20260505
08484000
1.0
FALSE
gisdb.iat.prodes
002
Server=10.71.10.202; Service=sde:postgresql:10.71.10.202; Database=gisdb; User=iat; Version=sde.DEFAULT
ArcSDE Connection
Geographic
GCS_SIRGAS_2000
Angular Unit: Degree (0.017453)
<GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.4.0'><WKT>GEOGCS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4674]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>999999999.99999988</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>8.9831528411952133e-09</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4674</WKID><LatestWKID>4674</LatestWKID></GeographicCoordinateSystem>
ExportFeatures yearly_deforestation_biome_m "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" # NOT_USE_ALIAS "uuid "uuid" true true false 80 Text 0 0,First,#,S:\NGI-GCGE\DAP\Temp\Gabriel\PRODES\Brutos\Brutos_20260330\10_30_am\yearly_deforestation_biome_mata_atlantica_v20260330\yearly_deforestation_biome_mata_atlantica_v20260330.shp,uuid,0,79;main_class "main_class" true true false 32 Text 0 0,First,#,S:\NGI-GCGE\DAP\Temp\Gabriel\PRODES\Brutos\Brutos_20260330\10_30_am\yearly_deforestation_biome_mata_atlantica_v20260330\yearly_deforestation_biome_mata_atlantica_v20260330.shp,main_class,0,31;image_date "image_date" true true false 8 Date 0 0,First,#,S:\NGI-GCGE\DAP\Temp\Gabriel\PRODES\Brutos\Brutos_20260330\10_30_am\yearly_deforestation_biome_mata_atlantica_v20260330\yearly_deforestation_biome_mata_atlantica_v20260330.shp,image_date,-1,-1;year "year" true true false 9 Long 0 0,First,#,S:\NGI-GCGE\DAP\Temp\Gabriel\PRODES\Brutos\Brutos_20260330\10_30_am\yearly_deforestation_biome_mata_atlantica_v20260330\yearly_deforestation_biome_mata_atlantica_v20260330.shp,year,-1,-1;satellite "satellite" true true false 32 Text 0 0,First,#,S:\NGI-GCGE\DAP\Temp\Gabriel\PRODES\Brutos\Brutos_20260330\10_30_am\yearly_deforestation_biome_mata_atlantica_v20260330\yearly_deforestation_biome_mata_atlantica_v20260330.shp,satellite,0,31;sensor "sensor" true true false 32 Text 0 0,First,#,S:\NGI-GCGE\DAP\Temp\Gabriel\PRODES\Brutos\Brutos_20260330\10_30_am\yearly_deforestation_biome_mata_atlantica_v20260330\yearly_deforestation_biome_mata_atlantica_v20260330.shp,sensor,0,31" #
AlterFields "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" "uuid # 'Código PRODES' TEXT 255 # #;main_class # Classe TEXT 255 # #;image_date # 'Data da imagem' DATE # # #;year # 'Ano PRODES' LONG # # #;satellite # Satélite TEXT 255 # #;sensor # Sensor TEXT 255 # #"
AddFields "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" # S:\NGI-GCGE\DAP\Temp\Gabriel\PRODES\PRODES.gdb\Campos_padrao_PRODES
Append 'C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\yearly_deforestation_CE_2' "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" NO_TEST "uuid "uuid" true true false 80 Text 0 0,First,#,C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\.\IAT NGI 2026.gdb\yearly_deforestation_CE_2,uuid,0,79;main_class "main_class" true true false 254 Text 0 0,First,#,C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\.\IAT NGI 2026.gdb\yearly_deforestation_CE_2,main_class,0,253;image_date "image_date" true true false 8 Date 0 0,First,#,C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\.\IAT NGI 2026.gdb\yearly_deforestation_CE_2,image_date,-1,-1;year "year" true true false 9 Long 0 0,First,#,C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\.\IAT NGI 2026.gdb\yearly_deforestation_CE_2,year,-1,-1;satellite "satellite" true true false 50 Text 0 0,First,#,C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\.\IAT NGI 2026.gdb\yearly_deforestation_CE_2,satellite,0,49;sensor "sensor" true true false 50 Text 0 0,First,#,C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\.