Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)

Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Ve...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rahman, Md. Shahinoor [verfasserIn] Di, Liping [verfasserIn] Yu, Eugene [verfasserIn] Lin, Li [verfasserIn] Yu, Zhiqi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Crop damage Disaster vegetation damage index (DVDI) Flood inundation Rapid assessment Remote sensing

Übergeordnetes Werk:	Enthalten in: International journal of disaster risk science - Berlin : Springer, 2011, 12(2020), 1 vom: 02. Nov., Seite 90-110
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:1 ; day:02 ; month:11 ; pages:90-110

Links:	Volltext

DOI / URN:	10.1007/s13753-020-00305-7

Katalog-ID:	SPR042859395

Internformat


LEADER	01000caa a22002652 4500
001	SPR042859395
003	DE-627
005	20220111201525.0
007	cr uuu---uuuuu
008	210126s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1007/s13753-020-00305-7 \|2 doi
035			\|a (DE-627)SPR042859395
035			\|a (DE-599)SPRs13753-020-00305-7-e
035			\|a (SPR)s13753-020-00305-7-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 330 \|q ASE
100	1		\|a Rahman, Md. Shahinoor \|e verfasserin \|4 aut
245	1	0	\|a Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)
264		1	\|c 2020
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.
650		4	\|a Crop damage \|7 (dpeaa)DE-He213
650		4	\|a Disaster vegetation damage index (DVDI) \|7 (dpeaa)DE-He213
650		4	\|a Flood inundation \|7 (dpeaa)DE-He213
650		4	\|a Rapid assessment \|7 (dpeaa)DE-He213
650		4	\|a Remote sensing \|7 (dpeaa)DE-He213
700	1		\|a Di, Liping \|e verfasserin \|4 aut
700	1		\|a Yu, Eugene \|e verfasserin \|4 aut
700	1		\|a Lin, Li \|e verfasserin \|4 aut
700	1		\|a Yu, Zhiqi \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t International journal of disaster risk science \|d Berlin : Springer, 2011 \|g 12(2020), 1 vom: 02. Nov., Seite 90-110 \|w (DE-627)670628530 \|w (DE-600)2633158-5 \|x 2192-6395 \|7 nnns
773	1	8	\|g volume:12 \|g year:2020 \|g number:1 \|g day:02 \|g month:11 \|g pages:90-110
856	4	0	\|u https://dx.doi.org/10.1007/s13753-020-00305-7 \|z kostenfrei \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 12 \|j 2020 \|e 1 \|b 02 \|c 11 \|h 90-110

