LawnStarter research · 2026 edition

The Fall Leaf Burden Index: Which U.S. Metros Face the Heaviest Cleanup?

Name: Fall Leaf Burden Index 2026: 383 U.S. metropolitan areas
Creator: LawnStarter

Charleston, West Virginia, has the heaviest fall leaf burden of any U.S. metro we measured. Not Atlanta, the City in a Forest, and not Charlotte, the City of Trees.

Here’s why: LawnStarter scored 383 metropolitan areas on how much deciduous forest surrounds them and how much yard-and-housing exposure they have, then estimated when leaf cleanup peaks in each one. Type your city to see where it lands.

And no matter where you are, here’s what to do with all those leaves: “Before you bag them up and haul them away, think about how you can reuse them right there in your landscape,” says Erika Lyon, educator, Agriculture & Natural Resources, with the Ohio State University Extension.

How to read this: “Metro” refers to one of the 383 metropolitan statistical areas (MSAs) in this analysis. The Leaf Burden Index is a relative 0–100 score for comparing metros; it is not a count of leaves, bags, or cleanup costs.

#1 Charleston, WVheaviest fall leaf burden
84% vs 6%deciduous forest: Charleston vs the typical metro
92%of 241 modeled metros peak in November
383U.S. metros scored

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By Alice Onofrei · Published November 10, 2026 · Data: public sources, 2024–2026 · How we calculated it

Key findings

What the 2026 Fall Leaf Burden Index shows

The short answer

Charleston, WV has the heaviest fall leaf burden of any U.S. metro we measured (index 100), ahead of Beckley, WV (98.0) and Huntington-Ashland, WV-KY-OH (92.8). The top five metros all include West Virginia, and all 7 metros whose first-listed state is West Virginia are in the top 50.
The South has 16 of the 25 most leaf-burdened metros and the Northeast has 9. No Midwestern or Western metro makes the top 25, and 28 of the 50 least-burdened metros are in the West.
Deciduous forest cover is the heaviest-weighted input and is strongly associated with rank (rank correlation 0.78). Charleston, WV is 84% deciduous forest; the typical (median) metro is 6%.
In our timing model, leaf cleanup peaks in November in 221 of the 241 metros (92%) where the model is calibrated, and in December in the 20 warmest (peak month = the month at the midpoint of the 28-day peak window). Among those metros the earliest peak windows begin Oct 27 and the latest begin Nov 19. The 124 metros with no fall peak estimate (mostly evergreen, low-canopy or frost-free) are not counted, and on the 46 metros used to fit it the model picks the right peak month 89% of the time versus 83% for guessing November everywhere, a modest edge.
Big metros rank mid-table or low. Among the ten most populous, Atlanta-Sandy Springs-Roswell, GA ranks highest (#52) and Phoenix-Mesa-Chandler, AZ lowest (#375).
Few people live in the most leaf-burdened metros. The 25 most leaf-burdened metros hold 9.4 million people, 3.2% of the 293.8 million living in the 383 metros scored.

How to interpret the results: This is a relative comparison among metros. Western and Pacific Northwest scores are directional because the leaf-supply measures may miss deciduous trees planted along streets and in yards. Peak windows are modeled estimates, and the satellite leaf-drop measure has important limitations. Full details appear in the limitations section below.

The numbers behind the headlines

84%

Charleston, WV is mostly deciduous forest

Deciduous forest covers 84.3% of Charleston’s land, compared with 6.0% in the typical metro.

7 of 7

West Virginia metros in the top 50

Every metro whose first-listed state is West Virginia ranks in the top 50. Pennsylvania has 6 of 16; Connecticut has 4 of 5.

16 + 9

South and Northeast hold the top 25

The South has 16 of the 25 heaviest metros and the Northeast has 9. The Midwest and West have none.

92%

of 241 modeled metros peak in November

In our timing model, cleanup peaks in November in 221 of the 241 metros where the model is calibrated, and in December in 20. 124 metros have no fall peak estimate.

28 of 50

least-burdened metros are Western

28 of the 50 lowest-scoring metros are in the West, where forests are sparse or conifer-dominated.

3.2%

of metro residents live in the top 25 metros

The 25 most leaf-burdened metros hold 9.4 million of 293.8 million people in the 383 metros (Census 2025 estimates).

#52 to #375

the ten biggest metros, ranked

Atlanta-Sandy Springs-Roswell, GA ranks #52; Phoenix-Mesa-Chandler, AZ ranks #375. New York, the largest metro, is #168.

124 vs 53 days

leaf-drop season, longest to shortest

The satellite-measured leaf-drop window is longest in Auburn-Opelika, Alabama, and Montgomery, Alabama (nearly tied), and shortest in Champaign-Urbana, Illinois (a median 95 days among the 345 metros with reliable data). This measure uses one year of satellite observations, so interpret it cautiously.

Oct 28–29

typical first fall frost

Among the 358 metros with a typical frost, the median metro's first frost (first day with a low below freezing, or 32 °F; 1991–2020 gridded normal) falls on Oct 28–29. It ranges from Sep 13 in Bozeman, MT to Dec 26 in Jacksonville, FL; 25 metros have no typical frost.

25.0 inches

wettest fall, versus the driest

Mount Vernon-Anacortes, WA averages 25.0 inches of September-November precipitation across the metro (1991–2020 normal); El Centro, CA averages 0.6 inches. Median metro: 10.2 inches. These are metro-wide gridded averages, not single weather-station readings. The metro covers Skagit County, which reaches into the Cascade Range.

82%

of homes are detached in the most yard-heavy metro

Wildwood-The Villages, FL has the highest share of detached single-family homes (82.3%); New York-Newark-Jersey City, NY-NJ has the lowest (34.9%). Median metro: 66.9%.

19.3

median Leaf Burden Index score

The typical metro scores 19.3 out of 100, while the top 10 average 83.3. A total of 78 metros score below 5; 47 score 50 or higher.

The ranking

Fall Leaf Burden Index 2026: all 383 metros ranked

Scores run from 0 to 100, with the heaviest-burdened metro set to 100. A higher score indicates a heavier modeled leaf-cleanup burden, based on deciduous forest, yard-and-housing exposure, and cleanup conditions. Select any metro to open its card.

The 10 most leaf-burdened U.S. metros

1
100.0
2
98.0
3
92.8
4
92.3
5
81.9
6
77.4
7
76.5
8
72.8
9
71.3
10
69.9

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Showing 383 of 383 metros

Fall Leaf Burden Index 2026 for 383 U.S. metropolitan areas. Regions are U.S. Census Bureau regions, assigned by the first state in the metro's name. Scores are shown to one decimal but metros are ranked on unrounded scores, so neighbors can show the same score. Peak windows are estimates; “extrapolated” means the metro is outside the range used to fit the model.