\IAT NGI 2026.gdb\yearly_deforestation_CE_2,sensor,0,49;area_ha "Área (ha)" true true false 8 Double 0 0,First,#;validacao "Validação" true true false 4 Long 0 0,First,#;validacao_motivo "Motivo da validação" true true false 4 Long 0 0,First,#;atendimento "Atenidmento" true true false 4 Long 0 0,First,#;rev "Revisor" true true false 4 Long 0 0,First,#;aia "AIA(s)" true true false 4 Long 0 0,First,#;eprotocolo "E-protocolo" true true false 255 Text 0 0,First,#;obs "Observação" true true false 255 Text 0 0,First,#;regional "Escritório Regional" true true false 255 Text 0 0,First,#;municipio "Município" true true false 255 Text 0 0,First,#;uc "Unidade de Conservação" true true false 255 Text 0 0,First,#;fitogeo "Região fitogeográfica" true true false 255 Text 0 0,First,#;car "Imóvel CAR" true true false 255 Text 0 0,First,#;area_imovel "Área do imóvel (ha)" true true false 8 Double 0 0,First,#;mod_fiscal "Módulos fiscais " true true false 8 Double 0 0,First,#;itsc "Intersecção imóvel (ha)" true true false 8 Double 0 0,First,#;porc_itsc "Intersecção imóvel (%)" true true false 8 Double 0 0,First,#;num_car "Quantidade de imóveis" true true false 4 Long 0 0,First,#;publicacao "Data de publicação" true true false 8 Date 0 0,First,#;indicador_numerico "Indicador de validez" true true false 4 Long 0 0,First,#;Uso_principal_2008 "Uso principal 2008" true true false 255 Text 0 0,First,#;Veg_florestal_2008 "Veg. florestal 2008" true true false 255 Text 0 0,First,#;Veg_herb_arb_2008 "Veg. herb. arb. 2008" true true false 255 Text 0 0,First,#;Uso_principal_2018 "Uso principal 2018" true true false 255 Text 0 0,First,#;Veg_florestal_2018 "Veg. florestal 2018" true true false 255 Text 0 0,First,#;Veg_herb_arb_2018 "Veg. herb. arb. 2018" true true false 255 Text 0 0,First,#" # # # NOT_UPDATE_GEOMETRY NO_ENFORCE_DOMAINS USE_FEATURE_SERVICE_MODE
CalculateFields "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" PYTHON3 "publicacao '"01/04/2026"' # #;regional regional(!uuid!) # #;municipio municipio(!uuid!) # #;uc uc(!uuid!) # #;fitogeo fito(!uuid!) # #;car car(!uuid!) # #;area_imovel area_imovel(!uuid!) # #;num_car num_car(!uuid!) # #;itsc itsc(!uuid!) # #;porc_itsc porc_itsc(!uuid!) # #;mod_fiscal mod_fiscal(!uuid!) # #;indicador_numerico indicador_numerico(!uuid!) # #;Uso_principal_2008 get_2008_uso_principal(!uuid!) # #;Veg_florestal_2008 get_2008_veg_florestal(!uuid!) # #;Veg_herb_arb_2008 get_2008_veg_herb_arb(!uuid!) # #;Uso_principal_2018 get_2018_uso_principal(!uuid!) # #;Veg_florestal_2018 get_2018_veg_florestal(!uuid!) # #;Veg_herb_arb_2018 get_2018_veg_herb_arb(!uuid!) # #" "import arcpy
from collections import defaultdict
# =============================================================================
# CONFIGURAÇÃO DO WORKSPACE
# =============================================================================
workspace_gdb = r"C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb"
if not arcpy.Exists(workspace_gdb):
raise Exception(f"Geodatabase não encontrada: {workspace_gdb}")
arcpy.env.workspace = workspace_gdb
# =============================================================================
# SCRIPT 1 – Dados regionais, município, fitogeografia, UC e CAR
# =============================================================================
tabs = {
'regional': ('Alertas_PRODES_TabulateInter3', 'REGIONAL'),
'municipio': ('Alertas_PRODES_TabulateInter5', 'nome'),
'fito': ('Alertas_PRODES_TabulateInter6', 'FITO'),
'uc_nome_uc': ('Alertas_PRODES_TabulateInter7', 'NOME_UC'),
'uc_nome_uc_lower': ('Alertas_PRODES_TabulateInter8', 'nome_uc')
}
dados_tabs = {chave: {} for chave in tabs}
for func_nome, (tabela, col_alvo) in tabs.items():
if not arcpy.Exists(tabela):
print(f"Aviso: tabela {tabela} não encontrada. Ignorada.")
