of plant and animal species (Patten 1998). So, discovering the relationships between geomorphological characteristics and riparian vegetation can enhance our ability to spatio-temporal prediction and aware restoration actions (Engelhardt et al. 2011).

From the view of ecogeomorphic sensitivity, the importance of riparian and coastal buffers in regulating and moderating water flow and sediment yield in small mountainous catchments, such as the northern Talesh catchments that have experienced a higher percentage of deforestation compared to southern catchments, is greater. Floods in forested mountain landscapes are distinctly different from lowland floods. Floods in mountainous areas are different from floods in plains and relatively flat areas. Maximum floods in such places are short-term (hours to days) and have sharp peaks, inducing an intense flow of water foot-slopes and steep channels. Meanwhile, the rugged topography strengthens the mass movement downslope, where a steep channel provides the necessary conditions for the sudden transfer of coarse sediments and wood debris within the drainage networks. So, flood disturbance in mountainous rugged units is characterized by physical damage to rivers and riparian ecosystems (Swanson et al. 1998). In addition to the stream flow regime characteristics of such catchments, the characteristics of the riparian ecosystem itself contribute to its ecogeomorphic sensitivity. Due to the incision of valleys and the lack of lateral channel expansion, this zone is narrower in the small northern Talesh catchments. Consequently, their forest strip is also narrower than in larger catchments. Therefore, the preservation and survival of the riparian zone in these catchments is a priority in terms of flood occurrence.

3.3. Accuracy assessment of the logistic regression model

After examining the quality and quantity of the effects of independent variables on the dependent variable, it is time to evaluate the validity and performance of the logistic regression model. This evaluation is performed based on the Pseudo R² and ROC statistics. The PR² rate of the resulting regression model in the study area is 0.12, which indicates an acceptable fit of the regression line. Pir Bavaghar (2015) discriminated that if the values of this statistic are between 0.2 and 0.4, a good fit is obtained for the model. However, due to the high dispersion of experimental data and their pixel nature, values lower than 0.2 are expected. Arekhi et al. (2013) also obtained a PR² < 0.2. Kumar et al. (2014) only achieved a value of 0.29 in one of the four predictive models. However, the ROC statistic is more important than the PR² statistic and refers to the agreement between the actual deforestation map and the map obtained from the deforestation prediction model. This statistics which "signifies the predictive capability of the models for future probability of deforestation” (Saha et al. 2020), is affected by the quality and quantity of the input data. The ROC value in the Talesh catchments was 0.75, which indicates that the predictive regression model is good. This statistic was also 0.76 in the model of Arekhi et al. (2013). However, Siles 2009; Kumar et al. 2014; Pir Bavagar 2015; Pojiono et al. 2019; and Saha et al. 2020; achieved higher rates. The ROC values obtained in the studies of these researchers were 0.85, 0.87, 0.81, 0.86, and 0.87, respectively. These differences in the evaluation values of predictive models can be due to various reasons that may occur in all spatial-statistical analyses. The sampling method, both in preparing forest cover maps and in using deforestation samples, is one of the factors that affect the final results of the model. In this study, an attempt was made to correctly apply the principles of sampling in both stages with a good distribution. However, the concentration or dispersion of deforestation samples and their general spatial arrangement aren't under the authority of the researcher. The existence of forest loss areas in a scattered and point-like manner may make it somewhat difficult to explain the spatial distribution of deforestation. The reference for generating maps of independent variables and the spatial resolution of digital elevation models are other important factor in modeling changes in forest cover. The existence of redundant and inaccurate data in some places on the one hand, and the inadequacy in converting raw elevation data, is a common topic in terrain analysis. Resampling of the DEM, improvement of spatial resolution by moving windows, and applying a majority filter can lead to better fitting of regression models by reducing environmental heterogeneity and integrating erroneous pixels with accurate ones, which requires further studies. Finally, the selection of appropriate independent variables for modeling the occurrence of deforestation is effective in the predictive power of the models. In this research, although some variables such as aspect and distance to river, which were present in the initial model, had a negligible effect on deforestation and were removed from the final model, it should be noted that one of the advantages of using geomorphometry variables in modeling land cover changes is the stability of such variables, which reduces the uncertainty in modeling land cover change and increases their accuracy.