1	South	100.0	84.3%	70%	Oct 29	Nov 10 – Dec 8
2	South	98.0	75.7%	74%	Oct 24	Nov 7 – Dec 5
3	South	92.8	74.9%	71%	Oct 28	Nov 9 – Dec 7
4	South	92.3	68.8%	74%	Oct 27	Nov 9 – Dec 7
5	South	81.9	59.5%	77%	Oct 30	Nov 10 – Dec 8
6	South	77.4	58.0%	74%	Oct 20	Nov 5 – Dec 3
7	South	76.5	58.0%	74%	Oct 25	Nov 8 – Dec 6
8	Northeast	72.8	60.4%	71%	Oct 17	Nov 4 – Dec 2
9	South	71.3	48.5%	70%	Nov 03	Nov 12 – Dec 10
10	South	69.9	51.2%	71%	Oct 27	Nov 9 – Dec 7
11	South	68.3	51.4%	70%	Oct 27	Nov 9 – Dec 7
12	South	68.1	47.9%	70%	Oct 31	Nov 11 – Dec 9
13	South	67.5	52.0%	66%	Oct 25	Nov 8 – Dec 6
14	Northeast	67.1	49.2%	74%	Oct 13	Nov 2 – Nov 30
15	Northeast	66.9	53.3%	68%	Oct 18	Nov 5 – Dec 3
16	South	66.4	39.9%	72%	Nov 10	Nov 16 – Dec 14
17	Northeast	62.5	49.4%	68%	Oct 20	Nov 5 – Dec 3
18	Northeast	61.8	52.1%	65%	Oct 14	Nov 3 – Dec 1
19	South	61.2	47.0%	65%	Oct 30	Nov 10 – Dec 8
20	South	60.7	67.6%	56%	Oct 19	Nov 5 – Dec 3
21	Northeast	60.3	50.3%	61%	Oct 22	Nov 6 – Dec 4
22	Northeast	60.3	47.7%	69%	Oct 13	Nov 2 – Nov 30
23	South	59.2	50.0%	69%	Oct 29	Nov 10 – Dec 8
24	Northeast	58.8	43.9%	59%	Oct 19	Nov 5 – Dec 3
25	Northeast	58.0	42.9%	66%	Oct 07	Oct 30 – Nov 27
26	South	57.3	38.4%	76%	Oct 23	Nov 7 – Dec 5
27	South	57.0	31.5%	79%	Nov 07	Nov 14 – Dec 12
28	Northeast	56.1	37.5%	73%	Oct 08	Oct 31 – Nov 28
29	South	56.1	33.7%	70%	Nov 07	Nov 14 – Dec 12
30	South	55.8	40.0%	69%	Oct 29	Nov 10 – Dec 8
31	South	55.5	41.2%	72%	Nov 04	Nov 13 – Dec 11
32	Northeast	54.9	39.0%	56%	Oct 23	Nov 7 – Dec 5
33	South	54.8	30.9%	73%	Nov 04	Nov 13 – Dec 11
34	Northeast	54.7	48.2%	62%	Oct 19	Nov 5 – Dec 3
35	Northeast	54.5	42.0%	59%	Oct 20	Nov 5 – Dec 3
36	South	54.4	33.0%	70%	Oct 31	Nov 11 – Dec 9
37	South	54.0	45.0%	66%	Oct 24	Nov 7 – Dec 5
38	Northeast	54.0	43.1%	68%	Oct 11	Nov 1 – Nov 29
39	South	53.6	45.3%	70%	Nov 01	Nov 11 – Dec 9
40	South	53.4	40.2%	65%	Oct 25	Nov 8 – Dec 6
41	South	52.4	47.2%	62%	Oct 21	Nov 6 – Dec 4
42	South	51.7	36.0%	70%	Nov 02	Nov 12 – Dec 10
43	South	51.6	25.6%	70%	Nov 08	Nov 15 – Dec 13
44	Northeast	51.1	43.5%	65%	Oct 07	Oct 30 – Nov 27
45	Midwest	51.0	34.4%	78%	Oct 07	Oct 30 – Nov 27
46	Northeast	50.8	57.3%	57%	Oct 17	Nov 4 – Dec 2
47	South	50.6	30.6%	68%	Nov 12	Nov 17 – Dec 15
48	Midwest	49.8	40.6%	73%	Oct 29	Nov 10 – Dec 8
49	South	49.4	34.8%	64%	Nov 06	Nov 14 – Dec 12
50	Midwest	49.2	57.5%	57%	Oct 18	Nov 5 – Dec 3
51	South	49.1	26.5%	70%	Nov 02	Nov 12 – Dec 10
52	South	48.5	26.0%	66%	Nov 13	Nov 17 – Dec 15
53	South	48.4	19.4%	79%	Nov 10	Nov 16 – Dec 14
54	Midwest	47.8	28.4%	78%	Oct 11	Nov 1 – Nov 29
55	South	47.5	30.8%	74%	Nov 01	Nov 11 – Dec 9
56	South	46.7	23.5%	72%	Nov 09	Nov 15 – Dec 13
57	South	46.4	28.2%	64%	Nov 03	Nov 12 – Dec 10
58	South	46.2	37.7%	64%	Oct 30	Nov 10 – Dec 8
59	Midwest	46.1	30.8%	70%	Oct 25	Nov 8 – Dec 6
60	Northeast	46.0	35.1%	68%	Oct 19	Nov 5 – Dec 3
61	South	45.8	23.5%	64%	Nov 11	Nov 16 – Dec 14
62	Northeast	45.1	34.3%	53%	Oct 27	Nov 9 – Dec 7
63	Northeast	44.8	37.4%	62%	Oct 11	Nov 1 – Nov 29
64	Midwest	44.7	30.6%	77%	Oct 19	Nov 5 – Dec 3
65	South	44.3	42.5%	61%	Oct 23	Nov 7 – Dec 5
66	Northeast	44.2	32.1%	56%	Oct 14	Nov 3 – Dec 1
67	Midwest	43.8	31.0%	76%	Oct 22	Nov 6 – Dec 4
68	Northeast	43.2	33.9%	65%	Oct 13	Nov 2 – Nov 30
69	South	43.0	34.4%	70%	Oct 27	Nov 9 – Dec 7
70	Northeast	42.6	36.2%	69%	Oct 27	Nov 9 – Dec 7
71	Northeast	42.6	28.8%	59%	Oct 13	Nov 2 – Nov 30
72	Midwest	42.5	36.7%	73%	Oct 29	Nov 10 – Dec 8
73	South	42.3	40.5%	64%	Oct 29	Nov 10 – Dec 8
74	South	42.1	28.3%	66%	Nov 07	Nov 14 – Dec 12
75	Northeast	41.7	43.5%	56%	Oct 29	Nov 10 – Dec 8
76	Northeast	41.6	38.9%	57%	Oct 19	Nov 5 – Dec 3
77	South	41.4	17.1%	67%	Nov 16	Nov 19 – Dec 17
78	South	41.3	26.4%	60%	Nov 07	Nov 14 – Dec 12
79	South	41.2	26.9%	71%	Nov 03	Nov 12 – Dec 10
80	Northeast	41.1	27.8%	57%	Oct 13	Nov 2 – Nov 30
81	Midwest	40.8	33.5%	66%	Oct 29	Nov 10 – Dec 8
82	Midwest	39.6	33.5%	73%	Oct 22	Nov 6 – Dec 4
83	Midwest	39.4	41.1%	66%	Oct 10	Nov 1 – Nov 29
84	Midwest	39.0	28.5%	66%	Oct 29	Nov 10 – Dec 8
85	South	38.9	16.0%	66%	Nov 16	Nov 19 – Dec 17
86	South	38.2	33.3%	68%	Oct 25	Nov 8 – Dec 6
87	South	38.1	25.3%	59%	Nov 14	Nov 18 – Dec 16
88	Northeast	37.9	5.2%	82%	Nov 06	Nov 14 – Dec 12
89	Northeast	37.5	32.1%	56%	Oct 12	Nov 2 – Nov 30
90	South	37.3	21.0%	66%	Nov 06	Nov 14 – Dec 12
91	Northeast	36.7	23.2%	55%	Oct 25	Nov 8 – Dec 6
92	Northeast	36.6	32.5%	58%	Oct 09	Oct 31 – Nov 28
93	Northeast	36.3	25.6%	75%	Oct 26	Nov 8 – Dec 6
94	South	36.1	9.0%	74%	Nov 08	Nov 15 – Dec 13
95	South	35.6	24.1%	69%	Oct 31	Nov 11 – Dec 9
96	Northeast	35.3	12.3%	67%	Oct 11	Nov 1 – Nov 29
97	South	34.8	10.8%	72%	Nov 14	Nov 18 – Dec 16
98	South	34.8	11.7%	70%	Nov 14	Nov 18 – Dec 16
99	South	34.1	30.5%	72%	Nov 02	Nov 12 – Dec 10
100	South	34.1	14.5%	59%	Nov 16	Nov 19 – Dec 17
101	South	34.0	11.8%	61%	Nov 14	Nov 18 – Dec 16