continue
with arcpy.da.SearchCursor(tabela, ['uuid', col_alvo, 'AREA']) as cursor:
for uuid_val, valor, area in cursor:
if uuid_val not in dados_tabs[func_nome]:
dados_tabs[func_nome][uuid_val] = (area, valor)
else:
if area > dados_tabs[func_nome][uuid_val][0]:
dados_tabs[func_nome][uuid_val] = (area, valor)
# Contagens de UUID nas tabelas 9 a 14
tabelas_contagem = [
"Alertas_PRODES_TabulateInter9",
"Alertas_PRODES_TabulateInter10",
"Alertas_PRODES_TabulateInter11",
"Alertas_PRODES_TabulateInter12",
"Alertas_PRODES_TabulateInter13",
"Alertas_PRODES_TabulateInter14"
]
contagem_por_uuid = {}
for tabela in tabelas_contagem:
if not arcpy.Exists(tabela):
print(f"Aviso: tabela {tabela} não encontrada. Ignorada.")
continue
with arcpy.da.SearchCursor(tabela, ["uuid"]) as cursor:
for linha in cursor:
uuid_val = linha[0]
contagem_por_uuid[uuid_val] = contagem_por_uuid.get(uuid_val, 0) + 1
# Dados do CAR (TabulateInter4)
tabela_car = 'Alertas_PRODES_TabulateInter4'
dados_car = {} # uuid -> (area_maior, cod_imovel, num_area, area_maior, perc, mod_fiscal)
contagem_car = {}
if arcpy.Exists(tabela_car):
with arcpy.da.SearchCursor(tabela_car,
['uuid', 'cod_imovel', 'num_area', 'AREA', 'PERCENTAGE', 'mod_fiscal']) as cursor:
for uuid_val, cod_imovel, num_area, area, perc, mod_fiscal in cursor:
contagem_car[uuid_val] = contagem_car.get(uuid_val, 0) + 1
if uuid_val not in dados_car:
dados_car[uuid_val] = (area, cod_imovel, num_area, area, perc, mod_fiscal)
else:
if area > dados_car[uuid_val][0]:
dados_car[uuid_val] = (area, cod_imovel, num_area, area, perc, mod_fiscal)
else:
print(f"Aviso: tabela {tabela_car} não encontrada. Funções relacionadas ao CAR retornarão None/0.")