Indexfelder

author_variant	m s r ms msr l d ld e y ey l l ll z y zy
matchkey_str	article:21926395:2020----::eoeesnbsdaiassmnofodrpaaesnnvliatr
hierarchy_sort_str	2020
publishDate	2020
allfields	10.1007/s13753-020-00305-7 doi (DE-627)SPR042859395 (DE-599)SPRs13753-020-00305-7-e (SPR)s13753-020-00305-7-e DE-627 ger DE-627 rakwb eng 330 ASE Rahman, Md. Shahinoor verfasserin aut Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss. Crop damage (dpeaa)DE-He213 Disaster vegetation damage index (DVDI) (dpeaa)DE-He213 Flood inundation (dpeaa)DE-He213 Rapid assessment (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Di, Liping verfasserin aut Yu, Eugene verfasserin aut Lin, Li verfasserin aut Yu, Zhiqi verfasserin aut Enthalten in International journal of disaster risk science Berlin : Springer, 2011 12(2020), 1 vom: 02. Nov., Seite 90-110 (DE-627)670628530 (DE-600)2633158-5 2192-6395 nnns volume:12 year:2020 number:1 day:02 month:11 pages:90-110 https://dx.doi.org/10.1007/s13753-020-00305-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1 02 11 90-110
spelling	10.1007/s13753-020-00305-7 doi (DE-627)SPR042859395 (DE-599)SPRs13753-020-00305-7-e (SPR)s13753-020-00305-7-e DE-627 ger DE-627 rakwb eng 330 ASE Rahman, Md. Shahinoor verfasserin aut Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss. Crop damage (dpeaa)DE-He213 Disaster vegetation damage index (DVDI) (dpeaa)DE-He213 Flood inundation (dpeaa)DE-He213 Rapid assessment (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Di, Liping verfasserin aut Yu, Eugene verfasserin aut Lin, Li verfasserin aut Yu, Zhiqi verfasserin aut Enthalten in International journal of disaster risk science Berlin : Springer, 2011 12(2020), 1 vom: 02. Nov., Seite 90-110 (DE-627)670628530 (DE-600)2633158-5 2192-6395 nnns volume:12 year:2020 number:1 day:02 month:11 pages:90-110 https://dx.doi.org/10.1007/s13753-020-00305-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1 02 11 90-110
allfields_unstemmed	10.1007/s13753-020-00305-7 doi (DE-627)SPR042859395 (DE-599)SPRs13753-020-00305-7-e (SPR)s13753-020-00305-7-e DE-627 ger DE-627 rakwb eng 330 ASE Rahman, Md. Shahinoor verfasserin aut Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss. Crop damage (dpeaa)DE-He213 Disaster vegetation damage index (DVDI) (dpeaa)DE-He213 Flood inundation (dpeaa)DE-He213 Rapid assessment (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Di, Liping verfasserin aut Yu, Eugene verfasserin aut Lin, Li verfasserin aut Yu, Zhiqi verfasserin aut Enthalten in International journal of disaster risk science Berlin : Springer, 2011 12(2020), 1 vom: 02. Nov., Seite 90-110 (DE-627)670628530 (DE-600)2633158-5 2192-6395 nnns volume:12 year:2020 number:1 day:02 month:11 pages:90-110 https://dx.doi.org/10.1007/s13753-020-00305-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1 02 11 90-110
allfieldsGer	10.1007/s13753-020-00305-7 doi (DE-627)SPR042859395 (DE-599)SPRs13753-020-00305-7-e (SPR)s13753-020-00305-7-e DE-627 ger DE-627 rakwb eng 330 ASE Rahman, Md. Shahinoor verfasserin aut Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss. Crop damage (dpeaa)DE-He213 Disaster vegetation damage index (DVDI) (dpeaa)DE-He213 Flood inundation (dpeaa)DE-He213 Rapid assessment (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Di, Liping verfasserin aut Yu, Eugene verfasserin aut Lin, Li verfasserin aut Yu, Zhiqi verfasserin aut Enthalten in International journal of disaster risk science Berlin : Springer, 2011 12(2020), 1 vom: 02. Nov., Seite 90-110 (DE-627)670628530 (DE-600)2633158-5 2192-6395 nnns volume:12 year:2020 number:1 day:02 month:11 pages:90-110 https://dx.doi.org/10.1007/s13753-020-00305-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1 02 11 90-110
allfieldsSound	10.1007/s13753-020-00305-7 doi (DE-627)SPR042859395 (DE-599)SPRs13753-020-00305-7-e (SPR)s13753-020-00305-7-e DE-627 ger DE-627 rakwb eng 330 ASE Rahman, Md. Shahinoor verfasserin aut Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss. Crop damage (dpeaa)DE-He213 Disaster vegetation damage index (DVDI) (dpeaa)DE-He213 Flood inundation (dpeaa)DE-He213 Rapid assessment (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Di, Liping verfasserin aut Yu, Eugene verfasserin aut Lin, Li verfasserin aut Yu, Zhiqi verfasserin aut Enthalten in International journal of disaster risk science Berlin : Springer, 2011 12(2020), 1 vom: 02. Nov., Seite 90-110 (DE-627)670628530 (DE-600)2633158-5 2192-6395 nnns volume:12 year:2020 number:1 day:02 month:11 pages:90-110 https://dx.doi.org/10.1007/s13753-020-00305-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1 02 11 90-110
language	English
source	Enthalten in International journal of disaster risk science 12(2020), 1 vom: 02. Nov., Seite 90-110 volume:12 year:2020 number:1 day:02 month:11 pages:90-110
sourceStr	Enthalten in International journal of disaster risk science 12(2020), 1 vom: 02. Nov., Seite 90-110 volume:12 year:2020 number:1 day:02 month:11 pages:90-110
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Crop damage Disaster vegetation damage index (DVDI) Flood inundation Rapid assessment Remote sensing
dewey-raw	330
isfreeaccess_bool	true
container_title	International journal of disaster risk science
authorswithroles_txt_mv	Rahman, Md. Shahinoor @@aut@@ Di, Liping @@aut@@ Yu, Eugene @@aut@@ Lin, Li @@aut@@ Yu, Zhiqi @@aut@@
publishDateDaySort_date	2020-11-02T00:00:00Z
hierarchy_top_id	670628530
dewey-sort	3330
id	SPR042859395