102	Northeast	34.0	27.9%	64%	Oct 31	Nov 11 – Dec 9
103	Midwest	33.7	19.2%	74%	Sep 25	Oct 24 – Nov 21 extrapolated
104	Northeast	33.4	17.6%	57%	Oct 12	Nov 2 – Nov 30
105	Northeast	33.0	34.9%	56%	Oct 26	Nov 8 – Dec 6
106	Midwest	33.0	24.8%	75%	Oct 02	Oct 28 – Nov 25
107	South	32.6	20.7%	73%	Nov 03	Nov 12 – Dec 10
108	South	32.5	10.3%	68%	Nov 04	Nov 13 – Dec 11
109	South	32.2	13.3%	68%	Nov 14	Nov 18 – Dec 16
110	West	32.2	3.3%	71%	Nov 12	Nov 17 – Dec 15
111	South	32.1	0.0%	77%	Nov 25	No fall peak estimated
112	West	32.0	2.5%	69%	Nov 06	Nov 14 – Dec 12
113	Midwest	31.9	26.0%	71%	Oct 29	Nov 10 – Dec 8
114	Northeast	31.6	28.9%	62%	Oct 27	Nov 9 – Dec 7
115	Midwest	31.4	25.2%	72%	Oct 17	Nov 4 – Dec 2
116	Midwest	30.7	15.2%	75%	Oct 13	Nov 2 – Nov 30
117	Northeast	30.5	10.8%	64%	Oct 04	Oct 29 – Nov 26
118	Northeast	29.4	15.2%	58%	Oct 09	Oct 31 – Nov 28
119	Northeast	29.4	34.6%	50%	Oct 12	Nov 2 – Nov 30
120	West	29.3	2.8%	73%	Nov 02	Nov 12 – Dec 10
121	South	29.1	0.0%	73%	Nov 26	No fall peak estimated
122	South	28.8	0.0%	71%	Nov 27	No fall peak estimated
123	Midwest	28.7	17.2%	77%	Oct 19	Nov 5 – Dec 3
124	Midwest	28.4	24.8%	75%	Oct 24	Nov 7 – Dec 5
125	South	28.4	22.6%	70%	Nov 02	Nov 12 – Dec 10
126	Northeast	28.3	27.0%	57%	Oct 07	Oct 30 – Nov 27
127	South	28.2	0.2%	67%	Nov 24	No fall peak estimated
128	South	28.2	6.0%	67%	Nov 10	Nov 16 – Dec 14
129	South	28.1	16.4%	70%	Nov 07	Nov 14 – Dec 12
130	South	28.0	29.1%	44%	Nov 02	Nov 12 – Dec 10
131	Midwest	27.8	25.2%	67%	Oct 03	Oct 28 – Nov 25
132	South	27.2	0.0%	64%	Nov 17	No fall peak estimated
133	Northeast	27.1	21.8%	65%	Oct 18	Nov 5 – Dec 3
134	Midwest	27.1	16.7%	74%	Oct 16	Nov 4 – Dec 2
135	Northeast	27.0	20.3%	60%	Oct 21	Nov 6 – Dec 4
136	Midwest	26.9	28.5%	63%	Oct 27	Nov 9 – Dec 7
137	South	26.3	17.4%	73%	Nov 01	Nov 11 – Dec 9
138	South	26.2	0.0%	72%	Nov 24	No fall peak estimated
139	South	25.9	5.0%	67%	Nov 16	Nov 19 – Dec 17
140	Midwest	25.8	19.2%	65%	Oct 17	Nov 4 – Dec 2
141	West	25.7	3.6%	67%	Nov 04	Nov 13 – Dec 11
142	Midwest	25.7	22.2%	74%	Oct 20	Nov 5 – Dec 3
143	Midwest	25.5	15.0%	76%	Oct 17	Nov 4 – Dec 2
144	South	25.4	0.7%	66%	Nov 17	No fall peak estimated
145	South	25.4	4.9%	73%	Nov 18	Nov 20 – Dec 18 extrapolated
146	Midwest	25.3	14.0%	70%	Oct 18	Nov 5 – Dec 3
147	South	25.2	0.0%	66%	Nov 14	Nov 18 – Dec 16
148	South	25.2	0.0%	65%	Dec 01	No fall peak estimated
149	South	24.8	0.0%	67%	none typical	No fall peak estimated
150	Midwest	24.8	17.5%	69%	Oct 14	Nov 3 – Dec 1
151	Northeast	24.6	11.4%	47%	Oct 18	Nov 5 – Dec 3
152	West	24.1	22.3%	73%	Oct 01	Oct 27 – Nov 24
153	South	24.1	1.6%	65%	Nov 30	Nov 26 – Dec 24 extrapolated
154	Midwest	23.8	17.1%	74%	Nov 01	Nov 11 – Dec 9
155	South	23.8	0.0%	65%	Nov 13	Nov 17 – Dec 15
156	South	23.7	0.0%	62%	Nov 20	No fall peak estimated
157	South	23.6	0.0%	65%	Nov 21	No fall peak estimated
158	South	23.6	5.3%	62%	Nov 07	Nov 14 – Dec 12
159	Northeast	23.5	9.3%	68%	Nov 04	Nov 13 – Dec 11
160	South	23.4	0.9%	69%	Nov 21	Nov 21 – Dec 19 extrapolated
161	West	23.4	0.0%	71%	Oct 30	Nov 10 – Dec 8
162	South	23.2	23.3%	45%	Nov 04	Nov 13 – Dec 11
163	Northeast	23.1	15.0%	48%	Oct 29	Nov 10 – Dec 8
164	Midwest	23.1	19.4%	76%	Oct 29	Nov 10 – Dec 8
165	South	22.8	4.4%	68%	Nov 16	Nov 19 – Dec 17
166	West	22.8	1.8%	56%	Nov 04	Nov 13 – Dec 11
167	Midwest	22.6	23.2%	61%	Oct 21	Nov 6 – Dec 4
168	Northeast	22.5	23.2%	35%	Oct 29	Nov 10 – Dec 8
169	Northeast	22.4	10.8%	55%	Nov 04	Nov 13 – Dec 11
170	Midwest	22.4	14.3%	76%	Oct 17	Nov 4 – Dec 2
171	South	22.4	17.8%	72%	Nov 21	Nov 21 – Dec 19 extrapolated
172	South	22.4	0.0%	64%	Dec 26	No fall peak estimated
173	West	22.2	1.1%	60%	Nov 08	Nov 15 – Dec 13
174	South	22.1	0.0%	62%	Nov 22	No fall peak estimated
175	West	21.9	0.3%	61%	Nov 12	Nov 17 – Dec 15
176	Midwest	21.7	13.5%	74%	Oct 15	Nov 3 – Dec 1
177	South	21.3	0.0%	82%	none typical	No fall peak estimated
178	South	21.1	0.0%	65%	Nov 29	No fall peak estimated
179	South	21.0	0.4%	67%	Nov 14	Nov 18 – Dec 16
180	South	20.9	0.0%	57%	Dec 02	No fall peak estimated
181	South	20.9	17.7%	72%	Nov 06	No fall peak estimated
182	Midwest	20.8	13.5%	68%	Oct 10	Nov 1 – Nov 29
183	South	20.6	0.2%	67%	Nov 16	Nov 19 – Dec 17
184	West	20.1	1.9%	63%	Oct 22	Nov 6 – Dec 4
185	South	20.0	0.0%	70%	Dec 06	No fall peak estimated
186	South	19.9	0.0%	63%	Dec 06	No fall peak estimated
187	West	19.9	0.0%	74%	Oct 10	Nov 1 – Nov 29
188	South	19.8	0.0%	66%	Nov 28	No fall peak estimated
189	South	19.6	0.4%	66%	Nov 08	Nov 15 – Dec 13
190	South	19.6	1.3%	68%	Nov 18	Nov 20 – Dec 18 extrapolated
191	South	19.6	0.0%	60%	Dec 05	No fall peak estimated
192	Northeast	19.3	16.5%	43%	Nov 02	Nov 12 – Dec 10
193	South	19.1	0.1%	52%	Dec 02	No fall peak estimated
194	South	19.0	0.9%	60%	Nov 16	Nov 19 – Dec 17
195	Midwest	19.0	16.2%	57%	Oct 16	Nov 4 – Dec 2
196	South	19.0	15.3%	75%	Oct 31	Nov 11 – Dec 9
197	West	19.0	0.7%	59%	Nov 16	Nov 19 – Dec 17