# --- Funções do Script 1 ---
def regional(uuid_val):
res = dados_tabs['regional'].get(uuid_val)
return res[1] if res else None
def municipio(uuid_val):
res = dados_tabs['municipio'].get(uuid_val)
return res[1] if res else None
def fito(uuid_val):
res = dados_tabs['fito'].get(uuid_val)
return res[1] if res else None
def uc(uuid_val):
val_nome_uc = dados_tabs['uc_nome_uc'].get(uuid_val)
val_nome_uc_lower = dados_tabs['uc_nome_uc_lower'].get(uuid_val)
valor1 = val_nome_uc[1] if val_nome_uc else None
valor2 = val_nome_uc_lower[1] if val_nome_uc_lower else None
tem_valor1 = valor1 not in (None, '')
tem_valor2 = valor2 not in (None, '')
if tem_valor1 and tem_valor2:
return valor1
elif tem_valor1:
return valor1
elif tem_valor2:
return valor2
else:
return None
def indicador_numerico(uuid_val):
return contagem_por_uuid.get(uuid_val, 0)
def car(uuid_val):
reg = dados_car.get(uuid_val)
return reg[1] if reg else None
def area_imovel(uuid_val):
reg = dados_car.get(uuid_val)
return reg[2] if reg else None
def num_car(uuid_val):
return contagem_car.get(uuid_val, 0)
def itsc(uuid_val):
reg = dados_car.get(uuid_val)
return reg[3] if reg else None
def porc_itsc(uuid_val):
reg = dados_car.get(uuid_val)
return reg[4] if reg else None
def mod_fiscal(uuid_val):
reg = dados_car.get(uuid_val)
return reg[5] if reg else None
# =============================================================================
# FUNÇÕES AUXILIARES COMUNS (para os percentuais de uso 2008/2018)
# =============================================================================
def _carregar_tabela(tabela):
"""
Carrega os percentuais de cada VALUE por UUID.
Retorna dicionário: uuid -> lista de percentuais na ordem:
[VALUE_1, VALUE_9, VALUE_10, VALUE_14, VALUE_21, VALUE_22, VALUE_26]
"""
percentuais = {}
campos = ["UUID", "VALUE_1", "VALUE_9", "VALUE_10",
"VALUE_14", "VALUE_21", "VALUE_22", "VALUE_26"]
with arcpy.da.SearchCursor(tabela, campos) as cursor:
for row in cursor:
uid = row[0]
valores = row[1:]
total = sum(valores)
if total != 0:
pcts = [(v / total) * 100.0 for v in valores]
else:
pcts = [0.0] * len(valores)
percentuais[uid] = pcts
return percentuais
def _formatar_pct(valor):
"""Formata o percentual: 0 e 100 sem decimais; demais com vírgula e duas casas."""
arredondado = round(valor, 2)
if arredondado == 100.0:
return "100 %"
elif arredondado == 0.0:
return "0 %"
else:
texto = f"{arredondado:.2f}".replace('.', ',')
return f"{texto} %"
# =============================================================================
# CACHES SEPARADOS PARA 2008 E 2018 (evita conflitos)
# =============================================================================
_cache_2008 = {}
_cache_2018 = {}
# =============================================================================
# FUNÇÕES DO SCRIPT 2 – USO 2008
# =============================================================================
def get_2008_uso_principal(uuid):
"""
Retorna o nome e percentual do maior uso em 2008 no formato: 'Nome (xx,xx %)'.
"""
global _cache_2008
if not _cache_2008:
tabela = r"C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Tabulate_Area_2008"
_cache_2008 = _carregar_tabela(tabela)
pcts = _cache_2008.get(uuid)
if pcts is None:
return None
nomes = [
"Veg_Florestal", # VALUE_1
"Silvicultura", # VALUE_9
"Veg_herb_arb", # VALUE_10
"Agropecuaria", # VALUE_14
"Mosaico", # VALUE_21
"Nao_veg", # VALUE_22
"Agua" # VALUE_26
]
idx_max = max(range(len(pcts)), key=lambda i: pcts[i])
pct_max = pcts[idx_max]
nome = nomes[idx_max]
return f"{nome} ({_formatar_pct(pct_max)})"
def get_2008_veg_florestal(uuid):
"""Percentual de Veg_florestal (VALUE_1) em 2008."""