Ecogeomorphology and Ecohydrogeomorphology, as new and prospective interdisciplinary sciences, are establishing their place in the 21st century for the management and protection of river ecosystems. This research, considering the interdisciplinary approach, was able to identify and model deforestation based on geomorphological variables. The study also highlighted the need for the use of terrain analysis as a powerful tool for geomorphologists to analyze and predict environmental changes. In this context, a special viewpoint was considered, based on the idea that focusing solely on geomorphological factors in relation to forest loss does not mean neglecting the anthropogenic variables in the occurrence of deforestation. Rather, it is possible to indirectly infer the relationship between anthropogenic factors and forest loss through these physical factors, because the physical environment is a framework for anthropogenic processes. The advantage of using spatial-statistical analysis for the successful detection, quantification, and prediction of environmental changes (deforestation) was discriminated in this research. Change detection of forest loss between 1991 and 2022 in the Talesh catchments showed that about 90 km² (4.5%) of total catchments has been exposed to deforestation which is considerable. In terms of catchment scale, large catchments need conservation and support measures due to their low forest cover percentage, while small catchments need more attention due to their active morphodynamics and greater vulnerability to human interventions. The results of the logistic regression analysis of the relationships between geomorphological variables and the probability of deforestation showed that the probability of forest loss is higher in low altitudes and valleys, low slopes, flow divergence points, convex land surfaces, downstream, homogeneous flat areas, and low humidity areas. Therefore, it can be stated that the "ecogeomorphic sensitivity" of such environments to deforestation is higher than other environments. These areas implicitly refer to the factor of accessibility and the location of human interventions. Sensitive areas with a high probability of deforestation need more care and protection, and any human activities in these areas should be carried out consciously and based on environmental sustainability principles. In this regard, the focal point of deforestation in the important ecogeomorphic unit known as "riparian" is noteworthy. This area and its adjacent floodplain in the Talesh catchments are subject to various human interventions such as rice cultivation, gardening, construction of roads and dams, sand and gravel extraction, mining, etc. It is suggested that regional, urban, and rural policymakers and planners pay special attention to the protection and restoration of this section of the catchments.

Overall, considering the various aspects of relationships between geomorphologic variables and deforestation, we conclude that the selected variables reflect a relatively complete representation of the geomorphology of a region, as they specify the characteristics of the topographic position, slope position/landform, and roughness intensity, which largely control environmental conditions through hydrogeomorphic processes. These processes are involved in the distribution and redistribution of water and sediment within the drainage system and control soil formation and development. In addition, a combination of direct and indirect gradients, negative and positive gradients is observed here, which adds to the worth of the results. This study emphasizes the necessity and importance of applied hydrogeomorphic variables such as the convergence index (CI) and the topographic wetness index (TWI), which represent different aspects of hydrogeomorphic conditions such as water availability, water accumulation, and water shaping ability across the catchments, for future researchers. The scientific and concise understanding of the spatial-temporal variation in deforestation grasped through this research can contribute to the development of further research of this kind in other parts of Iran, where forest resource shortages. In addition, from a non-academic view, these results can serve as a guideline for the better implementation of forestry, watershed management, and prioritization of catchments in terms of control and protection measures regarding forests. To improve the performance of predictive models, it is suggested that the relationship between the geographical distribution of human activities and the occurrence of deforestation in combination with physical factors.

Funding: This work is based upon research funded by Iran National Science Foundation (INSF) under project No.4023975.

Competing interests: The authors declare no competing interests.

Conflict of interest: The authors declare no conflict of interest.

Author Contribution

All authors whose names appear on the submission have contributed to the conception or design of the work; data acquisition, preparation and analysis were done by F. Pourfarashzadeh. The results were checked by A.Madadi, S. Asghari and M. Gharchorlu. F. Pourfarashzadeh and M. Gharachorlu wrote the initial manuscript. Review and edition of the main manuscript was done by M. Gharchorlu, A. Madadi, and S. Asghari.

Acknowledgment

This study was supported by the Iran National Science Foundation (INSF). We greatly acknowledge them.

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No competing interests reported.

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Spatial-statistical modeling of deforestation from an ecogeomorphic perspective in typical Hyrcanian forests, northern Iran

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Study area

2.2. Data used

2.3. Methodology

2.3.1. Detection of negative forest cover changes

2.3.2. Extraction and Preparation of Geomorphological Variables

2.3.3. Modeling Negative Change of Forest Cover (Deforestation)

3. Results and Discussion

3.1. Detection of Negative Changes in Forest cover (Deforestation)

3.2. Relationship between geomorphologic variables and deforestation (Logistic Regression model)

3.3. Accuracy assessment of the logistic regression model

4. Conclusion

Declarations

Author Contribution

Acknowledgment

References

Additional Declarations

Status:

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