198	South	18.6	0.4%	70%	Nov 25	No fall peak estimated
199	West	18.5	0.1%	63%	Nov 06	Nov 14 – Dec 12
200	Midwest	18.4	15.9%	69%	Oct 27	Nov 9 – Dec 7
201	Midwest	18.3	15.6%	69%	Oct 19	Nov 5 – Dec 3
202	West	18.0	0.1%	65%	Oct 25	Nov 8 – Dec 6
203	South	17.7	0.0%	68%	none typical	No fall peak estimated
204	South	17.4	0.1%	63%	Nov 09	Nov 15 – Dec 13
205	South	17.2	0.0%	53%	Nov 17	No fall peak estimated
206	West	16.9	0.1%	63%	Dec 18	No fall peak estimated
207	South	16.7	13.5%	64%	Oct 29	Nov 10 – Dec 8
208	South	16.7	0.2%	70%	Nov 08	Nov 15 – Dec 13
209	Midwest	16.5	14.7%	75%	Oct 24	Nov 7 – Dec 5
210	South	16.4	1.2%	66%	Nov 07	Nov 14 – Dec 12
211	Midwest	16.3	10.3%	78%	Oct 17	Nov 4 – Dec 2
212	South	16.1	0.0%	70%	none typical	No fall peak estimated
213	Midwest	16.1	10.7%	76%	Oct 17	Nov 4 – Dec 2
214	South	16.0	0.0%	52%	Dec 01	No fall peak estimated
215	Midwest	15.9	15.2%	71%	Oct 13	Nov 2 – Nov 30
216	Northeast	15.6	15.3%	55%	Nov 02	Nov 12 – Dec 10
217	West	15.4	15.8%	71%	Oct 08	Oct 31 – Nov 28
218	West	15.4	0.9%	71%	Nov 09	Nov 15 – Dec 13
219	Midwest	15.2	14.3%	60%	Oct 08	Oct 31 – Nov 28
220	Midwest	14.6	10.3%	78%	Oct 25	Nov 8 – Dec 6
221	Midwest	14.5	11.3%	70%	Oct 27	Nov 9 – Dec 7
222	South	14.5	0.9%	65%	Nov 24	Nov 23 – Dec 21 extrapolated
223	Midwest	14.4	15.1%	69%	Oct 05	Oct 29 – Nov 26
224	South	14.2	0.0%	66%	none typical	No fall peak estimated
225	South	14.0	0.0%	58%	none typical	No fall peak estimated
226	Midwest	13.7	12.2%	74%	Oct 25	Nov 8 – Dec 6
227	Midwest	13.5	16.9%	55%	Oct 10	Nov 1 – Nov 29
228	Midwest	13.5	11.6%	77%	Oct 17	Nov 4 – Dec 2
229	Midwest	13.4	17.5%	54%	Oct 27	Nov 9 – Dec 7
230	Midwest	13.3	9.6%	73%	Nov 02	Nov 12 – Dec 10
231	West	13.2	0.5%	64%	Nov 10	Nov 16 – Dec 14
232	West	12.5	0.0%	58%	Sep 22	Oct 23 – Nov 20 extrapolated
233	Midwest	12.4	10.1%	75%	Oct 24	Nov 7 – Dec 5
234	West	12.4	0.3%	67%	Dec 18	No fall peak estimated
235	South	12.4	9.2%	63%	Nov 22	Nov 22 – Dec 20 extrapolated
236	South	12.4	0.0%	50%	Dec 13	No fall peak estimated
237	Midwest	12.4	9.4%	67%	Oct 13	Nov 2 – Nov 30
238	South	12.4	0.0%	56%	none typical	No fall peak estimated
239	Midwest	12.4	7.8%	54%	Oct 13	Nov 2 – Nov 30
240	South	12.4	3.5%	66%	Nov 05	Nov 13 – Dec 11
241	South	12.3	0.0%	62%	none typical	No fall peak estimated
242	Midwest	12.0	8.4%	65%	Oct 15	Nov 3 – Dec 1
243	Midwest	11.9	8.4%	65%	Oct 16	Nov 4 – Dec 2
244	West	11.9	10.1%	66%	Sep 29	No fall peak estimated
245	Midwest	11.5	7.4%	70%	Oct 13	Nov 2 – Nov 30
246	South	11.3	0.2%	60%	Nov 09	Nov 15 – Dec 13
247	South	11.1	0.2%	62%	Dec 11	No fall peak estimated
248	South	10.8	0.0%	71%	none typical	No fall peak estimated
249	Midwest	10.5	5.3%	76%	Oct 17	Nov 4 – Dec 2
250	West	10.4	0.1%	66%	Oct 07	Oct 30 – Nov 27
251	South	10.4	0.0%	69%	Dec 10	No fall peak estimated
252	Midwest	10.3	8.1%	74%	Oct 22	Nov 6 – Dec 4
253	South	10.2	0.0%	55%	none typical	No fall peak estimated
254	South	10.1	0.0%	68%	none typical	No fall peak estimated
255	Midwest	10.0	8.0%	74%	Oct 13	Nov 2 – Nov 30
256	South	10.0	0.1%	49%	Nov 10	Nov 16 – Dec 14
257	Midwest	9.8	9.4%	68%	Oct 07	Oct 30 – Nov 27
258	Midwest	9.7	4.4%	70%	Oct 17	Nov 4 – Dec 2
259	Midwest	9.6	8.2%	77%	Oct 25	Nov 8 – Dec 6
260	South	9.5	0.0%	66%	none typical	No fall peak estimated
261	Midwest	9.5	8.5%	72%	Oct 19	Nov 5 – Dec 3
262	South	9.4	0.0%	58%	none typical	No fall peak estimated
263	West	9.4	1.1%	68%	Dec 05	No fall peak estimated
264	Midwest	9.2	7.8%	73%	Oct 19	Nov 5 – Dec 3
265	South	8.8	0.0%	43%	none typical	No fall peak estimated
266	Midwest	8.6	9.3%	65%	Oct 15	Nov 3 – Dec 1
267	Midwest	8.6	6.1%	72%	Oct 14	Nov 3 – Dec 1
268	South	8.4	0.0%	56%	none typical	No fall peak estimated
269	South	8.3	3.6%	59%	Nov 26	Nov 24 – Dec 22 extrapolated
270	South	7.9	0.0%	60%	none typical	No fall peak estimated
271	Midwest	7.7	6.6%	73%	Oct 19	Nov 5 – Dec 3
272	Midwest	7.6	3.5%	72%	Oct 10	Nov 1 – Nov 29
273	South	7.5	0.0%	69%	Dec 15	No fall peak estimated
274	Midwest	7.5	6.3%	52%	Oct 17	Nov 4 – Dec 2
275	West	7.4	0.0%	68%	Oct 17	Nov 4 – Dec 2
276	South	7.4	3.8%	70%	Nov 09	Nov 15 – Dec 13
277	Midwest	7.4	8.6%	63%	Oct 15	Nov 3 – Dec 1
278	West	7.4	0.2%	67%	Dec 08	No fall peak estimated
279	West	7.2	0.0%	66%	Oct 10	Nov 1 – Nov 29
280	West	7.2	1.3%	60%	Dec 12	Dec 2 – Dec 30 extrapolated
281	West	7.1	0.1%	70%	Sep 22	No fall peak estimated
282	Midwest	7.1	9.5%	55%	Oct 24	Nov 7 – Dec 5
283	Midwest	7.1	5.6%	68%	Oct 29	Nov 10 – Dec 8
284	Midwest	7.0	7.0%	70%	Oct 12	No fall peak estimated
285	South	6.9	3.0%	68%	Nov 24	Nov 23 – Dec 21 extrapolated
286	West	6.9	0.3%	57%	Oct 05	Oct 29 – Nov 26
287	West	6.7	0.2%	58%	Sep 13	Oct 18 – Nov 15 extrapolated
288	West	6.6	2.1%	72%	Dec 18	Dec 3 – Dec 31 extrapolated
289	South	6.5	3.2%	53%	Nov 26	Nov 24 – Dec 22 extrapolated
290	West	6.4	7.2%	64%	Oct 07	Oct 30 – Nov 27
291	West	6.3	0.0%	76%	Sep 22	Oct 23 – Nov 20 extrapolated