global _cache_2008
if not _cache_2008:
tabela = r"C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Tabulate_Area_2008"
_cache_2008 = _carregar_tabela(tabela)
pcts = _cache_2008.get(uuid)
if pcts is None:
return None
return _formatar_pct(pcts[0]) # índice 0 = VALUE_1
def get_2008_veg_herb_arb(uuid):
"""Percentual de Veg_herb_arb (VALUE_10) em 2008."""
global _cache_2008
if not _cache_2008:
tabela = r"C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Tabulate_Area_2008"
_cache_2008 = _carregar_tabela(tabela)
pcts = _cache_2008.get(uuid)
if pcts is None:
return None
return _formatar_pct(pcts[2]) # índice 2 = VALUE_10
# =============================================================================
# FUNÇÕES DO SCRIPT 3 – USO 2018
# =============================================================================
def get_2018_uso_principal(uuid):
"""
Retorna o nome e percentual do maior uso em 2018 no formato: 'Nome (xx,xx %)'.
"""
global _cache_2018
if not _cache_2018:
tabela = r"C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Tabulate_Area_2018"
_cache_2018 = _carregar_tabela(tabela)
pcts = _cache_2018.get(uuid)
if pcts is None:
return None
nomes = [
"Veg_Florestal", # VALUE_1
"Silvicultura", # VALUE_9
"Veg_herb_arb", # VALUE_10
"Agropecuaria", # VALUE_14
"Mosaico", # VALUE_21
"Nao_veg", # VALUE_22
"Agua" # VALUE_26
]
idx_max = max(range(len(pcts)), key=lambda i: pcts[i])
pct_max = pcts[idx_max]
nome = nomes[idx_max]
return f"{nome} ({_formatar_pct(pct_max)})"
def get_2018_veg_florestal(uuid):
"""Percentual de Veg_florestal (VALUE_1) em 2018."""
global _cache_2018
if not _cache_2018:
tabela = r"C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Tabulate_Area_2018"
_cache_2018 = _carregar_tabela(tabela)
pcts = _cache_2018.get(uuid)
if pcts is None:
return None
return _formatar_pct(pcts[0])
def get_2018_veg_herb_arb(uuid):
"""Percentual de Veg_herb_arb (VALUE_10) em 2018."""
global _cache_2018
if not _cache_2018:
tabela = r"C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Tabulate_Area_2018"
_cache_2018 = _carregar_tabela(tabela)
pcts = _cache_2018.get(uuid)
if pcts is None:
return None
return _formatar_pct(pcts[2])" NO_ENFORCE_DOMAINS
CalculateGeometryAttributes "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" "area_ha AREA" # HECTARES PROJCRS["SIRGAS_2000_UTM_Zone_22S",BASEGEOGCRS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",ELLIPSOID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],CS[ellipsoidal,2],AXIS["Latitude (lat)",north,ORDER[1]],AXIS["Longitude (lon)",east,ORDER[2]],ANGLEUNIT["Degree",0.0174532925199433]],CONVERSION["Transverse_Mercator",METHOD["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",-51.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0]],CS[Cartesian,2],AXIS["Easting (X)",east,ORDER[1]],AXIS["Northing (Y)",north,ORDER[2]],LENGTHUNIT["Meter",1.0],ID["EPSG","31982"]] SAME_AS_INPUT
SetSubtypeField "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" validacao DO_NOT_CLEAR
AddSubtype "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" 1 VÁLIDO
AddSubtype "C:\Users\bol.gabrielsanti\Documents\ArcGIS\Projects\IAT NGI 2026\IAT NGI 2026.gdb\Alertas_PRODES_4_2" 2 INVÁLIDO