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR042859395</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111201525.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210126s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13753-020-00305-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR042859395</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs13753-020-00305-7-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13753-020-00305-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rahman, Md. Shahinoor</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Crop damage</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Disaster vegetation damage index (DVDI)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Flood inundation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rapid assessment</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remote sensing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Di, Liping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Eugene</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Zhiqi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of disaster risk science</subfield><subfield code="d">Berlin : Springer, 2011</subfield><subfield code="g">12(2020), 1 vom: 02. Nov., Seite 90-110</subfield><subfield code="w">(DE-627)670628530</subfield><subfield code="w">(DE-600)2633158-5</subfield><subfield code="x">2192-6395</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:12</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:02</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:90-110</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s13753-020-00305-7</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">12</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">02</subfield><subfield code="c">11</subfield><subfield code="h">90-110</subfield></datafield></record></collection>
author	Rahman, Md. Shahinoor
spellingShingle	Rahman, Md. Shahinoor ddc 330 misc Crop damage misc Disaster vegetation damage index (DVDI) misc Flood inundation misc Rapid assessment misc Remote sensing Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)
authorStr	Rahman, Md. Shahinoor
ppnlink_with_tag_str_mv	@@773@@(DE-627)670628530
format	electronic Article
dewey-ones	330 - Economics
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	2192-6395
topic_title	330 ASE Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI) Crop damage (dpeaa)DE-He213 Disaster vegetation damage index (DVDI) (dpeaa)DE-He213 Flood inundation (dpeaa)DE-He213 Rapid assessment (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213
topic	ddc 330 misc Crop damage misc Disaster vegetation damage index (DVDI) misc Flood inundation misc Rapid assessment misc Remote sensing
topic_unstemmed	ddc 330 misc Crop damage misc Disaster vegetation damage index (DVDI) misc Flood inundation misc Rapid assessment misc Remote sensing
topic_browse	ddc 330 misc Crop damage misc Disaster vegetation damage index (DVDI) misc Flood inundation misc Rapid assessment misc Remote sensing
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	International journal of disaster risk science
hierarchy_parent_id	670628530
dewey-tens	330 - Economics
hierarchy_top_title	International journal of disaster risk science
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)670628530 (DE-600)2633158-5
title	Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)
ctrlnum	(DE-627)SPR042859395 (DE-599)SPRs13753-020-00305-7-e (SPR)s13753-020-00305-7-e
title_full	Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)
author_sort	Rahman, Md. Shahinoor
journal	International journal of disaster risk science
journalStr	International journal of disaster risk science
lang_code	eng
isOA_bool	true
dewey-hundreds	300 - Social sciences
recordtype	marc
publishDateSort	2020
contenttype_str_mv	txt
container_start_page	90
author_browse	Rahman, Md. Shahinoor Di, Liping Yu, Eugene Lin, Li Yu, Zhiqi
container_volume	12
class	330 ASE
format_se	Elektronische Aufsätze
author-letter	Rahman, Md. Shahinoor
doi_str_mv	10.1007/s13753-020-00305-7
dewey-full	330
author2-role	verfasserin
title_sort	remote sensing based rapid assessment of flood crop damage using novel disaster vegetation damage index (dvdi)
title_auth	Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)
abstract	Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.
abstractGer	Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.
abstract_unstemmed	Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)
url	https://dx.doi.org/10.1007/s13753-020-00305-7
remote_bool	true
author2	Di, Liping Yu, Eugene Lin, Li Yu, Zhiqi
author2Str	Di, Liping Yu, Eugene Lin, Li Yu, Zhiqi
ppnlink	670628530
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1007/s13753-020-00305-7
up_date	2024-07-03T15:13:39.366Z
_version_	1803571292397371392
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR042859395</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111201525.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210126s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13753-020-00305-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR042859395</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs13753-020-00305-7-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13753-020-00305-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rahman, Md. Shahinoor</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An $ R^{2} $ value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Crop damage</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Disaster vegetation damage index (DVDI)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Flood inundation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rapid assessment</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remote sensing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Di, Liping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Eugene</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Zhiqi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of disaster risk science</subfield><subfield code="d">Berlin : Springer, 2011</subfield><subfield code="g">12(2020), 1 vom: 02. Nov., Seite 90-110</subfield><subfield code="w">(DE-627)670628530</subfield><subfield code="w">(DE-600)2633158-5</subfield><subfield code="x">2192-6395</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:12</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:02</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:90-110</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s13753-020-00305-7</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">12</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">02</subfield><subfield code="c">11</subfield><subfield code="h">90-110</subfield></datafield></record></collection>
score	7.40026

Nicht das Richtige dabei?

Schreiben Sie uns!

Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?