292	South	6.3	2.1%	66%	Dec 01	Nov 26 – Dec 24 extrapolated
293	South	6.2	0.0%	66%	none typical	No fall peak estimated
294	Midwest	6.1	4.8%	76%	Oct 17	Nov 4 – Dec 2
295	West	6.1	2.7%	67%	Sep 29	No fall peak estimated
296	Midwest	6.0	3.5%	70%	Oct 11	Nov 1 – Nov 29
297	South	5.5	1.1%	66%	Nov 22	No fall peak estimated
298	West	5.5	0.2%	64%	Sep 22	Oct 23 – Nov 20 extrapolated
299	Midwest	5.5	5.2%	70%	Oct 16	Nov 4 – Dec 2
300	Midwest	5.3	8.3%	52%	Oct 17	Nov 4 – Dec 2
301	West	5.2	3.3%	73%	Nov 04	No fall peak estimated
302	West	5.2	0.2%	49%	none typical	No fall peak estimated
303	West	5.2	1.0%	75%	Nov 26	No fall peak estimated
304	Midwest	5.1	2.9%	66%	Oct 12	Nov 2 – Nov 30
305	Midwest	5.1	3.8%	78%	Oct 19	Nov 5 – Dec 3
306	West	4.8	0.0%	63%	Nov 28	No fall peak estimated
307	West	4.5	0.0%	63%	Dec 19	No fall peak estimated
308	Midwest	4.2	3.8%	71%	Oct 17	Nov 4 – Dec 2
309	Midwest	4.1	4.6%	62%	Oct 09	Oct 31 – Nov 28
310	West	4.0	0.0%	56%	Dec 15	No fall peak estimated
311	Midwest	3.9	5.8%	58%	Oct 13	No fall peak estimated
312	South	3.9	0.9%	66%	Dec 12	No fall peak estimated
313	Midwest	3.7	3.6%	72%	Oct 09	No fall peak estimated
314	West	3.6	0.7%	68%	Oct 05	Oct 29 – Nov 26
315	South	3.6	3.1%	73%	Nov 12	No fall peak estimated
316	West	3.5	0.4%	69%	Dec 04	No fall peak estimated
317	West	3.5	0.1%	60%	Oct 15	No fall peak estimated
318	Midwest	3.4	2.9%	71%	Oct 29	No fall peak estimated
319	West	3.4	0.9%	73%	Sep 21	No fall peak estimated
320	West	3.4	0.0%	52%	Dec 04	No fall peak estimated
321	West	3.2	0.0%	70%	Oct 16	Nov 4 – Dec 2
322	West	3.2	0.1%	67%	Sep 29	No fall peak estimated
323	West	3.2	0.7%	57%	Oct 01	Oct 27 – Nov 24
324	Midwest	3.1	3.4%	60%	Oct 15	Nov 3 – Dec 1
325	West	3.0	0.0%	68%	Nov 28	No fall peak estimated
326	West	2.7	0.0%	76%	Dec 16	No fall peak estimated
327	West	2.7	0.1%	50%	none typical	No fall peak estimated
328	South	2.6	2.4%	73%	Nov 15	No fall peak estimated
329	South	2.5	0.0%	41%	none typical	No fall peak estimated
330	West	2.4	0.3%	63%	Oct 13	No fall peak estimated
331	West	2.4	0.0%	48%	none typical	No fall peak estimated
332	West	2.3	0.0%	75%	Dec 18	No fall peak estimated
333	South	2.3	1.7%	80%	Nov 05	No fall peak estimated
334	West	2.2	0.2%	76%	Oct 03	No fall peak estimated
335	Midwest	2.2	1.3%	72%	Oct 10	Nov 1 – Nov 29
336	South	2.1	0.8%	73%	Nov 17	No fall peak estimated
337	Midwest	2.0	0.1%	65%	Oct 04	No fall peak estimated
338	Midwest	1.9	2.4%	60%	Oct 17	Nov 4 – Dec 2
339	West	1.8	0.0%	67%	Nov 06	No fall peak estimated
340	South	1.8	0.0%	68%	Dec 22	No fall peak estimated
341	West	1.7	0.1%	59%	Oct 19	Nov 5 – Dec 3
342	West	1.6	0.3%	72%	Nov 30	No fall peak estimated
343	West	1.5	1.5%	61%	Oct 12	Nov 2 – Nov 30
344	West	1.5	0.3%	67%	Oct 13	No fall peak estimated
345	West	1.5	0.1%	68%	Sep 29	No fall peak estimated
346	Midwest	1.4	2.3%	52%	Oct 02	No fall peak estimated
347	South	1.4	0.2%	66%	none typical	No fall peak estimated
348	South	1.3	0.0%	65%	none typical	No fall peak estimated
349	West	1.3	0.0%	72%	Dec 19	No fall peak estimated
350	South	1.2	0.0%	65%	none typical	No fall peak estimated
351	Midwest	1.1	0.4%	71%	Oct 13	No fall peak estimated
352	West	1.0	0.1%	76%	Oct 10	No fall peak estimated
353	West	1.0	0.0%	66%	Oct 26	No fall peak estimated
354	West	1.0	0.0%	74%	Dec 16	No fall peak estimated
355	Midwest	0.9	1.0%	56%	Oct 17	No fall peak estimated
356	West	0.8	0.0%	65%	Nov 09	No fall peak estimated
357	Midwest	0.7	0.9%	61%	Oct 05	No fall peak estimated
358	Midwest	0.7	0.8%	58%	Sep 29	No fall peak estimated
359	Midwest	0.6	0.9%	56%	Sep 28	No fall peak estimated
360	West	0.6	0.2%	77%	Oct 07	No fall peak estimated
361	West	0.6	0.0%	62%	Nov 22	No fall peak estimated
362	West	0.5	0.1%	60%	Oct 12	No fall peak estimated
363	Midwest	0.5	0.9%	47%	Oct 03	No fall peak estimated
364	West	0.5	0.0%	69%	Dec 14	No fall peak estimated
365	South	0.5	0.1%	71%	Nov 13	No fall peak estimated
366	West	0.4	0.0%	66%	Oct 25	No fall peak estimated
367	West	0.4	0.1%	57%	Oct 15	No fall peak estimated
368	West	0.4	0.0%	71%	Dec 06	No fall peak estimated
369	West	0.3	0.0%	71%	Sep 27	No fall peak estimated
370	South	0.3	0.0%	70%	Dec 10	No fall peak estimated
371	West	0.3	0.0%	60%	Dec 02	No fall peak estimated
372	West	0.2	0.0%	60%	Dec 02	No fall peak estimated
373	West	0.2	0.0%	60%	Nov 09	No fall peak estimated
374	South	0.2	0.1%	70%	Oct 28	No fall peak estimated
375	West	0.2	0.0%	65%	Dec 14	No fall peak estimated
376	West	0.1	0.0%	66%	Sep 26	No fall peak estimated
377	South	0.1	0.1%	67%	Nov 07	No fall peak estimated
378	West	0.1	0.0%	73%	Oct 05	No fall peak estimated
379	South	0.1	0.0%	68%	Nov 08	No fall peak estimated
380	South	0.1	0.0%	58%	Nov 13	No fall peak estimated
381	South	0.0	0.0%	62%	Nov 14	No fall peak estimated
382	West	0.0	0.0%	62%	none typical	No fall peak estimated
383	West	0.0	0.0%	55%	Dec 24	No fall peak estimated