ExportFeatures "ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2" S:\NGI-GCGE\DAP\Temp\Gabriel\PRODES\PRODES.gdb\Alertas_PRODES # NOT_USE_ALIAS "uuid "Código PRODES" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,uuid,0,254;main_class "Classe" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,main_class,0,254;image_date "Data da imagem" true true false 8 Date 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,image_date,-1,-1;year "Ano PRODES" true true false 4 Long 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,year,-1,-1;area_ha "Área (ha)" true true false 8 Double 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,area_ha,-1,-1;validacao "Validação" true true false 4 Long 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,validacao,-1,-1;validacao_motivo "Motivo da validação" true true false 4 Long 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,validacao_motivo,-1,-1;atendimento "Atenidmento" true true false 4 Long 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,atendimento,-1,-1;rev "Revisor" true true false 4 Long 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,rev,-1,-1;aia "AIA(s)" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,aia,0,254;eprotocolo "E-protocolo" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,eprotocolo,0,254;obs "Observação" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,obs,0,254;regional "Escritório Regional" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,regional,0,254;municipio "Município" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,municipio,0,254;uc "Unidade de Conservação" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,uc,0,254;fitogeo "Região fitogeográfica" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,fitogeo,0,254;car "Imóvel CAR" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,car,0,254;area_imovel "Área do imóvel (ha)" true true false 8 Double 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,area_imovel,-1,-1;mod_fiscal "Módulos fiscais " true true false 8 Double 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,mod_fiscal,-1,-1;itsc "Intersecção imóvel (ha)" true true false 8 Double 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,itsc,-1,-1;porc_itsc "Intersecção imóvel (%)" true true false 8 Double 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,porc_itsc,-1,-1;num_car "Quantidade de imóveis" true true false 4 Long 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,num_car,-1,-1;publicacao "Data de publicação" true true false 8 Date 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,publicacao,-1,-1;satellite "Satélite" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,satellite,0,254;sensor "sensor" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,sensor,0,254;indicador_numerico "Indicador de validez" true true false 4 Long 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,indicador_numerico,-1,-1;Uso_principal_2008 "Uso principal 2008" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,Uso_principal_2008,0,254;Veg_florestal_2008 "Veg. florestal 2008" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,Veg_florestal_2008,0,254;Veg_herb_arb_2008 "Veg. herb. arb. 2008" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,Veg_herb_arb_2008,0,254;Uso_principal_2018 "Uso principal 2018" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,Uso_principal_2018,0,254;Veg_florestal_2018 "Veg. florestal 2018" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,Veg_florestal_2018,0,254;Veg_herb_arb_2018 "Veg. herb. arb. 2018" true true false 255 Text 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,Veg_herb_arb_2018,0,254;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,ModelBuilder\yearly_deforestation_ExportFeatures2_ExportFeatures (10):Alertas_PRODES_4_2,Shape_Area,-1,-1" #
