Forest landscape and its ecological quality: a stepwise spatiotemporal evaluation through patch-matrix model in Jhargram District, West Bengal State, India

doi:10.1016/j.regsus.2021.06.002

Abstract

Abstract:

Landscape conversion becomes a continuous process in a natural landscape for any strategic development. In a forest landscape mosaic, the conversion from non-forest land to forest land implies a constructive approach. Various bio-geographic processes are enriched and developed when the land was converted to forest land in a given landscape matrix. The present study evaluated how the increased forest cover improves the ecological quality of forest in Jhargram District of West Bengal State, India, from 1985 to 2015. The quality of forests includes dominance, fragmentation and connectivity, which are the basis ecological indicators of habitat structure. To address this issue, we extracted forest cover maps of 1985 and 2015 from land use/land cover classification. A grid framework was overlaid on these forest cover maps for patch-matrix model analysis. Reliable landscape ecological indices were used for the measurements of forest landscape quality in 1985 and 2015. Then a simple linear regression model was used to compare the results. Temporally, forest cover increased in Jhargram District from 1985 to 2015. The comparison of measurement indices depicts that although only a small amount of land was changed into forest land in the study area, this small change has greatly improved the structural compositional quality of the forest land. Compared with 1985, the forest land area increased by about 6930.56 hm² in 2015. This increased forest cover improved the basic landscape ecological characters, such as inter patch connectivity, forest core area, forest habitat dependence, forest habitat dominance and forest edge effect. As a result, the ecosystem function in Jhargram District has been improved, which again attracts wildlife and enriches biodiversity.

Key words: Forest cover, Landscape quality, Patch-matrix model, Ecological index, Forest fragmentation, Biodiversity

Mrinmay MANDAL, Nilanjana Das CHATTERJEE. Forest landscape and its ecological quality: a stepwise spatiotemporal evaluation through patch-matrix model in Jhargram District, West Bengal State, India[J]. Regional Sustainability, 2021, 2(2): 164-176.

Figures/Tables 12

Fig. 1.

Fig. 2.

Table 1

Landscape ecological indices used in patch-matrix model (McGarigal and Marks, 1995)."

Landscape ecological pattern	Index	Method	Description	Unit	Range	Ecological importance
Dominance	LPI	$n max a ij j = 1 A 100$	a_ij=area (m²) of patch ij class; A=total landscape area (m²)	%	0<LPI≤100	Patch dominance over the landscape (the higher the value, the higher the importance)
	MCA	$∑ j = 1 n a ij c n 1 110000$	$a ij c$ =core area (m²) of patch ij based on specified-edge depth (300m); n_i=number of patches in the landscape of patch type (class) i	hm²	≥0, without limit	Amount of patch class core from entire the landscape (the higher the value, the higher the importance)
	MPS	$∑ j = 1 n a ij n 1 (110000)$	a_ij=area (m²) of patch ij class; n_i $=$ total number of patches in ij class	hm²	≥0, without limit	Nature of patch class size from entire the landscape (the higher the value, the higher the importance)
	TCAI	$∑ a ij c a ij 100$	$a ij c$ =core area( $m 2$ ) of patch ij based on specified-edge depth (300 m); a_ij=area (m²) of patch ij	%	0<TCAI≤100	Patch class core dominance over the landscape (the higher the value, the higher the importance)
Fragmentation	TEI	Sum of perimeter of all corresponding patches		m	≥0, without limit	Amount of patch class perimeter (the higher the value, the lower the importance)
	EDI	$∑ k = 1 m e ik A$ (10000)	e_ik=total length (m) of edge in the landscape involving patch type (class) i; A=total landscape area (m²)	m/hm²	≥0, without limit	Patch class edge proportion to the total patch area (the higher the value, the lower the importance)
	AWMSI	$∑ j = 1 n 25 p ij a ij a ij ∑ j = 1 n a ij$	p_ij=perimeter (m) of patch ij; a_ij=area (m²) of patch ij; n_i=number of patches in the landscape of patch type (class) i	-	≥1, without limit	Patch class shape complexity of entire the landscape (higher the value, lower the importance)
	AWMPFD	$∑ j = 1 n 2 ln 25 p ij ln a ij a ij ∑ j = 1 n a ij$	a_ij=area (m²) of patch ij; p_ij=perimeter (m) of patch ij; n_i=number of patches in the landscape of patch type (class) i	-	1<AWMPFD≤2	Fractal nature of the patch class of entire the landscape (higher the value, lower the importance)
Connectivity	MNN	$∑ j = 1 n h ij n i$	h_ij=distance from ij to a nearest neighboring patch of the same type (class) i, based on patch edge-to-edge distance; n_i=number of patches in the landscape of patch type (class) i	m	≥0, without limit	Distance between nearest single class patch (the higher the value, the lower the importance)
Connectivity	MPI	$∑ j = 1 n ∑ s = 1 n a ijs h ijs 2 n i$	a_ijs=area (m²) of patch ijs within the specified neighborhood (m) of the patch ij; h_ijs=distance (m) between patch ijs and patch ijs, based on patch edge-to-edge distance	-	≥0, without limit	Compactness of single class patch (the higher the value, the higher the importance)

Table 1

Table 2

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Fig. 4.

Table 3

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Fig. 9.

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Ecological pattern	Index	Unit	Forest class of 1985	Forest class of 2015	Comparative value between 1985 and 2015	Ecological significance
Dominance	LPI	%	5.18	5.35	+0.15	Positive
	MCA	hm²	126.15	143.24	+17.09	Positive
	MPS	hm²	274.37	298.31	+23.94	Positive
	TCAI	%	40.17	42.83	+2.66	Positive
Fragmentation	TEI	×10⁶ m	2.33	2.39	+0.06	Negative
	EDI	m/hm²	30.68	28.87	-1.81	Positive
	AWMSI	-	1.99	1.98	-0.01	Positive
	AWMPFD	-	1.08	1.08	0.00	None
Connectivity	MNN	m	761.20	740.70	-20.50	Positive
Connectivity	MPI	-	15.18	19.38	+4.20	Positive

Ecological pattern	Index	Unit	r		Correlation coefficient (r) difference between 1985 and 2015	Ecological significance
Ecological pattern	Index	Unit	1985	2015		Ecological significance
Dominance	MCA	hm²	0.692	0.694	+0.002	Positive
	MPS	hm²	0.832	0.853	+0.021	Positive
	TCAI	%	0.845	0.856	+0.011	Positive
Fragmentation	EDI	m/hm²	0.880	0.874	-0.006	Negative
	AWMSI	-	0.670	0.652	-0.018	Positive
	AWMPFD	-	0.618	0.604	-0.014	Positive
Connectivity	MPI	-	0.408	0.500	+0.092	Positive
Connectivity	MNN	m	-0.154	-0.164	-0.010	Positive