Interactive map

Where leaf burden is heaviest

Each dot is a metro. Darker means a heavier fall leaf burden. Switch the layer to see what drives the pattern, or zoom to a region.

Dots are placed at each metro's Census internal point. Metros are metropolitan statistical areas, which can include a lot of rural land; the map shows the metro, not its built-up core. A text version of every number is in the ranking table above.

Leaf cleanup calendar

When does leaf cleanup peak in your city?

Drag the date and watch the season move across the country. Orange dots are metros in their estimated peak cleanup window; green dots have not reached it yet; gray dots are past it. White dots have no fall peak estimate.

Date between October 15 and December 31

How many metros are in peak, day by day

259 metros have a peak estimate. 124 do not (see methodology).

Metros peaking on this date

Your metro

Pick a metro in the finder at the top of the page to see its peak window and first frost on one timeline.

How to read the calendar

Peak windows are modeled, not measured. The estimated end of the cleanup season is 191.5 + 0.498 × the day of the year of the typical first autumn frost (1991–2020), and the peak window is the 28 days ending there. Calibration on 46 fall-peaking metros gave an average error of about 6 days for the season end, and the right peak month 89% of the time versus 83% for guessing November everywhere. These windows estimate cleanup timing; they do not measure when leaves actually fall.

Regions and states

Leaf burden by region and by state

Explore expert advice on fallen leaves and fall-color destinations in West Virginia and Ohio.

By region

Northeast (49 metros)

Median index 41.6 · 9 in the top 25 · 16 in the top 50 · 0 in the bottom 50. Highest-ranked: Altoona, PA (#8).

South (157 metros)

Median index 23.8 · 16 in the top 25 · 31 in the top 50 · 12 in the bottom 50. Highest-ranked: Charleston, WV (#1).

Midwest (94 metros)

Median index 13.4 · 0 in the top 25 · 3 in the top 50 · 10 in the bottom 50. Highest-ranked: Traverse City, MI (#45).

West (83 metros)

Median index 3.6 · 0 in the top 25 · 0 in the top 50 · 28 in the bottom 50. Highest-ranked: Bremerton, WA (#110).

Why You Shouldn’t Let Leaves Pile Up on Your Lawn

A thick, matted layer of whole leaves can smother grass, hold moisture, and create problems for a lawn.

“The biggest mistake people make is that they just leave the thick layer or piles of leaves right on the lawns where they smother grass, get covered with the snow and create an environment for grass fungal diseases, attract pests, and impact soil health as well.”
—Mirjana Bulatovic-Danilovich, extension specialist with West Virginia University Extension

“My recommendation would be to rake leaves and either mulch them or dispose of them, then set the mower at low for the last mowing of the season.”
—Mirjana Bulatovic-Danilovich, West Virginia University Extension

“Leaves can also become slippery on sidewalks, driveways, and other high-traffic areas, so those areas should be kept clear.”
“So rather than thinking of fallen leaves as waste that needs to be bagged and hauled away, I encourage homeowners to think of them as a resource that can be recycled back into their landscape.”
—Erika Lyon, Agriculture and Natural Resources educator with Ohio State University Extension

When should you rake instead of mulch?

“If you're trying to mow with the mower and you're seeing leaves pile up in front of the mower, almost like you’re pushing leaves, then you have too many. Or if you can't see the grass for the leaves, you have too many.”
—David Richmond, Agriculture and Natural Resources agent with West Virginia University Extension

Where to See Great Fall Colors in West Virginia and Ohio

Seven of the 10 highest-burden metros include West Virginia. For a fall-color visit, extension educators point to state parks and scenic drives in West Virginia and Ohio.

“I would say any of the state parks would be a great spot for color watching. I have the most experience with the newest State Park, the New River Gorge. Dolly Sods, Canaan Valley, and Blackwater Falls come to mind as well.”
—Mirjana Bulatovic-Danilovich, West Virginia University Extension

“My wife and I, the conference we were just at, was in the Canaan Valley/Dolly Sods area, and it was beautiful.”
—David Richmond, West Virginia University Extension

The mountainous eastern part of West Virginia is another favorite area for leaf peeping.

“The leaf change depends on what kind of season we had. When we have a dry year, then a lot of times the foliage isn’t as pretty. It seems like the lower elevations, they change a lot quicker. It seems like when you have more moisture, the leaves are more vibrant and, you know, they'll hang on a little bit longer. And then it's a slow change process.”
—David Richmond, West Virginia University Extension

Ohio has 12 metros in the Leaf Burden Index. Erika Lyon of Ohio State University Extension recommends eastern Ohio for fall color.

“That’s a tough one because there are so many great places to see fall color in Ohio, especially here in the eastern part of the state where we have so much forested land.”
“One of my favorite drives is along State Route 22 between Steubenville and the Piedmont Lake area. You can see some really vibrant reds, oranges, and yellows through that stretch. I also enjoy Harrison State Forest. Cuyahoga Valley and Hocking Hills are also beautiful places to experience Ohio’s fall color.”
—Erika Lyon, Ohio State University Extension

By state

Median index of the metros whose first-listed state is the state, for states with at least 3 metros. States are ordered by that median; this is a summary of metros, not a statewide ranking or measurement.

Median Leaf Burden Index by state
Order	State	Metros	Median index	Highest-ranked metro
1	West Virginia	7	92.3	Charleston, WV (#1)
2	Virginia	9	55.8	Winchester, VA-WV (#6)
3	Connecticut	5	54.9	Norwich-New London-Willimantic, CT (#21)
4	Tennessee	10	51.3	Chattanooga, TN-GA (#9)
5	Arkansas	5	45.8	Fayetteville-Springdale-Rogers, AR (#23)
6	New York	13	43.2	Kingston, NY (#15)
7	Pennsylvania	16	42.2	Altoona, PA (#8)
8	Massachusetts	6	41.9	Pittsfield, MA (#25)
9	North Carolina	15	36.1	Asheville, NC (#13)
10	Maryland	4	35.8	Hagerstown-Martinsburg, MD-WV (#37)
11	Alabama	12	34.4	Gadsden, AL (#27)
12	Kentucky	6	33.3	Louisville/Jefferson County, KY-IN (#69)
13	Georgia	14	32.0	Gainesville, GA (#16)
14	Maine	3	30.5	Portland-South Portland, ME (#96)
15	Missouri	8	29.4	Cape Girardeau, MO-IL (#48)
16	Mississippi	3	28.8	Jackson, MS (#98)
17	Ohio	12	27.0	Akron, OH (#59)
18	Michigan	15	25.3	Traverse City, MI (#45)
19	New Jersey	3	23.1	Vineland, NJ (#159)
20	South Carolina	8	22.4	Greenville-Anderson-Greer, SC (#47)
21	Louisiana	10	22.1	Slidell-Mandeville-Covington, LA (#111)
22	Washington	11	20.1	Bremerton-Silverdale-Port Orchard, WA (#110)
23	Oregon	8	18.7	Grants Pass, OR (#161)
24	Indiana	12	17.3	Bloomington, IN (#50)
25	Minnesota	5	14.4	Duluth, MN-WI (#103)
26	Florida	22	13.2	Pensacola-Ferry Pass-Brent, FL (#138)
27	Wisconsin	13	12.4	La Crosse-Onalaska, WI-MN (#83)
28	Oklahoma	4	12.2	Tulsa, OK (#99)
29	Kansas	4	10.2	Topeka, KS (#226)
30	Montana	5	6.7	Missoula, MT (#232)
31	Utah	5	6.4	Ogden, UT (#152)
32	Illinois	8	6.3	Peoria, IL (#228)
33	Iowa	8	6.2	Dubuque, IA (#215)
34	Colorado	7	3.6	Grand Junction, CO (#217)
35	California	25	3.5	Santa Cruz-Watsonville, CA (#206)
36	Idaho	6	3.3	Coeur d'Alene, ID (#187)
37	Texas	26	3.2	Longview, TX (#160)
38	Nebraska	3	3.1	Omaha, NE-IA (#299)
39	New Mexico	4	0.9	Santa Fe, NM (#330)
40	North Dakota	4	0.7	Grand Forks, ND-MN (#346)
41	Arizona	7	0.6	Prescott Valley-Prescott, AZ (#339)
42	Nevada	3	0.5	Carson City, NV (#317)