ExportFeatures Alertas_PRODES C:\Users\deborasusan\AppData\Roaming\Esri\ArcGISPro\Favorites\PostgreSQL-10-gisdb(iat).sde\gisdb.iat.D55_GAT_Fiscalizacao\gisdb.iat.prodes # NOT_USE_ALIAS "uuid "Código PRODES" true true false 255 Text 0 0,First,#,Alertas_PRODES,uuid,0,254;main_class "Classe" true true false 255 Text 0 0,First,#,Alertas_PRODES,main_class,0,254;image_date "Data da imagem" true true false 8 Date 0 0,First,#,Alertas_PRODES,image_date,-1,-1;year "Ano PRODES" true true false 4 Long 0 0,First,#,Alertas_PRODES,year,-1,-1;area_ha "Área (ha)" true true false 8 Double 0 0,First,#,Alertas_PRODES,area_ha,-1,-1;validacao "Validação" true true false 4 Long 0 0,First,#,Alertas_PRODES,validacao,-1,-1;validacao_motivo "Motivo da validação" true true false 4 Long 0 0,First,#,Alertas_PRODES,validacao_motivo,-1,-1;atendimento "Atenidmento" true true false 4 Long 0 0,First,#,Alertas_PRODES,atendimento,-1,-1;rev "Revisor" true true false 4 Long 0 0,First,#,Alertas_PRODES,rev,-1,-1;aia "AIA(s)" true true false 255 Text 0 0,First,#,Alertas_PRODES,aia,0,254;eprotocolo "E-protocolo" true true false 255 Text 0 0,First,#,Alertas_PRODES,eprotocolo,0,254;obs "Observação" true true false 255 Text 0 0,First,#,Alertas_PRODES,obs,0,254;regional "Escritório Regional" true true false 255 Text 0 0,First,#,Alertas_PRODES,regional,0,254;municipio "Município" true true false 255 Text 0 0,First,#,Alertas_PRODES,municipio,0,254;uc "Unidade de Conservação" true true false 255 Text 0 0,First,#,Alertas_PRODES,uc,0,254;fitogeo "Região fitogeográfica" true true false 255 Text 0 0,First,#,Alertas_PRODES,fitogeo,0,254;car "Imóvel CAR" true true false 255 Text 0 0,First,#,Alertas_PRODES,car,0,254;area_imovel "Área do imóvel (ha)" true true false 8 Double 0 0,First,#,Alertas_PRODES,area_imovel,-1,-1;mod_fiscal "Módulos fiscais " true true false 8 Double 0 0,First,#,Alertas_PRODES,mod_fiscal,-1,-1;itsc "Intersecção imóvel (ha)" true true false 8 Double 0 0,First,#,Alertas_PRODES,itsc,-1,-1;porc_itsc "Intersecção imóvel (%)" true true false 8 Double 0 0,First,#,Alertas_PRODES,porc_itsc,-1,-1;num_car "Quantidade de imóveis" true true false 4 Long 0 0,First,#,Alertas_PRODES,num_car,-1,-1;publicacao "Data de publicação" true true false 8 Date 0 0,First,#,Alertas_PRODES,publicacao,-1,-1;satellite "Satélite" true true false 255 Text 0 0,First,#,Alertas_PRODES,satellite,0,254;sensor "sensor" true true false 255 Text 0 0,First,#,Alertas_PRODES,sensor,0,254;indicador_numerico "Indicador de validez" true true false 4 Long 0 0,First,#,Alertas_PRODES,indicador_numerico,-1,-1;Uso_principal_2008 "Uso principal 2008" true true false 255 Text 0 0,First,#,Alertas_PRODES,Uso_principal_2008,0,254;Veg_florestal_2008 "Veg. florestal 2008" true true false 255 Text 0 0,First,#,Alertas_PRODES,Veg_florestal_2008,0,254;Veg_herb_arb_2008 "Veg. herb. arb. 2008" true true false 255 Text 0 0,First,#,Alertas_PRODES,Veg_herb_arb_2008,0,254;Uso_principal_2018 "Uso principal 2018" true true false 255 Text 0 0,First,#,Alertas_PRODES,Uso_principal_2018,0,254;Veg_florestal_2018 "Veg. florestal 2018" true true false 255 Text 0 0,First,#,Alertas_PRODES,Veg_florestal_2018,0,254;Veg_herb_arb_2018 "Veg. herb. arb. 2018" true true false 255 Text 0 0,First,#,Alertas_PRODES,Veg_herb_arb_2018,0,254;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,Alertas_PRODES,Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,Alertas_PRODES,Shape_Area,-1,-1" #
20260505
12023100
20260505
12023100
Microsoft Windows 10 Version 10.0 (Build 22631) ; Esri ArcGIS 13.4.0.55405
Alertas PRODES (Gestão NGI)
