An evaluation of land-use capability using the LESA method coupled with geostatistics in a GIS environment

Wang, Yingchao; Chen, Hu; Wang, Linshen

doi:10.36783/18069657rbcs20230062

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Division – Soil Use and Management | Commission – Land Use Planning • Rev. Bras. Ciênc. Solo 48 • 2024 • https://doi.org/10.36783/18069657rbcs20230062 copy

An evaluation of land-use capability using the LESA method coupled with geostatistics in a GIS environment

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Land-use effectiveness can be ensured by utilizing GIS and geostatistical tools in conjunction with land assessment methods to prevent soil erosion and salinization. This study employs a GIS-based LESA methodology, combined with geostatistics, to evaluate the land’s capacity to produce agricultural crops on calcareous soils. Land Evaluation for Agricultural Uses (LESA) key components are site assessment and land evaluation, with the former being non-soil-dependent and the latter being soil-dependent. Geostatical kriging was used to interpolate and generalize a GIS map of land capability. The study found that 27.88, 47.94, 18.76, and 5.41 % of the study area were unsuitable for crop farming, marginally suitable, moderately suitable, and highly suitable, respectively. Our research demonstrates that a flexible GIS framework can assist decision-makers in more accurately assessing land resources, including unsuitable, marginally-suitable, and reforested lands.

Keywords
agriculture activities; land capability; evaluation; site assessment

Properties	Min	Max	Mean	Std. dev
Clay (g kg^-1)	17.9	40.0	26.5	4.53
Silt (g kg^-1)	24.0	40.0	31.7	3.20
Sand (g kg^-1)	30.1	55.0	41.8	4.94
SOM (%)	1.74	3.56	2.79	0.43
CCE (%)	11.5	66.3	43.5	12.05
pH(H₂O)	7.0	7.8	7.6	0.21
EC (dS m^-1)	0.04	12.7	2.4	2.24
ESP (%)	1.0	110.5	14.4	13.6
Soil depth (m)	0.553	1.981	1.458	0.330

Adjacent land use compatibility	Score	Area in agriculture applying within 1.5 miles	Score	Accessing farming support services	Score
4 sides in agriculture use	100	80-100 %	100	<0.8 km	100
3 sides in agriculture use	80	60-80 %	75	0.8-1.6 km	80
2 sides in agriculture use	60	40-60 %	50	1.6-2.4 km	60
1 side in agriculture use	40	20-40 %	25	2.4-3.2 km	40
4 sides in non- agriculture applying	0	<20 %	0	>3.2 km	0

Distance of urban feeder highway	Score	Distance of public water	Score	Distance of public road	Score	Distance of public drainage systems	Score
<1.6 km	100	<60 m	100	<360 m	100	<60 m	100
1.6-3.2 km	80	60-360 m	80	360-1200 m	80	60-360 m	80
3.2-4.8 km	60	360-784 m	60	1200-1900 m	60	360-784 m	60
4.8-6.4 km	40	784-1200 m	40	1900-2700 m	40	784-1200 m	40
6.4-8.0 km	20	1200-2400 m	20	2700-3600 m	20	1200-2400 m	20
>8.0 km	0	>2400 m	0	>3600 m	0	>2400 m	0

Proximity of historic buildings	Score	Proximity of wetland and riparian	Score	Environmentally sensitive area	Score
>2400 m	100	>800 m	100	>2400 m	100
2400-1200 m	80	800-400 m	80	2400-1200 m	80
1200-784 m	60	400-200 m	60	1200-784 m	60
784-360 m	40	200-100 m	40	784-360 m	40
360-60 m	20	100-300 m	20	360-60 m	20
<60 m	0	<30 m	0	<60 m	0

Soil property	Model	Range (km)		k^*	Direction	Nugget	Sill	Nugget/Sill
Soil property	Model	minor	major	k^*	(degrees)	Nugget	Sill	Nugget/Sill
pH	Gaussian	28.9	79.40	2.74	165.1	0.02	0.07	0.28
EC	Spherical	21.2	31.4	1.48	58.8	1.71	4.61	0.37
CCE	Gaussian	11.00	33.04	3.00	53.6	50.14	147.51	0.34
ESP	Gaussian	22.7	31.6	1.39	55.7	39.02	300.78	0.13
SOM	Spherical	5.40	16.31	3.02	117.1	0.06	0.18	0.33
Clay	Gaussian	52.02	71.86	1.38	67.8	11.34	35.22	0.32
Silt	Spherical	5.75	12.81	2.23	16.8	2.04	8.34	0.25
Sand	Spherical	5.71	8.97	1.57	26.1	7.39	20.11	0.37
Soil depth	Gaussian	10.80	32.34	2.99	85.7	871.31	2337.11	0.37

Parameters	R²	RMSE	ME
pH	0.64	0.151	0.001
EC	0.71	0.694	0.012
CCE	0.52	12.78	0.165
ESP	0.61	15.98	0.961
SOM	0.73	0.46	-0.025
Clay	0.56	4.24	-0.044
Silt	0.55	3.40	-0.198
Sand	0.67	4.87	-0.063
Soil depth	0.51	39.24	0.741

Prime farmland classification score	Area		Soil productivity class	Area		Land capability class	Area
Prime farmland classification score	ha	%	%	ha	%	Land capability class	ha	%
0-10	-	-	0-10	-	-	I	90,207	27.88
10-20	-	-	10-20	-	-	II	155,116	47.94
20-30	-	-	20-30	-	-	III	60,702	18.76
30-40	2,940	0.91	30-40	-	-	IV	17,511	5.41
40-50	11,310	3.50	40-50	813	0.25	-	-	-
50-60	100,513	31.07	50-60	9,231	2.85	-	-	-
60-70	98,068	30.31	60-70	110,741	34.23	-	-	-
70-80	90,810	28.07	70-80	153,001	47.29	-	-	-
80-90	15,052	4.65	80-90	33,693	10.41	-	-	-
90-100	4,845	1.50	90-100	16,058	4.96	-	-	-

SA-1 score	Area		SA-2 score	Area		SA-3 score	Area
SA-1 score	ha	%	SA-2 score	ha	%	SA-3 score	ha	%
0-10	-	-	0-10	-	-	0-10	-	-
10-20	-	-	10-20	-	-	-10-20	-	-
20-30	-	-	20-30	-	-	20-30	-	-
30-40	11,000	0.00	30-40	-	-	30-40	-	-
40-50	1,495	0.46	40-50	-	-	40-50	127,000	0.04
50-60	12,693	3.92	50-60	687,000	0.21	50-60	14,279	4.41
60-70	92,030	28.44	60-70	22,746	7.03	60-70	98,728	30.52
70-80	154,222	47.67	70-80	89,652	27.71	70-80	30,595	9.46
80-90	56,035	17.32	80-90	210,452	65.05	80-90	157,953	48.82
90-100	7,052	2.18	90-100	-	-	90-100	21,854	6.75

LESA score	LESA score class	Area
		ha	%
40-50	Marginally-suitable	11,271	3.48
50-60	Marginally-suitable	157,759	48.76
60-70	Moderately-suitable	152,848	47.24
70-80	Moderately-suitable	1,659	0.51

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[1] * Corresponding author: E-mail: chenhu327@126.com