Fall is changing

The climate backdrop: a warmer, later fall

A separate LawnStarter analysis of fall climate across the same 383 metros finds a consistent shift between the 1991–2020 baseline and 2016–2025.

+1.35°F

warmer falls in every metro

All 383 metros had a warmer fall (September to November) in 2016–2025 than in 1991–2020. The typical metro ran 1.35°F warmer.

12 vs 9

unusually hot fall days a year

The typical metro now averages about 12 unusually hot fall days per year, compared with about 9 in 1991–2020. The number increased in 382 of 383 metros.

282 of 324

metros saw a later first freeze

Among metros with enough station data, the median first freeze moved later in 282, by a median of 5 days. If the same relationship held, the estimated end of the cleanup season would move roughly 2 to 3 days later in the typical metro (a rough implication, not a measured result).

Sources: NOAA NCEI nClimGrid-Daily and GHCN-Daily (see the source list). Fall is September 1 to November 30. “Recent” is 2016–2025; the baseline is 1991–2020.

Methodology

How we calculated the Fall Leaf Burden Index

The index compares 383 metropolitan statistical areas in the contiguous United States (OMB July 2023 delineation, Census TIGER/Line 2024 boundaries). It covers fall leaf drop from deciduous trees only.

Leaf Supplydeciduous forest cover (2) + total tree canopy (1)

Cleanup Intensitydetached homes (3) + developed land (1) + leaf-drop window (1) + autumn rain (1)

→

Leaf Burden Indexrescaled so the top metro = 100

Each input is converted to a 0-100 score across the 383 metros: score = (value − minimum) ÷ (maximum − minimum) × 100. Within each half, the score is the weighted average of its inputs (weights in parentheses above). The two halves are multiplied and divided by 100, then divided by the highest result so that the top metro scores 100. Ties share a rank.

Why multiply instead of add? An additive (weighted-average) version of the same inputs ranks Houston #187 of 383 even though only 0.2% of its land is deciduous forest, because homes, developed land and rain alone earn it points; in the index it ranks #247. Multiplying means a metro needs both leaves and exposure to rank high.

The six inputs
Input	Weight	What it captures	Source
Deciduous forest cover	2 of 3 in Leaf Supply	Share of the metro's land classed as deciduous forest: areas dominated by trees generally taller than 5 m with more than 20% of vegetation cover, where more than 75% of the tree species shed leaves seasonally (NLCD class 41). Whole 30 m pixels are counted, so this share can exceed average canopy cover.	Annual NLCD
Total tree canopy cover	1 of 3 in Leaf Supply	Average tree canopy cover as a percent of land area (USFS Tree Canopy Cover), a check on overall tree density that includes planted and mixed trees.	USFS Tree Canopy Cover
Detached single-family homes	3 of 6 in Cleanup Intensity	Share of housing units that are 1-unit detached homes, the homes with yards and trees to clear.	ACS 2020-2024 5-year, DP04
Developed land	1 of 6 in Cleanup Intensity	Share of land classed as developed (open space through high intensity), where people live and leaves become a cleanup problem.	Annual NLCD classes 21-24
Leaf-drop window length	1 of 6 in Cleanup Intensity	Days between the start of autumn greenness decline (satellite “senescence”, when greenness last crosses 90% of its seasonal range) and dormancy (15%), for 2024. A longer season means a longer cleanup. Weakest input.	MODIS MCD12Q2
Autumn precipitation	1 of 6 in Cleanup Intensity	September to November precipitation, 1991–2020 normal, averaged across the metro. Wet leaves are heavier and mat down.	NOAA daily gridded climate normals

Estimated peak window (separate from the ranking)

Estimated cleanup-season end = 191.5 + 0.498 × first autumn frost (day of year, 1991–2020 median). The peak window is the 28 days ending at the season end and the peak month is the month of its midpoint. It was calibrated against observed cleanup timing (dates only, no volumes) from LawnStarter service records in 46 fall-peaking metros; the average leave-one-out error is about 6 days for the season end. Confidence tiers: Calibrated (241 metros whose first frost falls inside the range of the 46 metros used to fit the model), Extrapolated (18 metros whose first frost is outside that range) and Not estimated (124 metros: 64 with very little tree canopy over developed land, 35 with mostly evergreen or mixed canopy and late frost, 25 with no typical autumn frost). The peak window does not affect the ranking.

Limitations you should know about

Leaf supply is measured at the regional forest level. It cannot see the trees over yards and streets where leaves become cleanup work. About 87% of Seattle's street trees are deciduous by count (79% by trunk area; genus-based classification of the city's SDOT street tree inventory, trees in service), yet the Seattle metro ranks #166. Treat Western and Pacific Northwest ranks as directional.
Metro boundaries include rural land. Farmland-heavy Midwest metros are diluted on forest share (Chicago ranks #274).
The leaf-drop window is the weakest input: one year (2024) of satellite data, all land pixels, and 38 metros with under 50% valid pixels. It carries weight 1.
Precipitation and frost are metro-wide gridded values. Precipitation comes from NOAA’s daily gridded climate normals and frost from NOAA’s nClimGrid-Daily; both are area-weighted across the whole metro, hills and mountains included, so they can differ from the airport weather station for the same city (the Seattle metro averages 22.6 inches of September-November precipitation, while the Sea-Tac airport station normal is about 12 inches, largely because the metro reaches into the wetter Cascade foothills and mountains). Frost is the first day from August to December with a daily minimum below freezing (32 °F), taken as the median of 1991–2020 and then across the metro’s grid cells; metro-wide medians can sit earlier than the airport station for the same city (cooler rural and upland land) or later (the strict below 32 °F threshold is colder than the 32 °F-or-below convention of NOAA’s freeze normals).
Precipitation is a seasonal total from 1991–2020 normals, not a count of rainy days. We tested a fall 2025 rainy-day count; it covers one year and changed the ranking almost not at all, so it was not used.
Evergreen leaf drop is out of scope. Evergreens such as live oak, which keeps most of its leaves through winter and sheds them as new growth emerges in spring, are outside the index. Texas and Florida metros score low mainly because little of their land is deciduous forest.
We tested a satellite measure of deciduous canopy over developed land and removed it after checks against street trees of known species in Seattle and New York City showed it did not separate deciduous from evergreen trees.
The index is comparative. It is not a count of leaves, bags or dollars.

Ranking stability. Across six weighting variants (equal weights, and dropping autumn precipitation, the leaf-drop window, developed land, total tree canopy, or both timing-and-conditions inputs), 22 to 23 of the top 25 metros and 7 to 10 of the top 10 stay the same. Dropping total tree canopy changes mid-table ranks most (rank correlation 0.79).