<p>Esta camada corresponde à base de gestão dos alertas de desmatamento do Projeto PRODES (Programa de Monitoramento do Desmatamento por Satélite) para o Estado do Paraná, administrada pelo Instituto Água e Terra (IAT), por meio do <span>Núcleo de Inteligência Geográfica e da Informação (NGI).</span></p>
<p>Os dados espaciais são obtidos a partir das bases oficiais do INPE, disponibilizadas como dados abertos na plataforma dados.gov.br, e posteriormente integrados ao ambiente institucional do IAT, onde passam a ser utilizados para fins de controle, acompanhamento e atualização da situação dos alertas no âmbito da gestão ambiental estadual.</p>
<p>As feições representam áreas com detecção de supressão de vegetação nativa identificadas por sensoriamento remoto, conforme a metodologia oficial do PRODES. No contexto desta camada, os alertas são associados a informações de gestão interna, como análise técnica, encaminhamentos, sobreposição com autorizações, autos de infração ou outras providências administrativas, conforme os fluxos do IAT.</p>
<p>Ressalta-se que a detecção do desmatamento pelo PRODES não configura, por si só, infração ambiental. A caracterização de irregularidade depende da análise administrativa conduzida pelo IAT, da verificação da existência de autorizações válidas e, quando aplicável, de ações de fiscalização e instrução processual.</p>
Alertas_PRODES_público
MapServer
Camada vetorial de gestão dos alertas de desmatamento do PRODES para o Estado do Paraná, mantida pelo Instituto Água e Terra (IAT), por meio do Núcleo de Inteligência Geográfica e da Informação (NGI). Os dados têm como base os alertas produzidos pelo INPE e disponibilizados via dados.gov.br, sendo incorporados e acompanhados pelo IAT quanto à sua situação administrativa e operacional.
Enterprise Geodatabase Feature Class
dataset
EPSG
6.11(9.2.0)
0
Simple
FALSE
0
TRUE
FALSE
gisdb.iat.prodes
Feature Class
0
OBJECTID
OBJECTID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
shape
Geometry
8
0
0
Feature geometry.
Esri
Coordinates defining the features.
uuid
Código PRODES
String
255
0
0
main_class
Classe
String
255
0
0
image_date
Data da imagem
Date
8
0
0
year
Ano PRODES
Integer
4
10
0
area_ha
Área (ha)
Double
8
38
8
validacao
Validação
Integer
4
10
0
validacao_motivo
Motivo da validação
Integer
4
10
0
atendimento
Atenidmento
Integer
4
10
0
rev
Revisor
Integer
4
10
0
aia
AIA(s)
String
255
0
0
eprotocolo
E-protocolo
String
255
0
0
obs
Observação
String
255
0
0
regional
Escritório Regional
String
255
0
0
municipio
Município
String
255
0
0
uc
Unidade de Conservação
String
255
0
0
fitogeo
Região fitogeográfica
String
255
0
0
car
Imóvel CAR
String
255
0
0
area_imovel
Área do imóvel (ha)
Double
8
38
8
mod_fiscal
Módulos fiscais
Double
8
38
8
itsc
Intersecção imóvel (ha)
Double
8
38
8
porc_itsc
Intersecção imóvel (%)
Double
8
38
8
num_car
Quantidade de imóveis
Integer
4
10
0
publicacao
Data de publicação
Date
8
0
0
satellite
Satélite
String
255
0
0
sensor
sensor
String
255
0
0
indicador_numerico
Indicador de validez
Integer
4
10
0
Uso_principal_2008
Uso principal 2008
String
255
0
0
Veg_florestal_2008
Veg. florestal 2008
String
255
0
0
Veg_herb_arb_2008
Veg. herb. arb. 2008
String
255
0
0
Uso_principal_2018
Uso principal 2018
String
255
0
0
Veg_florestal_2018
Veg. florestal 2018
String
255
0
0
Veg_herb_arb_2018
Veg. herb. arb. 2018
String
255
0
0
VÁLIDO
1
INVÁLIDO
2
st_area(shape)
st_area(shape)
Double
0
0
0
st_perimeter(shape)
st_perimeter(shape)
Double
0
0
0
20260505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