Land area shown in our data tables is the Census 2024 Gazetteer figure and feeds no score. Percent inputs were computed against land area derived from the same satellite grids and differ from the Census figure by a median of 0.15%.

FAQ

Frequently asked questions about the Fall Leaf Burden Index

Which U.S. city has the most fall leaves to clean up?

Charleston, WV ranks first on the 2026 Fall Leaf Burden Index, with Beckley, WV second and Huntington-Ashland, WV-KY-OH third. The top five metros all include West Virginia. The index is a relative burden score, not a count of leaves. It scores 383 metropolitan areas on how much deciduous forest surrounds them, how much yard-and-housing exposure they have and how hard the cleanup is.

How is the Fall Leaf Burden Index calculated?

Each metro gets a Leaf Supply score (deciduous forest cover and total tree canopy) and a Cleanup Intensity score (detached single-family homes, developed land, the length of the leaf-drop season and autumn rainfall). The two are multiplied, so a metro needs both leaves and homes to rank high, and the top metro is set to 100. Each input is scaled from 0 to 100 across the 383 metros.

When does leaf cleanup peak?

In 221 of the 241 metros where we could calibrate timing (92%), the estimated peak falls in November, and in 20 warm metros it falls in December (the 124 metros with no fall peak estimate are not counted). The model is only modestly better than assuming November everywhere: it gets the peak month right 89% of the time versus 83%. The estimated season end is about 191.5 plus half a day (0.498) for each day the typical first frost arrives later, and the peak window is the 28 days ending there. Look up any metro in the calendar above.

Why do Texas and Florida rank so low?

The index measures fall leaf drop from deciduous trees. Texas and Florida metros tend to rank lower because little of their land is deciduous forest: the median Texas metro is 0.5% and the median Florida metro is 0.0%, compared with 6.0% across all metros. Evergreens such as live oak—which keeps most of its leaves through winter and sheds them as new growth emerges in spring—are outside the index. Among 26 Texas metros, the median rank is 320; among 22 Florida metros, it is 230. None ranks in the top 100. Spring leaf drop would require a separate index.

Is the ranking reliable for the West and the Pacific Northwest?

Treat Western ranks as directional. Leaf supply is measured from regional forest cover and total tree canopy, which cannot see the planted deciduous trees along streets and in yards. In Seattle, for example, our check of the city's street tree inventory found about 87% of street trees were deciduous by count, yet the Seattle metro ranks #166.

Does the index measure how much leaf cleanup costs or how many leaves fall?

No. It is a comparative index of burden, not a count of leaves, bags or dollars, and it contains no pricing. It compares metros with each other on land cover, housing, climate and timing inputs from public sources.

Why is the leaf-drop window only a small part of the score?

The satellite-measured leaf-drop window is the least reliable input. It uses one year of satellite data (2024), covers all land rather than tree cover alone, and had fewer than 50% valid pixels in 38 metros. It carries a weight of 1, the same as developed land and autumn precipitation, and dropping it changes only 2 of the top 25 metros.

Can I use these statistics?

Yes. Please credit LawnStarter, name the LawnStarter Fall Leaf Burden Index, 2026 edition, and link to this page. All 383 rows are available as a CSV download below, and every statistic on the page has a copy button with the attribution included.

Data and sources

Download the data, cite the study, and check our sources

Graphics for press and social

Free to use with credit to LawnStarter and a link to this page.

Infographic: the 10 most leaf-burdened U.S. metros, led by Charleston, WV at 100, Beckley, WV at 98.0 and Huntington-Ashland, WV-KY-OH at 92.8. Download image

Map of 383 U.S. metros colored by Fall Leaf Burden Index, darkest across Appalachia and the Northeast and lightest in the West. Download image

Chart of how many metros are in their estimated leaf cleanup peak by day, busiest on Nov 24, with 92% of calibrated metros peaking in November. Download image

Nine statistics from the Fall Leaf Burden Index, including 84% deciduous forest in Charleston, WV and 7 of 7 West Virginia metros in the top 50. Download image

Suggested citation
LawnStarter. (2026). Fall Leaf Burden Index, 2026 edition: 383 U.S. metropolitan areas compared for fall leaf cleanup burden. October 28, 2026.

Sources

Annual NLCD 2025 land cover (deciduous forest, class 41; developed classes 21-24). U.S. Geological Survey. Annual NLCD land cover, 2025 (30 m). Class definitions: Multi-Resolution Land Characteristics (MRLC) Consortium, Annual NLCD Land Cover Legend. https://www.mrlc.gov/data/legends/annual-nlcd-land-cover-legend
Tree canopy cover. USDA Forest Service. USFS Tree Canopy Cover v2025-6 (30 m, 1985-2025), via Google Earth Engine. https://developers.google.com/earth-engine/datasets/catalog/projects_gtac-data-publish_assets_TCC_Product_Version_2025-6
Detached single-family homes. U.S. Census Bureau. American Community Survey 2024 5-year estimates, Data Profile DP04: 1-unit detached housing units (DP04_0007E) divided by total housing units (DP04_0001E). https://api.census.gov/data/2024/acs/acs5/profile/variables/DP04_0007E.json
Leaf-drop window. NASA / USGS LP DAAC. MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500 m V061 (MCD12Q2). https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd12q2-061
Autumn precipitation. NOAA NCEI. U.S. Daily Gridded Climate Normals, 1991–2020 (derived from nClimGrid-Daily), averaged across each metro. https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals
First autumn frost. NOAA NCEI. nClimGrid-Daily (1/24-degree gridded daily temperature and precipitation). https://www.ncei.noaa.gov/products/land-based-station/nclimgrid-daily
Metro boundaries. U.S. Census Bureau. TIGER/Line 2024 Core Based Statistical Area boundaries. https://www2.census.gov/geo/tiger/TIGER2024/CBSA/
Metro definitions. U.S. Office of Management and Budget. OMB Bulletin No. 23-01, Revised Delineations of Metropolitan Statistical Areas (July 21, 2023); Census Bureau delineation files, July 2023. https://www.census.gov/geographies/reference-files/time-series/demo/metro-micro/delineation-files.html
Metro population. U.S. Census Bureau. Metropolitan and Micropolitan Statistical Area Population Totals and Components of Change: 2020–2025 (Vintage 2025, population as of July 1, 2025). https://www.census.gov/data/tables/time-series/demo/popest/2020s-total-metro-and-micro-statistical-areas.html
Map outlines. U.S. Census Bureau. Cartographic Boundary Files, 2023 states (1:20,000,000). https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html
Regions. U.S. Census Bureau. TIGER/Line 2024 state file (REGION field); regions are the four Census Bureau regions. https://www2.census.gov/geo/tiger/TIGER2024/STATE/
Seattle street trees (limitation check). City of Seattle / SDOT. SDOT Trees, street tree inventory (Seattle GeoData). https://catalog.data.gov/dataset/sdot-trees
Live oak leaf drop (scope note). Clemson Cooperative Extension, Home & Garden Information Center. Live Oak (HGIC 1014). https://hgic.clemson.edu/factsheet/live-oak/
Fall temperature, hot days, rainfall and freeze trends. NOAA NCEI nClimGrid-Daily and GHCN-Daily station records, 2016–2025 versus 1991–2020 baseline. https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily

Sources were reviewed in October 2026. We checked the published data for completeness and plausible ranges. Data collection took place from September through October 2026.

About the author

Alice Onofrei

Senior Research Manager at LawnStarter

Alice Onofrei is Senior Research Manager at LawnStarter. She leads data collection and analysis for a wide range of statistical studies. She is passionate about making research accessible and relevant, and she prioritizes accuracy and clarity as she turns complex data into meaningful insights.

Editor’s Note: LawnStarter writer Danielle Gorski contributed to this report by interviewing OSU Extension’s Erika Lyon and WVU Extension’s Mirjana Bulatovic-Danilovich and David Richmond.